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"scale": 0.1722256118200868, + "scale": 0.1279275687532539, "close": true -} +} \ No newline at end of file diff --git a/docs/about-me/projects/45-traditional-ai-case-studies.md b/docs/about-me/projects/45-traditional-ai-case-studies.md new file mode 100644 index 00000000000..4b4e514ff03 --- /dev/null +++ b/docs/about-me/projects/45-traditional-ai-case-studies.md @@ -0,0 +1,86 @@ +# Traditional AI Case study + +## Financial Technology (FinTech) Fraud Detection Case Study + +### Challenges + +A FinTech company was struggling with real-time fraud detection as the transaction volumes grew. Their current systems failed to maintain high accuracy, which posed a risk of financial losses and reduced customer trust. + +### Solution by Opstree + +Opstree partnered with the company to deliver a tailored AI-powered fraud detection solution. The solution leveraged **Amazon SageMaker** for model training and deployment, **Amazon Fraud Detector** for identifying suspicious activities, and proprietary models that combined supervised and unsupervised learning techniques, along with **scikit-learn** and **PyTorch** for custom fraud detection algorithms. + +Opstree further integrated **Amazon Textract** and **Amazon Rekognition** to strengthen document verification and identity validation processes, while adding advanced clustering techniques to uncover fraud networks. Continuous retraining pipelines were also implemented using **Amazon SageMaker**, ensuring the model stayed accurate over time. + +### Results + +Opstree's AI solution led to enhanced fraud detection with fewer false positives and negatives. The real-time detection capabilities and use of advanced services helped the company quickly identify fraudulent patterns and mitigate financial risks. + +- Reduced NPAs from 9% to 6%. +- Decreased monthly financial losses by ₹2 Cr. +- Faster fraud detection enabled quick action in high-risk areas. + +### Tools Used + +- **Amazon SageMaker** +- **Amazon Fraud Detector** +- **Amazon Textract** +- **Amazon Rekognition** +- Python (scikit-learn, PyTorch) + +--- + +## Financial Technology (FinTech) Credit Risk Analysis and Modeling Case Study + +### Challenges + +The FinTech company needed to expedite loan approvals while reducing Non-Performing Assets (NPAs). Traditional credit risk methods resulted in slower processing times and suboptimal loan terms, negatively impacting customer satisfaction. + +### Solution by Opstree + +Opstree developed an AI-based credit risk analysis solution using a combination of supervised models, including **logistic regression** with **PSI/CSI tracking** to ensure model accuracy. Using **scikit-learn**, Opstree trained custom credit risk models and deployed them using **Python (FastAPI)**, which enabled efficient real-time decision-making. + +Additionally, **Amazon SageMaker** was used to automate model retraining, while **PowerBI** provided detailed analytics and dashboards to track credit risk performance. Opstree also incorporated clustering algorithms to assess borrower risk profiles based on alternative data sources such as transactional patterns and social profiles. + +### Results + +The credit risk AI solution revolutionized the company's lending operations, improving loan approval speed and accuracy while reducing NPAs. The detailed analytics allowed the company to optimize lending terms and improve customer satisfaction. + +- Reduced NPAs from 9% to 6%. +- Reduced delinquency rates by 5%. +- Reduced reliance on external credit scores by 25% through in-house AI models. + +### Tools Used + +- **Python (FastAPI)** +- **scikit-learn** +- **Amazon SageMaker** +- **PowerBI** for analytics + +--- + +## IoT Predictive Maintenance for HVAC Systems Case Study + +### Challenges + +An HVAC company needed a proactive maintenance strategy to reduce equipment downtime and operational costs. Their traditional reactive approach led to frequent breakdowns and costly repairs. + +### Solution by Opstree + +Opstree implemented an AI-driven predictive maintenance solution based on **time-series modeling**. Using **ARIMA** models developed with **scikit-learn**, Opstree analyzed data from IoT sensors monitoring system performance. The solution predicted potential failures, allowing the company to address issues before they escalated. + +By leveraging real-time monitoring with integrated alerts, the maintenance team could take preventive actions, reducing equipment breakdowns and optimizing overall system performance. + +### Results + +With Opstree’s predictive maintenance solution, the company achieved significant reductions in operational costs and equipment failures, while optimizing resource allocation and improving customer satisfaction. + +- Reduced equipment downtime by 25%. +- Increased energy savings by 20%. +- Improved compliance rates by 30%. + +### Tools Used + +- **scikit-learn** (ARIMA) +- **Amazon SageMaker** +- **Time-series analysis** models diff --git a/docs/about-me/projects/46-podcast-streamlining-cloud-migration-through-data-engineering.md b/docs/about-me/projects/46-podcast-streamlining-cloud-migration-through-data-engineering.md index b30a484d024..d7d6dbd873b 100644 --- a/docs/about-me/projects/46-podcast-streamlining-cloud-migration-through-data-engineering.md +++ b/docs/about-me/projects/46-podcast-streamlining-cloud-migration-through-data-engineering.md @@ -7,7 +7,7 @@ From designing robust data pipelines to managing complex transformations and opt ### Podcast Details - Speaker - Deepak Sood, Sr. AI, Data & DevOps Architect, OpsTree Solutions -- Date - 15th October 2024 +- Date - Tuesday, 15th October 2024 - Time - 6:30 pm – 8:00 pm (IST) ### Workshop Agenda diff --git a/docs/about-me/projects/47-genai-case-study-careers360.md b/docs/about-me/projects/47-genai-case-study-careers360.md index a06286322bc..b1bb4440785 100644 --- a/docs/about-me/projects/47-genai-case-study-careers360.md +++ b/docs/about-me/projects/47-genai-case-study-careers360.md @@ -44,3 +44,17 @@ Opstree’s solution empowered Careers360’s content team to focus on higher-le ## Conclusion With Opstree’s GenAI-driven solution using Amazon Bedrock, Careers360 was able to transform its content operations, achieving greater efficiency and saving significant research time for its large team of content creators. + +## Why Choose OpsTree + +### 1. Expertise in Custom GenAI Solutions + +OpsTree specializes in designing and deploying tailored GenAI solutions, like Retrieval-Augmented Generation (RAG), that cater to your unique business needs. Leveraging platforms like **Amazon Bedrock**, we create models fine-tuned to your data and workflows, ensuring the AI-generated insights are relevant, actionable, and optimized for your specific use cases. + +### 2. Seamless Integration with AWS Ecosystem + +As AWS experts, OpsTree excels in integrating GenAI models with existing AWS services such as S3, Lambda, SageMaker, and more. This enables end-to-end automation, real-time updates, and seamless scalability, making your GenAI solution robust and future-proof while benefiting from AWS’s secure infrastructure. + +### 3. Proven Track Record with Scalable AI Solutions + +OpsTree has a proven history of successfully delivering AI-driven projects, including large-scale content research systems like the one implemented for Careers360. Our expertise in deploying **scalable, cost-optimized** AI solutions ensures that you receive a high-performance, enterprise-grade system that can handle evolving data demands efficiently. diff --git a/docs/about-me/projects/readme.md b/docs/about-me/projects/readme.md index 0679be5df7d..ccf737fd34f 100644 --- a/docs/about-me/projects/readme.md +++ b/docs/about-me/projects/readme.md @@ -35,6 +35,7 @@ - [MLOps Case Studies](about-me/projects/64-mlops-case-studies.md) - [Case Study: Anomaly Detection in Metric Data using Isolation Forest](about-me/projects/51-case-study-anomaly-detection.md) - [GenAI Case Study - Careers360](about-me/projects/47-genai-case-study-careers360.md) +- [Traditional AI Case Studies](about-me/projects/45-traditional-ai-case-studies.md) ### Bake.io diff --git a/docs/ai/big-data/tools.md b/docs/ai/big-data/tools.md index d00e42ac4fb..493b300ffe7 100644 --- a/docs/ai/big-data/tools.md +++ b/docs/ai/big-data/tools.md @@ -48,6 +48,14 @@ https://www.talend.com https://www.youtube.com/watch?v=bqa0kB59SUc +## Data on EKS + +![Data on EKS](../../media/Pasted%20image%2020241017195034.jpg) + +- [Hello from Data on EKS | Data on EKS](https://awslabs.github.io/data-on-eks/) +- [GitHub - awslabs/data-on-eks: DoEKS is a tool to build, deploy and scale Data & ML Platforms on Amazon EKS](https://github.com/awslabs/data-on-eks) +- [Introducing Data on EKS – Modernize Data Workloads on Amazon EKS | Containers](https://aws.amazon.com/blogs/containers/introducing-data-on-eks-modernize-data-workloads-on-amazon-eks/) + ## SAAS - [Atlan](https://atlan.com/) (Enterprise Data Catalogs for DataOps) diff --git a/docs/ai/data-science/datasets.md b/docs/ai/data-science/datasets.md index 70872b14c19..ddb79864108 100755 --- a/docs/ai/data-science/datasets.md +++ b/docs/ai/data-science/datasets.md @@ -68,3 +68,7 @@ H and DS use similar datasets, and DS is basically the next-gen version of H. Wh - [Croissant: a metadata format for ML-ready datasets](https://research.google/blog/croissant-a-metadata-format-for-ml-ready-datasets/) - [GitHub - mlcommons/croissant: Croissant is a high-level format for machine learning datasets that brings together four rich layers.](https://github.com/mlcommons/croissant) - Cat / Dog - https://bit.ly/ImgClsKeras + +## Links + +- [5 Free Datasets to Kickstart Your Machine Learning Projects Today - MachineLearningMastery.com](https://machinelearningmastery.com/5-free-datasets-to-kickstart-your-machine-learning-projects-today/) diff --git a/docs/ai/data-visualization/bi-tools.md b/docs/ai/data-visualization/bi-tools.md index bb6d2c12b52..273233fd611 100755 --- a/docs/ai/data-visualization/bi-tools.md +++ b/docs/ai/data-visualization/bi-tools.md @@ -78,6 +78,15 @@ Amazon QuickSight is built with "SPICE" -- a Super-fast, Parallel, In-memory Cal - [Amazon Q in QuickSight: Hands-On Demo for Generative BI and Real-Time Insights | Amazon Web Services - YouTube](https://www.youtube.com/watch?v=CFBlREfSItc) - [Amazon Q in QuickSight: 2024 Amazon QuickSight Learning Series - YouTube](https://www.youtube.com/watch?v=ioS4BZyxEK4) +### Topics + +[Getting started with Amazon QuickSight Q - Amazon QuickSight](https://docs.aws.amazon.com/quicksight/latest/user/quicksight-q-get-started.html) + +- Exclude unused fields +- Verify friendly field names +- Add synonyms to fields +- Review field configurations + ## DataIQ DataIQ is a business intelligence platform designed to help organizations manage, analyze, and derive insights from their data. It typically combines data governance, analytics, and data science capabilities to enable companies to become more data-driven. DataIQ allows users to access and analyze large datasets, create predictive models, and generate actionable insights, often with a focus on improving business outcomes. It can integrate with various data sources and tools to provide a unified view of data across an organization. The platform is used by data professionals, including data scientists, analysts, and business users, to streamline data operations and decision-making processes. diff --git a/docs/ai/deep-learning/roadmap.md b/docs/ai/deep-learning/roadmap.md index e423cabf35d..2850f018e6f 100644 --- a/docs/ai/deep-learning/roadmap.md +++ b/docs/ai/deep-learning/roadmap.md @@ -3,7 +3,7 @@ ![complete roadmap to prepare for deep learning](../../media/Screenshot%202024-09-20%20at%2011.18.50%20PM.jpg) - Foundational - Introduction to Neural Network, Loss Function, Optimizers - Gradient Descent, SGD, Adagrad, RMSProp, Adam - - Everyone is using Adam optimizer, since it is able to change the momentum i.e. the learning rate as your training is going on + - Everyone is using Adam optimizer, since it is able to change the momentum i.e. the learning rate as your training is going on - Activation function - ReLU, Sigmoid, Tanh - Geoffrey Hinton - inventor of backpropogation algorithm - Inputs, weights, bias @@ -28,8 +28,8 @@ - NLP - Sequence to sequence data - - Sentence - - Sales Forecasting + - Sentence + - Sales Forecasting - Neural language translation - HuggingFace, Ktrain diff --git a/docs/ai/libraries/deep-learning-frameworks.md b/docs/ai/libraries/deep-learning-frameworks.md index 8a02bcca846..f1191f77790 100644 --- a/docs/ai/libraries/deep-learning-frameworks.md +++ b/docs/ai/libraries/deep-learning-frameworks.md @@ -1,23 +1,5 @@ # Deep Learning Frameworks -## Apache MXNet - -A scalable deep learning framework. Extremely fast and efficient. Capable of scaling across multiple GPUs and multiple machines. - -Apache MXNet is an open-source deep learning software framework that trains and deploys deep neural networks. It aims to be scalable, allows fast model training, and supports a flexible programming model and multiple programming languages (including C++, Python, Java, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, and Wolfram Language). The MXNet library is portable and can scale to multiple GPUs and machines. It was co-developed by Carlos Guestrin at the University of Washington, along with GraphLab. - -As of September 2023, **it is no longer actively developed**. Apache MXNet was effectively abandoned due to a combination of factors including lack of significant contributions, outdated builds, and a shift in focus by its major backer, Amazon, towards other frameworks like PyTorch. The project saw no new releases for over a year, and there were very few pull requests or updates from contributors, leading to its move to the Apache Attic in 2023. Additionally, the community began migrating to other frameworks that offered more robust support and development activity. - -https://en.wikipedia.org/wiki/Apache_MXNet - -### MXNet Model Server - -Model Server for Apache MXNet is a tool for serving neural net models for inference - -Model Server for Apache MXNet (MMS) is a flexible and easy to use tool for serving deep learning models exported from [MXNet](http://mxnet.io/) or the Open Neural Network Exchange ([ONNX](http://onnx.ai/)). - -https://github.com/awslabs/mxnet-model-server - ## Pytorch PyTorch ( Tensors and Dynamic neural networks in Python with strong GPU acceleration) @@ -51,7 +33,6 @@ Open source machine learning library. Often used for neural networks, deep learn - **Keras** is not for beginners, its for rapid deployment and production. And meant to be used by the people who already understand the technology - **Pytorch** is great for research implementations, but it's very unnecessarily hard to deploy your model into production - **Tensorfow** is another great framework for deep learning. But is slow and memory hungry -- After using Pytorch/Keras/Tensorflow 2.0, I finally decided that MXNet would be my frameworks of choice for Deep Learning. ### 1. PyTorch @@ -152,6 +133,24 @@ If you are building a **deep learning computer vision model**, **PyTorch** or ** For traditional machine learning tasks (e.g., using support vector machines or decision trees), **scikit-learn** is a better fit. +## Apache MXNet + +A scalable deep learning framework. Extremely fast and efficient. Capable of scaling across multiple GPUs and multiple machines. + +Apache MXNet is an open-source deep learning software framework that trains and deploys deep neural networks. It aims to be scalable, allows fast model training, and supports a flexible programming model and multiple programming languages (including C++, Python, Java, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, and Wolfram Language). The MXNet library is portable and can scale to multiple GPUs and machines. It was co-developed by Carlos Guestrin at the University of Washington, along with GraphLab. + +As of September 2023, **it is no longer actively developed**. Apache MXNet was effectively abandoned due to a combination of factors including lack of significant contributions, outdated builds, and a shift in focus by its major backer, Amazon, towards other frameworks like PyTorch. The project saw no new releases for over a year, and there were very few pull requests or updates from contributors, leading to its move to the Apache Attic in 2023. Additionally, the community began migrating to other frameworks that offered more robust support and development activity. + +https://en.wikipedia.org/wiki/Apache_MXNet + +### MXNet Model Server + +Model Server for Apache MXNet is a tool for serving neural net models for inference + +Model Server for Apache MXNet (MMS) is a flexible and easy to use tool for serving deep learning models exported from [MXNet](http://mxnet.io/) or the Open Neural Network Exchange ([ONNX](http://onnx.ai/)). + +https://github.com/awslabs/mxnet-model-server + ## Links - https://www.kaggle.com/learn-forum/90594 diff --git a/docs/ai/libraries/mlops-model-deployment.md b/docs/ai/libraries/mlops-model-deployment.md index 8bb4a0918f9..b2d8d74a46a 100755 --- a/docs/ai/libraries/mlops-model-deployment.md +++ b/docs/ai/libraries/mlops-model-deployment.md @@ -156,3 +156,4 @@ https://www.seldon.io - [Let’s Architect! Learn About Machine Learning on AWS | AWS Architecture Blog](https://aws.amazon.com/blogs/architecture/lets-architect-learn-about-machine-learning-on-aws/) - [AWS re:Invent 2023 - Introduction to MLOps engineering on AWS (TNC215) - YouTube](https://www.youtube.com/watch?v=2kzJPhgDkDE) - [AWS re:Invent 2023 - Zero to machine learning: Jump-start your data-driven journey (SMB204) - YouTube](https://www.youtube.com/watch?v=-CSrOKo8Qgs) +- [Step-by-Step Guide to Deploying ML Models with Docker](https://www.kdnuggets.com/step-by-step-guide-to-deploying-ml-models-with-docker) diff --git a/docs/ai/libraries/tools.md b/docs/ai/libraries/tools.md index 2d48e7c5bd5..a20406df446 100755 --- a/docs/ai/libraries/tools.md +++ b/docs/ai/libraries/tools.md @@ -186,6 +186,14 @@ https://www.cortex.dev - https://explosion.ai/software - https://web.superquery.io - [Announcing New Tools for Building with Generative AI on AWS | AWS Machine Learning Blog](https://aws.amazon.com/blogs/machine-learning/announcing-new-tools-for-building-with-generative-ai-on-aws/) +- [7 Free Machine Learning Tools Every Beginner Should Master in 2024 - MachineLearningMastery.com](https://machinelearningmastery.com/7-free-machine-learning-tools-every-beginner-should-master-in-2024) + - Scikit-learn + - Great Expectations + - MLflow + - DVC (Data Version Control) + - SHAP (SHapley Additive exPlanations) + - FastAPI + - Docker ## SAAS Tools diff --git a/docs/ai/llm/llm-building.md b/docs/ai/llm/llm-building.md index 5e4ac1934cb..c07ab5455b3 100644 --- a/docs/ai/llm/llm-building.md +++ b/docs/ai/llm/llm-building.md @@ -199,3 +199,4 @@ An LLM Agent is a software entity capable of reasoning and autonomously executin - [Let's reproduce GPT-2 (124M) - YouTube](https://www.youtube.com/watch?v=l8pRSuU81PU) - [Scaling and Reliability Challenges of LLama3](https://mlops.substack.com/p/scaling-and-reliability-challenges) - [Building LLMs from the Ground Up: A 3-hour Coding Workshop - YouTube](https://www.youtube.com/watch?v=quh7z1q7-uc&ab_channel=SebastianRaschka) +- [How AWS engineers infrastructure to power generative AI](https://www.aboutamazon.com/news/aws/aws-infrastructure-generative-ai) diff --git a/docs/ai/llm/models.md b/docs/ai/llm/models.md index ad3f8a9aa73..e8f0ff8938a 100644 --- a/docs/ai/llm/models.md +++ b/docs/ai/llm/models.md @@ -109,6 +109,11 @@ Emotional prompting example - You are Dolphin, an uncensored and unbiased Al ass - [GitHub - tatsu-lab/alpaca\_eval: An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.](https://github.com/tatsu-lab/alpaca_eval) - [A Gentle Introduction to LLM Evaluations - Elena Samuylova - YouTube](https://www.youtube.com/live/ac6ZB5QEwGU) - [Eureka: OSS Framework to evaluate LLMs - by Bugra Akyildiz](https://mlops.substack.com/p/eureka-oss-framework-to-evaluate) +- [The Needle In a Haystack Test. Evaluating the performance of RAG… | by Aparna Dhinakaran | Towards Data Science](https://towardsdatascience.com/the-needle-in-a-haystack-test-a94974c1ad38) + - [GitHub - gkamradt/LLMTest\_NeedleInAHaystack: Doing simple retrieval from LLM models at various context lengths to measure accuracy](https://github.com/gkamradt/LLMTest_NeedleInAHaystack) + - [The Needle In a Haystack Test: Evaluating the Performance of LLM RAG Systems - Arize AI](https://arize.com/blog-course/the-needle-in-a-haystack-test-evaluating-the-performance-of-llm-rag-systems/) + - [Unlocking precision: The "Needle-in-a-Haystack" test for LLM evaluation](https://labelbox.com/guides/unlocking-precision-the-needle-in-a-haystack-test-for-llm-evaluation/) + - [The Needle in the Haystack Test and How Gemini Pro Solves It | Google Cloud Blog](https://cloud.google.com/blog/products/ai-machine-learning/the-needle-in-the-haystack-test-and-how-gemini-pro-solves-it) ### Tools diff --git a/docs/ai/llm/tools.md b/docs/ai/llm/tools.md index 50b0d57e42d..aa605388821 100644 --- a/docs/ai/llm/tools.md +++ b/docs/ai/llm/tools.md @@ -20,6 +20,7 @@ - [The Amazing AI Super Tutor for Students and Teachers | Sal Khan | TED - YouTube](https://www.youtube.com/watch?v=hJP5GqnTrNo&ab_channel=TED) - [WebChatGPT: ChatGPT with internet access | Chrome Web Store - Extensions](https://chrome.google.com/webstore/detail/webchatgpt-chatgpt-with-i/lpfemeioodjbpieminkklglpmhlngfcn/related) - [NotebookLM | Note Taking & Research Assistant Powered by AI](https://notebooklm.google/) + - [How to Create YouTube Video Study Guides with NotebookLM - KDnuggets](https://www.kdnuggets.com/how-to-create-youtube-video-study-guides-with-notebooklm) ## AI Generators diff --git a/docs/ai/ml-fundamentals/intro.md b/docs/ai/ml-fundamentals/intro.md index a6e11d02ed0..82f7f351705 100755 --- a/docs/ai/ml-fundamentals/intro.md +++ b/docs/ai/ml-fundamentals/intro.md @@ -124,3 +124,4 @@ https://betterexplained.com/articles/intuitive-convolution - https://www.toptal.com/machine-learning/interview-questions - [Mathematics of Machine Learning](https://www.youtube.com/watch?v=8onB7rPG4Pk) - [A friendly introduction to linear algebra for ML (ML Tech Talks) - YouTube](https://www.youtube.com/watch?v=LlKAna21fLE) +- [A Roadmap for Your Machine Learning Career - MachineLearningMastery.com](https://machinelearningmastery.com/a-roadmap-for-your-machine-learning-career/) diff --git a/docs/ai/nlp/word-embedding-to-transformers.md b/docs/ai/nlp/word-embedding-to-transformers.md index d57ffa13758..92a765af980 100644 --- a/docs/ai/nlp/word-embedding-to-transformers.md +++ b/docs/ai/nlp/word-embedding-to-transformers.md @@ -684,3 +684,8 @@ Mamba is a linear-time language model that outperforms Transformers on various t [Mamba: The Easy Way](https://jackcook.com/2024/02/23/mamba.html) [Mamba Explained](https://thegradient.pub/mamba-explained/) + +## Links + +- [State Space Sequence Models over Transformers?](https://mlops.substack.com/p/state-space-sequence-models-over) +- [Introduction - Hugging Face NLP Course](https://huggingface.co/learn/nlp-course/chapter1/1) diff --git a/docs/ai/others-resources-interview-learning-courses.md b/docs/ai/others-resources-interview-learning-courses.md index f96e71412a4..effa211c524 100755 --- a/docs/ai/others-resources-interview-learning-courses.md +++ b/docs/ai/others-resources-interview-learning-courses.md @@ -32,6 +32,7 @@ - [udlbook](https://udlbook.github.io/udlbook/) - [Learn Machine Learning in 2024](https://www.freecodecamp.org/news/learn-machine-learning-in-2024/) - [5 Free AI Courses With Certifications In 2024](https://www.forbes.com/sites/rachelwells/2024/08/08/5-free-ai-courses-with-certifications-in-2024/) +- [A Roadmap for Your Machine Learning Career - MachineLearningMastery.com](https://machinelearningmastery.com/a-roadmap-for-your-machine-learning-career/) ## DS (Data Science) + DA (Data Analytics) + DE (Data Engineering) diff --git a/docs/ai/readme.md b/docs/ai/readme.md index 7fcd41ee91c..6ce8bca9fd4 100755 --- a/docs/ai/readme.md +++ b/docs/ai/readme.md @@ -21,6 +21,10 @@ - [Solutions](ai/solutions.md) - [Social Media Analytics Solution](ai/social-media-analytics-solution.md) +## Data & AI Landscape + +![Data & AI Landscape](media/Data-and-AI-Landscape-2021-v3-small.jpg) + ## AGI (Artificial General Intelligence) / Sentient AGI will be intellectually, morally, ethically and creatively superior to humans in every conceivable way diff --git a/docs/ai/scikit-learn/intro.md b/docs/ai/scikit-learn/intro.md index b1d063ffff1..603477263da 100755 --- a/docs/ai/scikit-learn/intro.md +++ b/docs/ai/scikit-learn/intro.md @@ -9,8 +9,7 @@ ## References -http://scikit-learn.org/stable/tutorial/machine_learning_map - -http://scikit-learn.org/stable/modules/classes.html - -[Machine Learning with Python and Scikit-Learn - Full Course - YouTube](https://www.youtube.com/watch?v=hDKCxebp88A) +- http://scikit-learn.org/stable/tutorial/machine_learning_map +- http://scikit-learn.org/stable/modules/classes.html +- [Machine Learning with Python and Scikit-Learn - Full Course - YouTube](https://www.youtube.com/watch?v=hDKCxebp88A) +- [Integrating LLMs with Scikit-Learn Using Scikit-LLM - KDnuggets](https://www.kdnuggets.com/integrating-llms-with-scikit-learn-using-scikit-llm) diff --git a/docs/book-summaries/alchemy-the-surprising-power-of-ideas-that-dont-make-sense.md b/docs/book-summaries/alchemy-the-surprising-power-of-ideas-that-dont-make-sense.md new file mode 100644 index 00000000000..4fb2c395f09 --- /dev/null +++ b/docs/book-summaries/alchemy-the-surprising-power-of-ideas-that-dont-make-sense.md @@ -0,0 +1,101 @@ +# Alchemy: The Surprising Power of Ideas That Don't Make Sense + +Book by Rory Sutherland + +> “To be brilliant, you have to be irrational.”- Rory Sutherland + +> “A flower is simply a weed with an advertising budget.” — Rory Sutherland + +The book covers several different ideas, but ultimately it all comes down to the value of “psycho-logic”: + +> “My word to describe the way we make decisions — to distinguish it from the artificial concepts of ‘logic’ and ‘rationality… I have chosen psycho-logic as a neutral and non-judgemental term. I have done this for a reason. When we do put a name to non-rational behaviour, it is usually a word like ‘emotion’, which makes it sound like logic’s evil twin.”- Rory Sutherland + +## 1. Competence before confidence + +==“Insecurity leads to over-thinking and paralyses the ability to take action… If you want to get started, believe that you can do it or be good at something — but don’t expect yourself to be the best.”== + +## 2. The delusion of control + +“The world is much more random than we like to think… Don’t obsess over outcomes, and don’t try to project too far into the future. Think through decisions and choices as they come to you — but don’t agonise over them.” + +## 3. Don’t plan too much + +“Complex plans are designed to compensate for small vision or low self-esteem… The world is chaotic and complex — don’t try to cram the infinite detail of the real world into one neat grid.” + +## 4. Design your future + +“Design is about having a go — trying something and seeing what happens. Planning is how to try — you plan for things to happen as they should, rather than designing to turn them into something else.” + +## 5. Understand that branding is everything + +“Brands are just products with no price tag… People don’t buy images or messages; they buy better versions of themselves.” + +## 6. Think like a designer + +“If you can design it, you can make it happen… If you know the price of something, you control the supply; if you control supply, you can charge whatever prices suit your market. You will always have sufficient demand because there are always people who will do anything to get what you’re selling, whether or not they can afford it.” + +## 7. Bet on the jockey + +“Talented managers lead successful brands… The company is its own product, owned and managed by the right person with the right culture.” + +> “If there were a logical answer, we would have found it by now.” — Rory Sutherland + +## 8. Make it personal + +“Concentrate on what you can deliver to each person, not the group. By selling one at a time, you can deliver more than by selling ten at a time… Every relationship is an individual one; every customer has different wants and needs; never forget that they all use your product or service in different ways.” + +## 9. The luxury of care + +“Every marketer can sell a product, but to create an emotional bond, you need to be able to give something away… It is much harder and more effective to make someone feel they deserve something than that they simply want it.” + +## 10. Aim for your pains, not for your pleasures + +“The more pleasure you promise, the less effective your advertising is likely to be. Aim for people’s fears or their pain rather than their desires or pleasures… The best marketing only has to work once — it just sucks people in by tugging at something they already feel but don’t yet understand. Once trust is established, people will buy almost anything.” + +## 11. Good is the enemy of great + +“It is often easier to make something very, very cheap than it is to make what was expensive (but good) much cheaper and still be profitable… We think we want quality — but actually, we just want value: we don’t want a fair price, we want a great deal.” + +> “Don’t just copy what works, Copy how it works.” — Rory Sutherland + +## Marcellus Summary + +As the title says, the talk is all about how we optimise for speed, often irrationally at that i.e, there are somethings which are counter-intuitively most productive when not done in the shortest possible time. He gives some brilliant examples to back this. He talks about the paceometer which sits alongside the speedometer in the car, except it shows “_how many minutes at that speed it will take you to go 10 miles. Assuming you’re going 10 miles at 10 miles an hour, it’ll take you an hour._ + +_If you’re going 10 miles, or 20 miles, or 30 miles, something in that order of magnitude, there’s a big time-saving by going at 30 miles an hour rather than 20 miles an hour. In fact, you’ll save a whole 10 minutes just by accelerating about 10 miles an hour._ + +_On the other hand, if you accelerate from 80 miles an hour to 90 for example, or 70 to 80, you basically save a minute._ + +_Some of you may have noticed this if you’ve got a GPS in your car. You’re driving on the motorway at 60, you realize you’re going to be five minutes late for an appointment, so you welly it. And after driving at an insanely fast and dangerous speed for about eight minutes, you suddenly realize your arrival time has only improved by one minute._ + +_This is fascinating. Because to a physicist, they’re exactly the same. But when I present the information about time and distance in a different way, your reaction is now completely different. What it effectively says is: going quite a bit faster when you’re going slowly is a really big gain. Going very fast when you’re already going fast is the action of a dickhead._ + +_Basically, once you hit a comfortable 65 or 70 on the road, don’t bother. That’s enough. It’s a waste of time because the risk you encounter—the risk you incur on yourself, the risk you effectively impose on other people by going any faster—is utterly pointless in terms of time saved.”_ + +Another example he gives is of how people actually enjoy their train commute home, contrary to the assumption of the train authorities: + +“_Quite a lot of people enjoy their commute time. And there’s good behavioral evidence for this because economists have noticed that people live a bit further from work than they optimally should in order to create a chronological buffer between where they work and where they live. We like that decompression time._ + +_So this person announces the research to the people responsible for transport modeling at Transport for London, and they say, “You must never tell anybody that. It’s absolutely wrong for you to say that people might actually enjoy a train ride.”_ + +_“Eh? But it’s true.”_ + +_“Maybe it’s true, but all our models that justify transport investment assume that travel time is always a disutility. In other words, the more time you spend in transit, the worse off you are. If you come along with fancy ideas suggesting that people may sometimes prefer slower to faster, it fucks up our whole model.”_ + +**_So this is what’s happened to the world: optimization trumps human preference. The people who want to win the argument are effectively prepared to ignore human truths to preserve the integrity of the artificial model._** + +_The Unaccountability Machine by Dan Davies is a fantastic book, which argues that people create these models because if you can reduce decision-making to an algorithm, or a formula, or a process, or a procedure, you avoid the risk of blame. Computer says no, effectively._ + +_Instinctively, people love to codify things, and make them numerical, and turn them into optimization problems with a single right answer. Because the second you acknowledge ambiguity, you now have to exercise choice. If you can pretend there’s no ambiguity, then you haven’t made a decision, you can’t be blamed, you can’t be held responsible. And what’s the first thing you remove if you want to remove ambiguity from a model? You remove human psychology, because human psychology, particularly around time, is massively ambiguous.”_ + +The whole talk is full of blinding insights about human behaviour, contrary to popular perception. + +In conclusion, he says: _“I think there are things in life that you want to telescope and compress and accelerate and streamline and make more efficient. And there are things where the value is precisely in the inefficiency, in the time spent, in the pain endured, in the effort you have to invest. And I don’t think we’re going to differentiate between those things. .._ + +_The general assumption driven by these optimization models is always that faster is better. I think there are things we need to deliberately and consciously slow down for our own sanity and for our own productivity. If we don’t ask that question about what those things are, I think we’ll get things terribly, terribly wrong.”_ + +[Rory Sutherland – Are We Now Too Impatient to Be Intelligent? | Nudgestock 2024 - YouTube](https://www.youtube.com/watch?v=Bc9jFbxrkMk&ab_channel=Nudgestock) + +## Links + +- [11 rules I learned from Rory Sutherland book Alchemy | by Courtney Simms | New Writers Welcome | Medium](https://medium.com/new-writers-welcome/11-rules-i-learned-from-rory-sutherland-book-alchemy-5f6970a80259) diff --git a/docs/book-summaries/readme.md b/docs/book-summaries/readme.md index f9ca3919c27..e2622b91780 100755 --- a/docs/book-summaries/readme.md +++ b/docs/book-summaries/readme.md @@ -85,6 +85,7 @@ - [Find Your Element](book-summaries/find-your-element.md) - [Power and Prediction](book-summaries/power-and-prediction.md) - [7 Powers: The Foundations of Business Strategy](book-summaries/7-powers-the-foundations-of-business-strategy.md) +- [Alchemy: The Surprising Power of Ideas That Don't Make Sense](book-summaries/alchemy-the-surprising-power-of-ideas-that-dont-make-sense.md) - [Others](book-summaries/others.md) - [Books / Blinkist - Productivity](book-summaries/books-blinkist-productivity.md) - [Books / Blinkist - Management](../management/books-blinkist-management) diff --git a/docs/cloud/aws/networking-content-delivery/amazon-cloudfront.md b/docs/cloud/aws/networking-content-delivery/amazon-cloudfront.md index fc55cdd97f0..64e25c78f02 100644 --- a/docs/cloud/aws/networking-content-delivery/amazon-cloudfront.md +++ b/docs/cloud/aws/networking-content-delivery/amazon-cloudfront.md @@ -27,3 +27,9 @@ https://www.mitrai.com/tech-guide/eight-types-of-aws-storage-services-explained ### Pricing / Cost Optimization [Cost-Optimizing your AWS architectures by utilizing Amazon CloudFront features | Networking & Content Delivery](https://aws.amazon.com/blogs/networking-and-content-delivery/cost-optimizing-your-aws-architectures-by-utilizing-amazon-cloudfront-features/) + +## Links + +- [cdn-content-delivery-network](cloud/others/cdn-content-delivery-network.md) +- [cdn-comparision](cloud/others/cdn-comparision.md) +- [What you need to know when invalidating files - Amazon CloudFront](https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/invalidation-specifying-objects.html) diff --git a/docs/cloud/others/cdn-cache-invalidation.md b/docs/cloud/others/cdn-cache-invalidation.md new file mode 100644 index 00000000000..6003e783127 --- /dev/null +++ b/docs/cloud/others/cdn-cache-invalidation.md @@ -0,0 +1,87 @@ +# Cache Invalidation + +## Cache Invalidation vs Object Versioning + +### Understanding Versioning + +Versioning refers to the management of changes to documents, programs, and other information stored as computer files. In the context of CloudFront, versioning is crucial for ensuring that users receive the most up-to-date content without experiencing delays or errors caused by cached outdated files. + +One effective approach to versioning is appending a query string or file version number to your assets: + +```javascript +// Example of versioning with query strings +const imageUrl = 'https://domain.com/image.png?v=1.0'; + +// Versioning by renaming files +const imageUrl = 'https://domain.com/image-1.0.png'; +``` + +This method guarantees that when you update content, users will request the latest version of the file, avoiding potential caching issues. + +### Cache Invalidation Strategies + +Cache invalidation is a process whereby entries in a cache are replaced or removed. In CloudFront, this is typically done when you need to force the CDN to fetch the latest content from your origin server. + +To invalidate cached content, you can use the AWS Management Console or AWS CLI. Here is an example using the AWS CLI: + +```sh +// Invalidate a single file +aws cloudfront create-invalidation --distribution-id YOUR_DISTRIBUTION_ID --paths /path/to/your/file.ext + +// Invalidate multiple files +aws cloudfront create-invalidation --distribution-id YOUR_DISTRIBUTION_ID --paths /path/to/your/file.ext /path/to/another/file.ext + +// Invalidate all files +aws cloudfront create-invalidation --distribution-id YOUR_DISTRIBUTION_ID --paths '/*' +``` + +While invalidation is a powerful tool, it should be used judiciously since it can increase load on your origin and may incur additional costs if done frequently. As a best practice, use versioning methods to minimize the need for cache invalidation. + +## Strategies + +### 1. Use Cache-Control Headers with Appropriate Settings + +- **Short Cache Duration for HTML Pages:** Since news websites are updated frequently, set shorter cache durations for HTML pages (e.g., `max-age=300` seconds or 5 minutes). This will allow the CDN to serve the latest content without needing frequent manual invalidations. +- **Long Cache Duration for Static Assets:** For assets like images, CSS, and JavaScript files, use a long cache duration (e.g., `max-age=31536000` or 1 year) with a versioning scheme for file names. This approach reduces the need for invalidation since versioning handles the updates. + +### 2. Leverage URL Versioning for Static Assets + +- Use version identifiers (like timestamps, hash values, or sequential numbers) in the file names of static assets. For instance, `/styles/main_v1.css` or `/scripts/app_v123.js`. When an update occurs, update the file name to something like `/styles/main_v2.css`. +- Update the references in the HTML to use the new versioned URLs. This ensures that the CDN serves the latest version of the asset immediately. + +### 3. Automate Cache Invalidation for Updated Content + +- When publishing new articles or updating existing ones, automatically trigger a cache invalidation for the updated content. For example, invalidate the specific article page or a section (e.g., `/news/latest/`). +- Many CDNs offer APIs that can be integrated with your content management system (CMS) to automate the invalidation process upon publishing. + +### 4. Stale-While-Revalidate Strategy + +- Use the `stale-while-revalidate` directive in your `Cache-Control` header to allow the CDN to serve stale content while fetching an updated version in the background. This approach ensures low latency for users while still updating content frequently. +- Example: `Cache-Control: max-age=300, stale-while-revalidate=60`. This setting allows the page to be served from cache for up to 5 minutes while fetching a new version in the background if needed. + +### 5. Dynamic Cache Busting for Critical Updates + +- For critical updates (e.g., breaking news), invalidate the cache for the homepage and the specific news article to ensure the latest content is displayed immediately. +- Use a backend-based approach to detect high-priority updates and trigger an immediate cache refresh for the most relevant pages. + +### 6. Implement Edge Side Includes (ESI) for Personalization + +- If your website supports personalization (like user-specific content or ads), use ESI to dynamically include these components. The core page can still be cached, but personalized sections are fetched from the origin. +- This helps maintain a high cache hit ratio for the majority of the content while allowing personalized content to remain dynamic. + +### 7. Consider Using Service Workers for Offline and Pre-caching + +- Utilize service workers in the browser to cache specific assets and pages locally. This enables faster access to recently viewed articles and provides a fallback for users with poor connectivity. +- Service workers can also be used to pre-fetch and cache popular or related content, ensuring a seamless user experience. + +### 8. Use a Cache-First Strategy for Evergreen Content + +- For articles that are unlikely to change (evergreen content), cache them with longer durations and only invalidate when actual updates occur. This minimizes unnecessary cache purges. + +## Links + +- [Best Practices for Versioning and Cache Invalidation in CloudFront | Reintech media](https://reintech.io/blog/best-practices-versioning-cache-invalidation-cloudfront) +- [Use file versioning to update or remove content with a CloudFront distribution - Amazon CloudFront](https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/UpdatingExistingObjects.html) +- [Content delivery best practices  |  Cloud CDN  |  Google Cloud](https://cloud.google.com/cdn/docs/best-practices) +- [How does versioning work on Amazon Cloudfront? - Stack Overflow](https://stackoverflow.com/questions/57643059/how-does-versioning-work-on-amazon-cloudfront) +- [Object Versioning Instead of cache invalidation - Acowebs](https://acowebs.com/guideline/plugin-docs-faqs/wordpress-offload-media/object-versioning-instead-of-cache-invalidation/) diff --git a/docs/cloud/others/others-saas.md b/docs/cloud/others/others-saas.md index e0b53c94044..c2416d6cecc 100755 --- a/docs/cloud/others/others-saas.md +++ b/docs/cloud/others/others-saas.md @@ -50,6 +50,18 @@ https://blueyonder.com - https://github.com/partkeepr/PartKeepr - https://github.com/allegro/ralph +## Warehouse Management Solution (WMS) + +A warehouse management system is software that helps companies manage and control daily warehouse operations, from the moment goods and materials enter a distribution or fulfilment centre until the moment they leave. + +1. Improved operational efficiency +2. Reduced waste and costs +3. Real-time inventory visibility +4. Improved labour management +5. Better customer and supplier relationships + +[Page Not Found | SAP](https://www.sap.com/india/products/scm/extended-warehouse-management/what-is-a-wms.htm) + ## Human Resource Management System (HRMS) / HRIS / Hiring Tools - OrangeHRM - https://sourceforge.net/projects/orangehrm diff --git a/docs/cloud/others/readme.md b/docs/cloud/others/readme.md index bc2faf63ebe..e021348ed5d 100755 --- a/docs/cloud/others/readme.md +++ b/docs/cloud/others/readme.md @@ -20,9 +20,10 @@ - [VMware](cloud/others/vmware.md) - [SAP](cloud/others/sap.md) - [Content Delivery Network (CDN)](cloud/others/cdn-content-delivery-network.md) - - [CDN Comparision](cloud/others/cdn-comparision.md) - - [Cloudflare](cloud/others/cloudflare.md) - - [Amazon Cloudfront](cloud/aws/networking-content-delivery/amazon-cloudfront.md) + - [CDN Cache Invalidation](cloud/others/cdn-cache-invalidation.md) + - [CDN Comparision](cloud/others/cdn-comparision.md) + - [Cloudflare](cloud/others/cloudflare.md) + - [Amazon Cloudfront](cloud/aws/networking-content-delivery/amazon-cloudfront.md) - [Imagekit](cloud/others/imagekit.md) - [Ticketing / TMS](ticketing-tms) - [Loan Management System (LMS)](cloud/others/loan-management-system-lms.md) diff --git a/docs/computer-science/interview-question/readme.md b/docs/computer-science/interview-question/readme.md index 8c220ee5043..d4fd02c02c9 100755 --- a/docs/computer-science/interview-question/readme.md +++ b/docs/computer-science/interview-question/readme.md @@ -13,6 +13,7 @@ - [System Design - YouTube](computer-science/interview-question/system-design-youtube.md) - [System Design - MMOG - Game](system-design-mmog-game) - [Google Crawlers / Crawling](frontend/frontend-intro/google-crawlers-crawling.md) +- [System Design of Uber Data Architecture](computer-science/interview-question/system-design-uber-data-architecture.md) - [System Design - Others](system-design-others) - [Toptal](toptal) - [Blogs / Conferences / Blogging / Presentation / Tech Thursdays](blogs-conferences-blogging-presentation-tech-thursdays) diff --git a/docs/computer-science/interview-question/system-design-uber-data-architecture.md b/docs/computer-science/interview-question/system-design-uber-data-architecture.md new file mode 100644 index 00000000000..9794096626a --- /dev/null +++ b/docs/computer-science/interview-question/system-design-uber-data-architecture.md @@ -0,0 +1,258 @@ +# System Design of Uber Data Architecture + +Uber's real-time data infrastructure is a cornerstone of its business operations, processing massive amounts of data every day.  + +From drivers and riders to restaurants and back-end systems, Uber collects petabytes of data to power important features such as customer incentives, fraud detection, and predictions made by machine learning models. + +To manage this vast flow of information, Uber relies on a sophisticated system that handles three key components:  + +- Messaging platforms  +- Stream processing +- OLAP (OnLine Analytical Processing) + +Each element plays a crucial role in ensuring that data is processed and analyzed quickly, allowing Uber to respond to real-time events like ride requests, price changes, and more. + +![Data Flow at Uber Infrastructure](../../media/Screenshot%202024-10-18%20at%206.51.37%20PM.jpg) + +However, maintaining this real-time infrastructure isn't easy.  + +As Uber continues to grow, so do the challenges. The company needs to scale its systems to handle more data, support new use cases, and accommodate a growing number of users. At the same time, they must ensure that the data is always fresh, latency is kept low, and the entire system remains highly available. + +In short, Uber's real-time data system is essential to its ability to make split-second decisions, and the company has built an advanced architecture to keep everything running smoothly, even as demand grows. + +## Critical Requirements of Uber’s Real-Time Data + +Uber's real-time data system has some critical requirements to ensure it operates smoothly and efficiently, especially given its massive scale.  + +These requirements handle everything from ride pricing to food delivery, ensuring users get the best possible experience. + +- **Consistency:** It is crucial for Uber's mission-critical services. The data needs to be consistent across all regions, with no room for loss or duplication. This is especially important for services that rely on accurate data, like financial dashboards. +- **Availability:** Uber’s system needs to be up and running almost all the time, with a 99.99% uptime guarantee. Services like dynamic pricing, which adjust ride prices in real time based on supply and demand, simply can't afford downtime. +- **Data Freshness:** Events, like ride requests or order placements, need to be processed within seconds so the system can make real-time decisions, whether it's matching riders with drivers or alerting restaurants of new orders. Some tools, such as the UberEats Restaurant Manager, depend on query latency being super low—ideally, under a second. This allows restaurants to get instant insights into things like orders, sales, and performance metrics. +- **Scalability:** As Uber grows, its scalability becomes even more important. The data system must be able to expand as more data is generated and new use cases arise, ensuring smooth performance no matter how much demand increases. +- **Cost Efficiency:** Finally, cost efficiency plays a big role. Since Uber operates on low margins, the company focuses on managing resources efficiently, including memory and storage. This helps keep operational costs down while maintaining top performance. + +In short, Uber's real-time data infrastructure is designed to be consistent, available, fast, scalable, and cost-effective, ensuring the company's services run smoothly at all times. + +## Key Technologies Used By Uber + +Uber's real-time data infrastructure is powered by a combination of advanced open-source technologies, each customized to handle the company’s massive data needs.  + +The diagram below shows the overall landscape. + +![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3e74a5c9-a041-4657-a3e4-39017b238e76_1600x1017.png) + +Let’s take a closer look at the key technologies Uber relies on, how they work, and the unique tweaks that make them fit Uber's requirements. + +### Apache Kafka for Streaming Data + +Kafka is the backbone of Uber’s data streaming.  + +It handles trillions of messages and petabytes of data daily, helping to transport information from user apps (like driver and rider apps) and microservices. Kafka’s key role is to move this streaming data to batch and real-time systems. + +![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35bab385-a2ed-4c4f-958d-66e20e5d269b_1600x813.png) + +At Uber, Kafka was heavily customized to meet its large-scale needs. Some of the key features are as follows: + +- **Cluster Federation:** Uber created a federated Kafka cluster setup to improve scalability and reliability. With federation, data is distributed across smaller clusters (each with about 150 nodes), making it easier to manage and scale. +- **Dead Letter Queues (DLQ):** When messages fail (due to corruption or unexpected behavior), Uber's Kafka pushes them to a DLQ so they don’t block live traffic. This keeps data moving smoothly. +- **Consumer Proxy:** Kafka's client libraries were complex, and with so many programming languages in use at Uber, it became difficult to manage. Uber built a proxy layer to simplify client interactions. This layer consumes Kafka messages and forwards them to a user’s service, streamlining error handling and reducing client complexity. +- **Cross-cluster Replication:** For fault tolerance and redundancy, Uber developed uReplicator, which replicates Kafka messages across data centers. This ensures that data is available globally, even in case of failures. + +### Apache Flink for Stream Processing + +Apache Flink is another critical component of Uber’s infrastructure, used for processing data streams in real-time.  + +Flink can handle complex workloads, scale efficiently, and manage backlogs of millions of messages without slowing down. + +Here’s how Uber improved Flink for their environment: + +- **FlinkSQL:** Uber built a SQL layer on top of Flink, known as FlinkSQL, to make stream processing more accessible to users. It translates SQL queries into Flink jobs, allowing engineers and non-engineers alike to build real-time applications without the need to know the underlying code. +- **Resource Estimation and Auto-scaling:** Flink jobs can vary in resource requirements. Uber built tools to estimate how much CPU and memory a job needs based on its type and adjust resources automatically as workloads change throughout the day. +- **Failure Recovery:** Flink jobs are continuously monitored, and if a job fails, an automated system restarts it or scales it up as needed. + +By implementing these changes, Uber has made Flink more reliable and easier to use at scale, allowing thousands of real-time data processing jobs to run efficiently. + +See the diagram below that shows the Unified Flink Architecture at Uber. + +![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa9e8a845-940c-468d-a19c-f39f1a8cc4b4_1600x1017.png) + +### Apache Pinot for Real-Time OLAP + +For quick, low-latency data analysis, Uber uses Apache Pinot.  + +Pinot allows real-time analytics on terabytes of data, making it ideal for Uber’s dashboards and operational insights, like tracking ride demand or Uber Eats orders in real-time. + +Uber enhanced Pinot in several ways: + +- **Upsert Support:** Uber added the ability to handle upserts (updates + inserts), which is crucial for scenarios where data changes frequently, like correcting a ride fare or updating a delivery status. This feature ensures the latest data is always available for analysis. +- **Full SQL Support with Presto:** Although Pinot is great for real-time queries, it lacks certain advanced SQL features like joins. Uber integrated Pinot with Presto, a distributed SQL query engine, to bridge the gap. This combination allows users to run complex queries on Pinot’s real-time data with low latency. +- **Peer-to-peer Segment Recovery:** Originally, Pinot relied on external storage systems (like HDFS or S3) for backing up data segments, which created bottlenecks. Uber developed a peer-to-peer segment recovery system, allowing data replicas to serve as backups for each other. This improved both speed and reliability. + +### HDFS for Long-Term Storage + +Uber uses HDFS (Hadoop Distributed File System) as the foundation for its long-term data storage. HDFS stores data from Kafka streams and converts it into more efficient formats, like Parquet, for long-term use. + +The data stored in HDFS is used for: + +- **Backfilling:** When systems need to reprocess historical data (for example, if a bug is fixed), HDFS provides the source for this backfill. The stored data is also used to train new machine-learning models or test new data pipelines. +- **Checkpointing:** For services like Flink and Pinot, HDFS is used to store checkpoints—snapshots of the system’s state at a particular point in time. This allows systems to recover quickly in case of failure. + +### Presto for Interactive Queries + +Presto is Uber’s go-to query engine for exploring large datasets in real-time.  + +It’s designed to provide fast, distributed SQL queries across multiple data sources, including Pinot, Hive, and others. + +At Uber, Presto plays a critical role in: + +- **Real-time Data Exploration:** Presto allows engineers and data scientists to query real-time data in Pinot and other systems, providing insights on the fly. For example, an Uber Eats operations team might query real-time order data to monitor restaurant performance or customer trends. +- **Optimization with Pinot:** Uber has customized Presto to push as much query processing as possible to Pinot, reducing query latency and improving efficiency. + +## Use Cases + +Uber's real-time data infrastructure powers a variety of critical use cases, from surge pricing to real-time analytics for Uber Eats.  + +Let’s look at some of the most important use cases supported by Uber’s data infrastructure. + +### 1 - Surge Pricing + +One of Uber’s most well-known features is surge pricing, which adjusts fares based on real-time demand and supply conditions in a given area.  + +To make this happen, Uber uses a data pipeline powered by Kafka and Flink. Kafka ingests streaming data from ride requests and driver availability, while Flink processes this information in real-time to calculate pricing multipliers for each area. + +The surge pricing pipeline prioritizes data freshness and availability over strict consistency, meaning the system focuses on making quick pricing decisions rather than ensuring every single message is perfectly consistent across all regions.  + +This approach allows Uber to respond to changes in demand within seconds, keeping the marketplace balanced while maximizing driver availability. + +### 2 - UberEats Restaurant Manager + +For restaurant partners, Uber provides a dashboard called the UberEats Restaurant Manager, which offers real-time insights into order trends, sales, and service performance.  + +This dashboard is powered by Apache Pinot, a real-time OLAP system designed to handle large datasets with low-latency queries. + +Pinot enables fast querying by using pre-aggregated data, meaning that instead of running a complex query every time, certain metrics (like popular menu items or sales figures) are pre-calculated and stored, allowing for quick responses when the restaurant manager requests information.  + +This setup ensures that restaurant owners get real-time feedback, helping them make informed decisions about their business. + +### 3 - Real-Time Prediction Monitoring + +To ensure the quality of its machine-learning models, Uber has a system in place for real-time prediction monitoring. This system uses Flink to aggregate predictions from models and compare them against actual outcomes in real-time. + +With thousands of machine learning models deployed across Uber’s services, monitoring their performance is critical.  + +The system processes millions of data points per second to detect any deviations or inaccuracies in model predictions. By continuously aggregating and analyzing this data, Uber ensures that its models are performing as expected and can quickly identify any issues that need attention. + +### 4 - Ops Automation + +During the pandemic, Uber needed a way to respond quickly to operational needs, such as limiting the number of customers at restaurants to comply with health regulations. The Ops Automation system for Uber Eats was designed to provide this flexibility. + +This system uses Presto and Pinot to allow for ad hoc exploration of real-time data, enabling Uber’s operations team to run custom queries on current data about restaurant orders, couriers, and customer activity. For example, if a restaurant was nearing its capacity limit, the system could automatically trigger alerts or actions, such as temporarily pausing orders or notifying couriers to wait outside. + +This combination of real-time data and flexible querying made it possible for Uber Eats to adapt to fast-changing regulations, helping restaurants stay open while keeping customers and couriers safe. + +## Scaling Strategies + +To handle the massive scale of its operations, Uber has developed several key strategies for ensuring its data systems are highly available, reliable, and able to handle real-time and historical data processing.  + +Three of the most important strategies include the Active-Active Kafka setup, Active-Passive Kafka setup, and a backfill support solution for historical data processing. Let’s look at each in more detail: + +### 1 - Active-Active Kafka Setup + +For critical services like surge pricing, ensuring high availability and redundancy is a top priority at Uber. To meet this need, Uber uses an Active-Active Kafka setup across multiple regions. This means that Kafka clusters are deployed in different geographic regions, allowing data to be processed and synchronized across these regions in real-time. + +The Active-Active setup ensures that if one region experiences a failure—whether due to a network issue or a server problem—Uber’s systems can continue to function smoothly from another region.  + +For example, surge pricing calculations, which depend on real-time supply and demand data, are too important to fail. If the primary region fails, another region can immediately take over and continue processing the data without missing a beat. + +See the diagram below: + +![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F00f0c703-4ef5-4a6e-bc5e-82c3a6c86db6_1600x1141.png) + +This setup requires careful synchronization of data between regions. Uber uses uReplicator, a tool they developed to replicate Kafka messages across clusters, ensuring the system remains redundant and reliable. Even if one region goes down, the data is preserved and can be quickly restored in the backup region, minimizing disruption to the service. + +### 2 - Active-Passive Kafka Setup + +In addition to Uber's Active-Active Kafka setup, the company also employs an Active-Passive Kafka setup for certain services where strong data consistency is critical. While the Active-Active approach prioritizes availability and redundancy, the Active-Passive strategy is designed for use cases that cannot tolerate any data loss and require more stringent consistency guarantees, such as payment processing or auditing. + +In an Active-Passive Kafka setup, only one consumer (in a specific region, called the primary region) is allowed to process messages from Kafka at any given time.  + +If the primary region fails, the system fails over to a backup (passive) region, which then resumes processing from the same point where the primary left off. This ensures that no data is lost during the failover, preserving message order and maintaining data integrity. + +See the diagram below that shows the Active-Passive setup. + +![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8bd81bc0-b086-4fa9-bde0-b16c1fe32634_1600x961.png) + +The key challenge in Active-Passive setups is offset synchronization—ensuring that the consumer in the backup region starts processing from the same point as the primary region.  + +To achieve this, Uber developed a sophisticated offset management service. This service synchronizes offsets between the active and passive regions, using the uReplicator to replicate Kafka messages across clusters and periodically checkpoint the offset mappings between the regions. + +### 3 - Backfill Support with Kappa+ Architecture + +While real-time data processing is critical, there are times when Uber needs to reprocess historical data—whether for testing new data pipelines, training machine learning models, or fixing issues after a bug has been discovered. This is where Uber’s backfill support comes in, allowing them to reprocess data streams without causing significant delays or overhead. + +Uber uses a solution called Kappa+ architecture, which builds on the idea of processing real-time and historical data streams using the same logic.  + +Kappa+ allows Uber to reprocess archived data (stored in systems like Hive or HDFS) while using the same stream-processing framework they use for real-time data—Apache Flink. This eliminates the need for separate systems to handle real-time and batch data, simplifying operations and making the system more efficient. + +The backfill process also includes optimizations to handle the high throughput of historical data.  + +For example, Uber uses larger memory buffers and fine-tunes Flink jobs to handle out-of-order data or throttling. This ensures that even when processing large volumes of past data, the system performs smoothly without overwhelming resources. + +## Key Lessons + +Uber's journey in building its real-time data infrastructure is packed with valuable lessons. + +Here are the key ones to keep in mind: + +### 1 - Open-Source Adoption + +Uber’s decision to adopt open-source technologies was essential for building its infrastructure quickly and scaling effectively. Tools like Kafka, Flink, and Pinot provided solid foundations. + +However, scaling them to meet Uber’s massive data needs required significant customizations.  + +For example, Uber added features like Kafka cluster federation, FlinkSQL, and Pinot upserts to handle their unique requirements for high availability, low-latency queries, and real-time data streaming.  + +While open-source adoption allowed Uber to innovate fast, they also had to invest heavily in adapting these technologies to fit their system’s scale and complexity. + +### 2 - Rapid System Development + +To keep pace with their evolving business needs, Uber focused on enabling rapid system development.  + +One way they achieved this was through client standardization. By ensuring a consistent interface for interacting with systems, Uber reduced the risk of breaking connections when making updates. Additionally, the use of thin client models (simplified client software that offloads much of the processing to centralized servers) allowed Uber to streamline updates and reduce maintenance overhead. + +A strong CI/CD (Continuous Integration/Continuous Deployment) framework was also critical. This framework allowed Uber to test, integrate, and deploy updates automatically, reducing the risk of bugs and ensuring that new features could be quickly rolled out without affecting the system’s stability. + +### 3 - Operational Automation + +Managing Uber’s massive data infrastructure manually would be impossible, so operational automation became a key priority.  + +Uber emphasized automation for tasks like scaling infrastructure, managing clusters, and deploying new services. This minimized the need for manual intervention, helping the system scale efficiently even as the company grew. + +In addition to automation, Uber implemented robust monitoring and alerting systems. These systems allow the engineering teams to keep track of the health of the infrastructure in real-time, alerting them to any issues so they can be addressed quickly before they impact critical services like surge pricing or Uber Eats operations. + +### 4 - User Onboarding and Debugging + +With so many engineers, data scientists, and operational teams interacting with Uber’s data infrastructure, automated onboarding was crucial.  + +Uber developed processes that allowed new users to quickly access data, create Kafka topics, and launch Flink jobs without needing deep technical knowledge. + +Additionally, automated data discovery and auditing tools helped users find the data they needed and ensured that the data flowing through the system was accurate and reliable. By automating these processes, Uber reduced the workload for their core engineering teams and enabled more users to interact with the data systems independently. + +## Conclusion + +Uber's real-time data infrastructure is a vital part of its business operations, supporting everything from surge pricing and UberEats dashboards to real-time machine learning predictions.  + +By leveraging a combination of open-source technologies like Apache Kafka, Flink, Pinot, and Presto, Uber has built a highly scalable and reliable system that processes trillions of messages and petabytes of data every day. + +Key innovations, such as the Active-Active Kafka setup for high availability and the Kappa+ architecture for seamless backfill support, allow Uber to maintain real-time and historical data processing with minimal disruption. The infrastructure’s success also stems from Uber’s emphasis on customization, rapid development with thin client models, and extensive operational automation. + +As Uber continues to scale, these technologies and strategies provide the foundation for further innovation, enabling it to respond to new challenges while maintaining the high-performance standards required to serve millions of users globally. Uber's journey highlights the importance of combining open-source solutions with tailored engineering efforts to meet the needs of a fast-growing, data-driven organization. + +## References + +- [How Uber Manages Petabytes of Real-Time Data](https://blog.bytebytego.com/p/how-uber-manages-petabytes-of-real) +- [Real-time Data Infrastructure at Uber](https://arxiv.org/pdf/2104.00087) +- [Kafka Introduction](https://kafka.apache.org/documentation/#gettingStarted) +- [Pinot Architecture](https://docs.pinot.apache.org/basics/concepts/architecture) +- [What is Presto?](https://prestodb.io/docs/current/overview/concepts.html) diff --git a/docs/computer-science/iot/industrial-iot-iiot.md b/docs/computer-science/iot/industrial-iot-iiot.md index 0b2f3e9362c..c8f8e514186 100755 --- a/docs/computer-science/iot/industrial-iot-iiot.md +++ b/docs/computer-science/iot/industrial-iot-iiot.md @@ -90,3 +90,5 @@ Industrial Internet - DZone Guide Industrial Internet Reference Architecture [A Technical Exploration of IoT's Impact on Manufacturing](https://www.linkedin.com/pulse/technical-exploration-iots-impact-manufacturing-ankush-rana-obuac/) + +Digital Factory - [Altizon Inc. – Industrial Internet of Things (IIoT) Platform](https://altizon.com/) diff --git a/docs/computer-science/security/authentication/intro.md b/docs/computer-science/security/authentication/intro.md index 8568d08555f..e24f54aa3ad 100755 --- a/docs/computer-science/security/authentication/intro.md +++ b/docs/computer-science/security/authentication/intro.md @@ -153,6 +153,7 @@ For example, Google implements Single Sign On in its services. Google's central - [Keycloak](https://www.keycloak.org/) - Okta - Auth0 + - [FusionAuth: Auth. Built for Devs, by Devs](https://fusionauth.io/) - Supertokens - https://supertokens.io - **jumpcloud (Oyster)** - https://jumpcloud.com/daas-glossary/identity-management - https://magic.link diff --git a/docs/computer-science/system-design/microservice-architecture/readme.md b/docs/computer-science/system-design/microservice-architecture/readme.md index c964ad651bd..86bf465a166 100755 --- a/docs/computer-science/system-design/microservice-architecture/readme.md +++ b/docs/computer-science/system-design/microservice-architecture/readme.md @@ -4,3 +4,7 @@ - [Design Patterns](computer-science/system-design/microservice-architecture/design-patterns.md) - [Domain Driven Design](domain-driven-design) - [Example](computer-science/system-design/microservice-architecture/example.md) + +[Data Sharing Between Microservices - ByteByteGo Newsletter](https://blog.bytebytego.com/p/data-sharing-between-microservices) + +![Data Sharing Between Microservices](../../../media/Screenshot%202024-10-18%20at%206.55.20%20PM.jpg) diff --git a/docs/courses/course-credit-risk-modeling/fraud-detection-and-prevention.md b/docs/courses/course-credit-risk-modeling/fraud-detection-and-prevention.md index 66221a30fee..f9891719261 100644 --- a/docs/courses/course-credit-risk-modeling/fraud-detection-and-prevention.md +++ b/docs/courses/course-credit-risk-modeling/fraud-detection-and-prevention.md @@ -23,6 +23,14 @@ Amazon Fraud Detector is a fully managed service that makes it easy to identify - Online Fraud Insights - Account Takeover Insights +### Model metrics + +![Model performance charts](../../media/Screenshot%202024-10-16%20at%203.52.43%20PM.jpg) + +![ROC Curve](../../media/Screenshot%202024-10-16%20at%203.52.52%20PM.jpg) + +![Model variable importance](../../media/Screenshot%202024-10-16%20at%203.53.40%20PM.jpg) + ### Links - [What is Amazon Fraud Detector? - Amazon Fraud Detector](https://docs.aws.amazon.com/frauddetector/latest/ug/what-is-frauddetector.html) diff --git a/docs/courses/course-time-series-analysis/exponential-smoothing.md b/docs/courses/course-time-series-analysis/exponential-smoothing.md index f8f423f4bce..5f5b73cfeb3 100755 --- a/docs/courses/course-time-series-analysis/exponential-smoothing.md +++ b/docs/courses/course-time-series-analysis/exponential-smoothing.md @@ -1,17 +1,15 @@ # Exponential Smoothing -Exponential smoothingis a [rule of thumb](https://en.wikipedia.org/wiki/Rule_of_thumb) technique for smoothing [time series](https://en.wikipedia.org/wiki/Time_series) data using the exponential [window function](https://en.wikipedia.org/wiki/Window_function). Whereas in the [simple moving average](https://en.wikipedia.org/wiki/Simple_moving_average) the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time. It is an easily learned and easily applied procedure for making some determination based on prior assumptions by the user, such as seasonality. Exponential smoothing is often used for analysis of time-series data. +Exponential smoothing is a [rule of thumb](https://en.wikipedia.org/wiki/Rule_of_thumb) technique for smoothing [time series](https://en.wikipedia.org/wiki/Time_series) data using the exponential [window function](https://en.wikipedia.org/wiki/Window_function). Whereas in the [simple moving average](https://en.wikipedia.org/wiki/Simple_moving_average) the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time. It is an easily learned and easily applied procedure for making some determination based on prior assumptions by the user, such as seasonality. Exponential smoothing is often used for analysis of time-series data. Exponential smoothing is one of many [window functions](https://en.wikipedia.org/wiki/Window_functions) commonly applied to smooth data in [signal processing](https://en.wikipedia.org/wiki/Signal_processing), acting as [low-pass filters](https://en.wikipedia.org/wiki/Low-pass_filter) to remove high frequency [noise](https://en.wikipedia.org/wiki/Noise). This method is preceded by [Poisson](https://en.wikipedia.org/wiki/Sim%C3%A9on_Denis_Poisson)'s use of recursive exponential window functions in convolutions from the 19th century, as well as [Kolmogorov and Zurbenko's use of recursive moving averages](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Zurbenko_filter) from their studies of turbulence in the 1940s. Exponential smoothing refers to the use of an **exponentially weighted moving average (EWMA)** to "smooth" a time series. If you have some time seriesx~t~, you can define a new time series s~t~that is a smoothed version ofx~t~. -s~t~=αx~t~+(1−α)s~t-1~ +`s~t~=αx~t~+(1−α)s~t-1~` 1. Basic exponential smoothing - 2. Double exponential smoothing - 3. Triple exponential smoothing https://en.wikipedia.org/wiki/Exponential_smoothing diff --git a/docs/courses/course-time-series-analysis/intro-time-series.md b/docs/courses/course-time-series-analysis/intro-time-series.md index dc98bba60fd..b69dbe4b8f9 100755 --- a/docs/courses/course-time-series-analysis/intro-time-series.md +++ b/docs/courses/course-time-series-analysis/intro-time-series.md @@ -1,13 +1,17 @@ # Intro - Time Series -- QQ-Plot (Quantile-Quantile Plot) - - Used to determine whether a data set is distributed a certain way (To see if the values of a data set follow a given distribution?) - - Usually showcases how the data fits a Normal Distribution - - Takes all the values a variable can take, and arranges them in accending order - - ![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image1.jpg) - - Y-axis expresses the price with highest one on top and lowest on bottom - - X-axis expresses theoretical quantiles of the dataset. How many standard deviations away from the mean these values are. - - Diagonal line shows what the data points should follow, if they are Normally Distributed +### QQ-Plot (Quantile-Quantile Plot) + +- Used to determine whether a data set is distributed a certain way (To see if the values of a data set follow a given distribution?) +- Usually showcases how the data fits a Normal Distribution +- Takes all the values a variable can take, and arranges them in ascending order + +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image1.jpg) + +- Y-axis expresses the price with highest one on top and lowest on bottom +- X-axis expresses theoretical quantiles of the dataset. How many standard deviations away from the mean these values are. +- Diagonal line shows what the data points should follow, if they are Normally Distributed + - ACF - PACF - Seasonal Decomposition Graph @@ -22,14 +26,8 @@ ## Modeling -- Auto Regression (AR) - -Predict movements based on correlations - -- Moving Averages (MA) - -Account for unexpected shocks in our data - +- Auto Regression (AR) - Predict movements based on correlations +- Moving Averages (MA) - Account for unexpected shocks in our data - ARMA - ARIMA (Auto Regressive Integrated Moving Average) - ARMAX @@ -52,21 +50,11 @@ Account for unexpected shocks in our data https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python - **Univariate Time Series** - - Simple Average - -Sum all points divided by total number of points - -- Moving Average - -Take average of the last few data points only - -- Single Exponential Smoothing - -Attach larger weights to more recent observations than to observations from the distant past - + - Simple Average - Sum all points divided by total number of points +- Moving Average - Take average of the last few data points only +- Single Exponential Smoothing - Attach larger weights to more recent observations than to observations from the distant past - Holt's linear trend method - Holt's winter seasonal method - - **Multivariate Time Series** - Finds linear interdependencies between multiple variables - Examples - ARIMA, ARIMAX @@ -88,21 +76,16 @@ There is no chronological order in cross-sectional data ## Time-series variables -X = $ S&P 500 - -T = entire period - -t = a single period +- X = $ S&P 500 +- T = entire period +- t = a single period -## Pecurliarities +## Peculiarities - Intervals need to be identical - Adjust frequency - -roll up - Aggregating the data - -Increase frequency - Approximate missing values - + - roll up - Aggregating the data + - Increase frequency - Approximate missing values - Requires chronological order - Choose a cut off time for train test data - Cannot shuffle data so cannot apply some ML models @@ -114,19 +97,13 @@ Because all the other columns would automatically become time series, since ther Setting the desired frequency -df_comp = df_comp.asfreq() - -h - hourly - -w - weekly - -d - daily - -m - monthly - -a - annual / yearly - -b - business days / weekdays +- df_comp = df_comp.asfreq() +- h - hourly +- w - weekly +- d - daily +- m - monthly +- a - annual / yearly +- b - business days / weekdays ## Filling the missing values @@ -162,7 +139,7 @@ Arbitrage - Buy and sell commodities and make a safe profit, while the price adj In the most intuitive sense, stationarity means that the statistical properties of a process generating a time series do not change over time. It does not mean that the series does not change over time, just that the way it changes does not itself change over time. The algebraic equivalent is thus a linear function, perhaps, and not a constant one; the value of a linear function changes as 𝒙 grows, but the way it changes remains constant - it has a constant slope; one value that captures that rate of change. -![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image4.jpg) +![](../../media/Pasted%20image%2020241011132306.png) Figure 1: Time series generated by a stationary (top) and a non-stationary (bottom) processes. @@ -305,4 +282,4 @@ It cancels out all additional channels a previous period value affects the prese ## What is the difference between the ACF and the PACF? -The ACF measures the accumulated effects past lags have on the current value, while the PACFmeasures the direct effect. +The ACF measures the accumulated effects past lags have on the current value, while the PACF measures the direct effect. diff --git a/docs/courses/course-time-series-analysis/time-series-modeling.md b/docs/courses/course-time-series-analysis/time-series-modeling.md index 225cece476e..09bcf9f7fda 100755 --- a/docs/courses/course-time-series-analysis/time-series-modeling.md +++ b/docs/courses/course-time-series-analysis/time-series-modeling.md @@ -13,9 +13,7 @@ Patterns in the past persist in the future 2. Parsimonious (as simple as possible) 1. Generally: Simpler model > Complex model - 2. Significantly better predictions: Simpler model < Complex model - 3. Log-Likelihood Ratio test (LLR) Can be used for Different degrees of freedom @@ -394,20 +392,20 @@ It serves as a sort of ARMA equivalent to the ARCH, where we're including both p ![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image27.jpg) -How is the GARCHdifferent from the ARCH? +How is the GARCH different from the ARCH? Includes past conditional variances -Why is the GARCHsimilar to the ARMA? +Why is the GARCH similar to the ARMA? -Includes past values and past errors.(conditional variances and squared residuals) +Includes past values and past errors (conditional variances and squared residuals) -How does the GARCH (1,1) compare to complex ARCH counterparts? +How does the GARCH(1,1) compare to complex ARCH counterparts? It outperforms even the ARCH(12) model we examined earlier. - No higher order GARCH models outperform the GARCH(1,1) when it comes to variance of market returns -- All the effects of the conditional variacne 2 days ago will be contained in the conditional variance of yesterday +- All the effects of the conditional variance 2 days ago will be contained in the conditional variance of yesterday - No need to include more than 1 GARCH component How do GARCH(p,q) models for the volatility of returns perform compared to the GARHC(1,1)? diff --git a/docs/databases/concepts/types-of-databases.md b/docs/databases/concepts/types-of-databases.md index e97e3db4bb4..bccd6b9916f 100755 --- a/docs/databases/concepts/types-of-databases.md +++ b/docs/databases/concepts/types-of-databases.md @@ -94,6 +94,9 @@ https://rethinkdb.com https://medium.com/terminusdb/graph-fundamentals-part-1-rdf-60dcf8d0c459 - [Neo4j](https://tinkerpop.apache.org/docs/current/reference/#neo4j-gremlin)- OLTP graph database (embedded and high availability) (open source, noSQL graph database) - [Build Graph Databases with Neo4j](https://www.freecodecamp.org/news/learn-neo4j-database-course/) + - Use cases - [Graph Data Science for Fraud Detection & Analytics | Neo4j](https://neo4j.com/use-cases/fraud-detection/) + - [Graph Database Use Cases & Solutions](https://neo4j.com/use-cases/) + - [Generative AI - Ground LLMs with Knowledge Graphs](https://neo4j.com/generativeai/) - [neo4j-gremlin-bolt](https://github.com/SteelBridgeLabs/neo4j-gremlin-bolt)- OLTP graph database (using Bolt Protocol). - [OrientDB](https://github.com/orientechnologies/orientdb-gremlin)- OLTP graph database - [Apache S2Graph](https://s2graph.apache.org/)- OLTP graph database running on Apache HBase. diff --git a/docs/databases/others/technologies-tools.md b/docs/databases/others/technologies-tools.md index bf2e895bdc5..dc2f33a6696 100755 --- a/docs/databases/others/technologies-tools.md +++ b/docs/databases/others/technologies-tools.md @@ -68,6 +68,22 @@ https://github.com/dbeaver/dbeaver - show line numbers - SQL Editor > Formatting > Keyword Case: Upper +### Import large data + +- Open new connection (s): Yes +- Use transactions: Yes +- **Do Commit after row insert**: 100000 (increase more if large rows and less columns) +- **Use multi-row Insert**: Yes +- **Multi-row insert batch size**: 5000 (increase more if large rows and less columns) +- Skip bind values: No +- Disable batches: No +- Ignore duplicate rows: No +- Method for duplicate key case: +- Transfer auto-generated columns: Yes +- Disable referential integrity: No +- **Use bulk load**: Yes +- Truncate before load: No + ## Teradata Analytics, Data Lakes and Data Warehouses Unified in the Cloud diff --git a/docs/databases/sql-databases/postgres/readme.md b/docs/databases/sql-databases/postgres/readme.md index 83909838619..e0cfa10c5d6 100755 --- a/docs/databases/sql-databases/postgres/readme.md +++ b/docs/databases/sql-databases/postgres/readme.md @@ -7,6 +7,7 @@ - [parameters-configuration-optimization](databases/sql-databases/postgres/parameters-configuration-optimization.md) - [Replication](databases/sql-databases/postgres/replication.md) - [others](databases/sql-databases/postgres/others.md) +- [Postgres Commands](languages/sql/postgres-commands.md) ## Introduction @@ -41,6 +42,24 @@ PostgreSQL shares many of the same advantages of MySQL. It is easy to use, inexp - Up to 12 tables, postgres tries all possible join operations and prunning for optimization query - Generic query optimizer +## Schema vs Database + +In PostgreSQL, a database is a collection of one or more named schemas, and a schema is a collection of tables, views, functions, and other objects within a database: + +Here are some differences between databases and schemas in PostgreSQL: + +- **Purpose -** A database is used to isolate data, while a schema is used to organize and manage database objects. +- **Namespace -** Schemas are logically separated by a namespace within a database. +- **Structure -** A database can contain multiple schemas, and each schema can contain multiple tables. +- **Object names -** The same object name can be used in different schemas, but two objects of the same type cannot have the same name within a schema. +- **Access -** Users can access objects in any schema in the database they are connected to, if they have privileges to do so. + +#### Some reasons to use schemas include + +- Allowing multiple users to use one database without interfering with each other +- Organizing database objects into logical groups +- Putting third-party applications into separate schemas so they do not collide with the names of other objects + ## Functions in Postgres Postgres provides a powerful server-side function environment in multiple programming languages. diff --git a/docs/decentralized-applications/coins-tokens-chains/wallets.md b/docs/decentralized-applications/coins-tokens-chains/wallets.md index 9c9f382338f..22e52eb5c63 100644 --- a/docs/decentralized-applications/coins-tokens-chains/wallets.md +++ b/docs/decentralized-applications/coins-tokens-chains/wallets.md @@ -98,6 +98,10 @@ In the blockchain world, every on-chain event (a transaction that interacts with [What is a signature in MetaMask? - MetaMask](https://support.metamask.io/hc/en-us/articles/15756276171163-What-is-a-signature-in-MetaMask-) +## Institutional + +- Bitgo + ## Technologies [**Threshold Signatures Explained | Binance Academy**](https://academy.binance.com/en/articles/threshold-signatures-explained) diff --git a/docs/decentralized-applications/defi-apps.md b/docs/decentralized-applications/defi-apps.md index 50cb82fd614..1b76c8891fb 100644 --- a/docs/decentralized-applications/defi-apps.md +++ b/docs/decentralized-applications/defi-apps.md @@ -28,3 +28,7 @@ - [TELEGRAM Trading Bots: Game-Changer or Fad?? - YouTube](https://www.youtube.com/watch?v=FzgE0EPFpfY) - [Lybra Finance - Unleash the Utility of LST in DeFi](https://route2fi.substack.com/p/lybra-finance-unleash-the-utility) - [Lucid Finance](https://lucidfinance.xyz/) +- [Provenance Blockchain Foundation](https://provenance.io/) + - [Provenance Blockchain Explorer - Dashboard](https://explorer.provenance.io/dashboard) + - [Introduction | Provenance Blockchain Developer Portal](https://developer.provenance.io/docs/) + - [bfi.uchicago.edu/wp-content/uploads/Provenance-Whitepaper.pdf](https://bfi.uchicago.edu/wp-content/uploads/Provenance-Whitepaper.pdf) diff --git a/docs/devops/ides/mac.md b/docs/devops/ides/mac.md index f1f1391b62a..09777591ff3 100755 --- a/docs/devops/ides/mac.md +++ b/docs/devops/ides/mac.md @@ -75,6 +75,11 @@ Open two instance of same application - **open -n MQTT.fx.app** - **Google Drive for mac** - ~~bittorrent web~~ - ~~Popcorn~~ + - sudo xattr -cr /Applications/Popcorn-Time.app + - https://fusme.link/ + - https://jfper.link/ + - https://uxert.link/ + - https://yrkde.link/ - **VLC** or [GitHub - iina/iina: The modern video player for macOS.](https://github.com/iina/iina) - ~~Anki Flash Cards~~ - - ~~Anaconda navigator~~ - [~~https://conda.anaconda.org/conda-forge/~~](https://conda.anaconda.org/conda-forge/) @@ -133,6 +138,7 @@ https://www.google.com/inputtools - Duplicate Tab shortcut - Duplicate the current tab - ctrl + d - New tab to the right - cmd + t +- [ChatGPT Writer: Use AI on Any Site (GPT-4o, Claude, Gemini, and More)](https://chatgptwriter.ai/) ##### Screen recorders diff --git a/docs/economics/finance-investing/personal-finance.md b/docs/economics/finance-investing/personal-finance.md index a320e45474f..7019304dd2b 100644 --- a/docs/economics/finance-investing/personal-finance.md +++ b/docs/economics/finance-investing/personal-finance.md @@ -41,3 +41,5 @@ This is arguably the most important, and the most overlooked, pillar. It encapsu - [5 Emotional Mistakes That Lead to Common Financial Mistakes](https://freefincal.com/5-emotional-mistakes-that-lead-to-common-financial-mistakes/) - [How to stay invested in equity without worrying about market falls](https://freefincal.com/how-to-stay-invested-in-equity-without-worrying-about-market-falls/) - [10 Timeless Investing Tips for Financial Freedom - The 1% News](https://news.onepercentclub.io/plan/10-timeless-investing-tips-to-become-a-successful-investor/5382/) +- [Can I transfer Rs. 62 Lakhs from FDs to index funds and stay invested for 18 years?](https://freefincal.com/can-i-transfer-rs-62-lakhs-from-fds-to-index-funds-and-stay-invested-for-18-years/) +- [Life Decisions that Affect an Indian's Bank Balance](https://freefincal.com/life-decisions-that-affect-an-indians-bank-balance/) diff --git a/docs/economics/finance-investing/returns-tax-taxes-itr.md b/docs/economics/finance-investing/returns-tax-taxes-itr.md index 17a306b8827..c444ad636fc 100755 --- a/docs/economics/finance-investing/returns-tax-taxes-itr.md +++ b/docs/economics/finance-investing/returns-tax-taxes-itr.md @@ -90,6 +90,8 @@ https://cleartax.in/s/marginal-relief-surcharge 10% surcharge for people earning more than 50 lakh (get marginal relief surcharge) +[Tax Harvesting vs Portfolio Rebalancing: What is the difference?](https://freefincal.com/tax-harvesting-vs-portfolio-rebalancing-what-is-the-difference/) + ## Taxability For taxation purposes, all mutual funds with investments lower than 65% in equity instruments are considered debt funds. Short-term capital gains of less than 36 months are taxed corresponding to the investor's income tax slab. diff --git a/docs/economics/finance-investing/stocks-equity.md b/docs/economics/finance-investing/stocks-equity.md index 7c4d5436b01..fda6b35502e 100755 --- a/docs/economics/finance-investing/stocks-equity.md +++ b/docs/economics/finance-investing/stocks-equity.md @@ -141,6 +141,7 @@ https://www.moneycontrol.com/india-investors-portfolio - [How should Traders & Investors, trade or invest in a Bear Market? - YouTube](https://www.youtube.com/watch?v=GIIs2Pp_COY) - [Finshots is a stock-picking genius. But…](https://finshots.in/markets/finshots-is-a-stock-picking-genius-but/) - [Why portfolio rebalancing is important for investment success](https://freefincal.com/why-portfolio-rebalancing-is-important-for-investment-success/) +- [Mutual Fund Profit Booking vs Rebalancing: What is the difference?](https://freefincal.com/mutual-fund-profit-booking-vs-rebalancing-what-is-the-difference/) - [My Stock Portfolio Analysis: Mar 2024](https://freefincal.com/my-stock-portfolio-analysis-mar-2024/) - [SIP Top-Up: The Mindboggling math of SIP top up](https://www.hdfcfund.com/knowledge-stack/mf-vault/weekend-bytes/sip-top-mindboggling-math-sip-top) - [SWP: A smarter way to manage periodic expenses](https://www.hdfcfund.com/knowledge-stack/mf-vault/weekend-bytes/swp-smarter-way-manage-periodic-expenses) diff --git a/docs/economics/fintech-nbfc-banking-terms.md b/docs/economics/fintech-nbfc-banking-terms.md index d1fd2e7b258..a213b10f829 100755 --- a/docs/economics/fintech-nbfc-banking-terms.md +++ b/docs/economics/fintech-nbfc-banking-terms.md @@ -210,16 +210,18 @@ A loan tape is a snapshot of a fintech's customer base and outstanding balances, 38. Paisa bazaar 39. Bank bazaar 40. Home credit -41. Cred -42. Lazypay -43. Bharatpe -44. Payu -45. Amex -46. Uni pay 1/3rd card (uni app) -47. OfBusiness -48. axio, formerly Capital Float -49. Piramal Finance, one of India's largest non-banking lenders -50. Affirm (US - fintech) +41. Propelled (Education loan) +42. Jodo +43. Cred +44. Lazypay +45. Bharatpe +46. Payu +47. Amex +48. Uni pay 1/3rd card (uni app) +49. OfBusiness +50. axio, formerly Capital Float +51. Piramal Finance, one of India's largest non-banking lenders +52. Affirm (US - fintech) https://tracxn.com/d/soonicorn-awards/top-startups-in-india-fintech-2021 diff --git a/docs/economics/government-terms.md b/docs/economics/government-terms.md index 7eec786b2cf..f0314e9a03b 100755 --- a/docs/economics/government-terms.md +++ b/docs/economics/government-terms.md @@ -9,6 +9,7 @@ Fiscal policy is done by the government through spending. Monetary policy is don - UPI (Unified Payments Interface) - Aadhaar, DBT (Direct Benefit Transfer) - ONDC (Open Network for Digital Commerce) +- Government e Marketplace (GeM) | | **Excludable** | **Non-Excludable** | | ------------- | ----------------------------------------------------------- | ----------------------------------------- | diff --git a/docs/economics/mutual-funds/nifty-indexes.md b/docs/economics/mutual-funds/nifty-indexes-indices.md similarity index 91% rename from docs/economics/mutual-funds/nifty-indexes.md rename to docs/economics/mutual-funds/nifty-indexes-indices.md index 10e86173b1a..f9b5793c436 100644 --- a/docs/economics/mutual-funds/nifty-indexes.md +++ b/docs/economics/mutual-funds/nifty-indexes-indices.md @@ -1,4 +1,4 @@ -# Nifty Indexes +# Nifty Indexes / Indices ![Sensex then versus now](../../media/Pasted%20image%2020241006231135.jpg) @@ -43,6 +43,16 @@ [Nifty vs Nifty Next 50 vs Nifty Midcap 150 vs Nifty Smallcap 250](https://www.youtube.com/watch?v=gPaM-xy5O_w&ab_channel=freefincal-PrudentDIYInvesting%28freefincal%29) +[What Is NIFTY Midcap 150? How Can You Invest In This Midcap Index?](https://www.etmoney.com/learn/mutual-funds/what-is-nifty-midcap-150-how-can-you-invest-in-nifty-midcap-150/) + +[Nifty 50 vs Nifty Next 50 vs Nifty 100: Pick Your Large Cap Index](https://www.sharekhan.com/financial-blog/blogs/nifty-50-vs-nifty-next-50-vs-nifty-100-pick-your-large-cap-index) + +**Nifty 50:** The Nifty 50 comprises the 50 largest and most liquid companies listed on the National Stock Exchange of India. The stocks in the Nifty 50 are big players, typically large-cap stocks with a massive market value. + +**Nifty Next 50:** On the other hand, the Nifty Next 50 includes mid-cap companies that rank from 51 to 100 in terms of full market cap. So naturally, the average market cap of these companies is smaller than that of Nifty 50 constituents. The total market cap of the Nifty Next 50 is around 15-18% of the full NSE 500 market cap. + +**Nifty 100:** Finally, the Nifty 100 is an index that combines the Nifty 50 and Next 50, covering the 100 most valuable listed companies. As you can expect, the market cap is between Nifty 50 and Next 50. So it covers around 75-80% of the total market cap of the 500 stocks trading on the NSE. + ## Sectoral Indices - [Nifty Auto](https://www.niftyindices.com/indices/equity/sectoral-indices/nifty-auto) diff --git a/docs/economics/mutual-funds/readme.md b/docs/economics/mutual-funds/readme.md index 4dcf43e0368..074e03a9f7a 100755 --- a/docs/economics/mutual-funds/readme.md +++ b/docs/economics/mutual-funds/readme.md @@ -7,7 +7,7 @@ - [Mutual Fund Indicators](economics/mutual-funds/mutual-funds-indicators.md) - [Debentures / Bonds](economics/mutual-funds/debentures-bonds.md) - [Index Investing / ETF](economics/mutual-funds/index-investing-etf.md) -- [Nifty Indexes](economics/mutual-funds/nifty-indexes.md) +- [Nifty Indexes](economics/mutual-funds/nifty-indexes-indices.md) - [Equity Mutual Funds](economics/mutual-funds/equity-mutual-funds.md) - [Others](economics/mutual-funds/others.md) diff --git a/docs/knowledge/applied-sciences/civil-engineering.md b/docs/knowledge/applied-sciences/civil-engineering.md index b39b1059dbc..eb0303a9ac0 100755 --- a/docs/knowledge/applied-sciences/civil-engineering.md +++ b/docs/knowledge/applied-sciences/civil-engineering.md @@ -45,3 +45,5 @@ Habitat - 67 - [How a 23-Year-Old Solved Urban Sprawl - YouTube](https://www.you - [How to Control a Crowd - YouTube](https://www.youtube.com/watch?v=C_B09FZwSbA) - [Rare earths crunch? Why we need them and who has them | Business Beyond - YouTube](https://www.youtube.com/watch?v=cL4YXCKrJJ4) - [Jackhammer - Wikipedia](https://en.wikipedia.org/wiki/Jackhammer) +- [Mivan Shuttering क्या होती है? | Aluminum Formwork and Its Components - YouTube](https://www.youtube.com/watch?v=lti2g-yAuwQ) + - [What Is Mivan Construction| Its Advantages | How To Stop Wealth Destruction - YouTube](https://www.youtube.com/watch?v=ylb0Uu659Lo) diff --git a/docs/knowledge/biology/mbbs-medicine-medical-health.md b/docs/knowledge/biology/mbbs-medicine-medical-health.md index 4bbf350efa2..0f244bb0cea 100755 --- a/docs/knowledge/biology/mbbs-medicine-medical-health.md +++ b/docs/knowledge/biology/mbbs-medicine-medical-health.md @@ -175,8 +175,9 @@ https://en.wikipedia.org/wiki/Urine_therapy - 1 tablet after dinner 1 week apart twice - Zentel Chewable Tablet is an antiparasitic medicine, used for the treatment of parasitic worm infections. It works by killing the worms that cause infections and stops the infection from spreading. - Symptoms - Teeth Grinding / Itching at anus -- Cetirizine - - Cetirizine is **a relatively safe and effective medication for treating allergic rhinitis, urticaria, and allergic conjunctivitis**. +- Cetirizine (Antihistamine that relieves allergy symptoms) + - Cetirizine is **a relatively safe and effective medication for treating allergic rhinitis, urticaria, and allergic conjunctivitis**. + - [Cetirizine: View Uses, Side Effects and Medicines | 1mg](https://www.1mg.com/generics/cetirizine-209553) - D3 - CADILA Calcigen D3 (Vitamin D3 - Cholecalciferol) 60000 IU - Pack of 5 * 4 Capsules, Once A Week (4) - [Carbamide Forte Vitamin D3 60000 IU - Chewable Cholecalciferol Supplement | High Potency Vitamin D Supplement, D3 Vitamin Supplement for Women & Men | Orange Flavour - 40 Tablets. : Amazon.in: Health & Personal Care](https://www.amazon.in/Carbamide-Forte-Vitamin-Tablets-VITAMIN/dp/B09H6XXP13/) - 349 - 40 tablets - [CADILA Calcigen D3 (Vitamin D3 - Cholecalciferol) 60000 IU - Pack of 5 \* 4 Capsules, Once A Week (4) : Amazon.in: Health & Personal Care](https://www.amazon.in/Calcigen-D3-Vitamin-Cholecalciferol-Capsules/dp/B09B7DFNDP) diff --git a/docs/knowledge/general/important-dates-timelines.md b/docs/knowledge/general/important-dates-timelines.md index 0644a73b308..48fbfcba8c4 100755 --- a/docs/knowledge/general/important-dates-timelines.md +++ b/docs/knowledge/general/important-dates-timelines.md @@ -1,11 +1,10 @@ # Important Dates / Timelines -| | | -|---|---| -| 4th April 1949 | NATO Treaty | -| 5th - 16th June 1972 | Stockholm Conference - UNEP (United Nations Environment Programme) -Motto - Only one Earth | -| 3rd March 1973 | CITES Convention on International Trade in Endangered Species of wild flora and fauna, Washington Declaration | +| | | +| -------------------- | ------------------------------------------------------------------------------------------------------------- | +| 4th April 1949 | NATO Treaty | +| 5th - 16th June 1972 | Stockholm Conference - UNEP (United Nations Environment Programme) - Motto - Only one Earth | +| 3rd March 1973 | CITES Convention on International Trade in Endangered Species of wild flora and fauna, Washington Declaration | ### January @@ -211,3 +210,9 @@ Motto - Only one Earth | - December 25: Christmas Day https://byjus.com/free-ias-prep/important-national-international-days-dates-for-upsc-prelims + +## Others + +- AD - Anno Domini, CE - Common Era +- BC - Before Christ, BCE - Before Common Era +- BP - Before Present - [Before Present - Wikipedia](https://en.wikipedia.org/wiki/Before_Present) diff --git a/docs/knowledge/youtube-podcasts-magazine-apps.md b/docs/knowledge/youtube-podcasts-magazine-apps.md index 3795585d6bf..50a88033b83 100755 --- a/docs/knowledge/youtube-podcasts-magazine-apps.md +++ b/docs/knowledge/youtube-podcasts-magazine-apps.md @@ -456,7 +456,9 @@ Indian History - https://www.anirudhkanisetti.com/podcasts ### Torrents - [GitHub - iptv-org/iptv: Collection of publicly available IPTV channels from all over the world](https://github.com/iptv-org/iptv) + - https://thepiratebay.isworking.xyz/ - [84+ Working 1337x Proxy (October 2023) Mirror Sites To Unblock](https://technoxyz.com/1337x-proxy-unblock/) + - [100% Working The Pirate Bay Proxy - Ads Free September 2024](https://thepiratebayproxy.github.io/) - [1337x.to - The 1337x Official Site | 1337x Official Home](https://1337x-official.to/) - [**Torrent Search Engine** | 1337x.to](https://1337x-to.pages.dev/) - [1337x.to | Download Free Torrent and Magnet Links](https://1337x.bz/) diff --git a/docs/languages/sql/ddl-data-definition-language/create-table.md b/docs/languages/sql/ddl-data-definition-language/create-table.md index a577019d759..e0a65bba829 100755 --- a/docs/languages/sql/ddl-data-definition-language/create-table.md +++ b/docs/languages/sql/ddl-data-definition-language/create-table.md @@ -52,6 +52,37 @@ ALTER TABLE communication_exceptions MODIFY update_date DATETIME NOT NULL DEFAUL 5. Swap the old and new tables 6. Unlock the tables & drop the original +### Postgres ON UPDATE + +The `ON UPDATE` clause is not supported in PostgreSQL. To achieve similar functionality, you can use a `BEFORE UPDATE` trigger to automatically update the `update_date` column whenever a row is modified. Here’s how you can create the table and the trigger: + +```sql +-- Create the table +CREATE TABLE test ( + id SERIAL PRIMARY KEY, + create_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + update_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + value INT +); + +-- Create the trigger function to update the update_date column +CREATE OR REPLACE FUNCTION update_timestamp() +RETURNS TRIGGER AS $$ +BEGIN + NEW.update_date = CURRENT_TIMESTAMP; + RETURN NEW; +END; +$$ LANGUAGE plpgsql; + +-- Create the trigger that calls the function before each update +CREATE TRIGGER update_test_timestamp +BEFORE UPDATE ON test +FOR EACH ROW +EXECUTE FUNCTION update_timestamp(); +``` + +[Automatically updating a timestamp column in PostgreSQL using Triggers | by Avinash | Medium](https://aviyadav231.medium.com/automatically-updating-a-timestamp-column-in-postgresql-using-triggers-98766e3b47a0) + ### Redshift ```sql diff --git a/docs/languages/sql/postgres-commands.md b/docs/languages/sql/postgres-commands.md index f0e00af84fc..0ccec4d7e0a 100755 --- a/docs/languages/sql/postgres-commands.md +++ b/docs/languages/sql/postgres-commands.md @@ -1,6 +1,10 @@ # Postgres Commands -`brew install postgresql` +```bash +brew install postgresql + +sudo apt-get install -y postgresql14 +``` ## psql - start postgres sql query engine @@ -43,7 +47,7 @@ GRANT SELECT ON ALL TABLES IN SCHEMA public TO quicksight; REASSIGN OWNED BY quicksight TO postgres; -- or some other trusted role DROP OWNED BY ryan; -DROPUSERryan; +DROP USER ryan; ``` psql has a `ECHO_HIDDEN` variable you can set to show (or 'echo') any SQL queries performed behind the scenes by backslash commands. @@ -56,6 +60,10 @@ psql has a `ECHO_HIDDEN` variable you can set to show (or 'echo') any SQL querie CREATE DATABASE zenalytix_db_new; +CREATE TABLE test ( id SERIAL PRIMARY KEY, create_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP, update_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP, value INT ); + +Drop table test; + psql -U abc -d zenalytix_db_new -p 5432 psql -h localhost -p 5432 -U postgres -d airflow @@ -119,6 +127,22 @@ DROP USER test; delete from task_instance where execution_date::date < '2021-01-13 21:00:00+0'; ``` +### Administration + +```sql +-- Postgres 14 adds the predefined, non-login roles [**`pg_read_all_data`** / **`pg_write_all_data`**](https://www.postgresql.org/docs/current/predefined-roles.html). +-- They have `SELECT` / `INSERT`, `UPDATE`, `DELETE` privileges for _all_ tables, views, and sequences. Plus `USAGE` on schemas. We can `GRANT` membership in these roles: + +GRANT pg_read_all_data TO my_user; +GRANT pg_write_all_data TO my_user; +-- [sql - PostgreSQL: Give all permissions to a user on a PostgreSQL database - Stack Overflow](https://stackoverflow.com/questions/22483555/postgresql-give-all-permissions-to-a-user-on-a-postgresql-database) + +REVOKE ALL PRIVILEGES ON DATABASE loantape FROM app_quicksight; +drop user app_quicksight; + +set password_encryption = 'md5'; +``` + ### Configurations ```sql diff --git a/docs/management/jobs/good-profiles.md b/docs/management/jobs/good-profiles.md index f7323599b84..f6e0382f98e 100755 --- a/docs/management/jobs/good-profiles.md +++ b/docs/management/jobs/good-profiles.md @@ -11,6 +11,7 @@ - Andreas Klinger - https://klinger.io - [Jeroen Bouma | Financial Risk Analyst](https://www.jeroenbouma.com/) - https://www.linkedin.com/in/vishalkmishra/ +- https://www.linkedin.com/in/pavankmurthy/ - **Software Developer Good -** https://drive.google.com/drive/folders/1zMuTUjsifK9QVvrwF56oE8EJcgw9YBhJ?usp=sharing - **Pradeep Parameswaran** diff --git a/docs/management/jobs/readme.md b/docs/management/jobs/readme.md index 2851e4af249..f7dd968416d 100644 --- a/docs/management/jobs/readme.md +++ b/docs/management/jobs/readme.md @@ -135,6 +135,145 @@ In early career, taking bets vs random job hopping is critical - Keylime (International Development) - Fiverr +Forget [Fiverr](https://www.linkedin.com/company/fiverr-com/) , [Naukri.com](https://www.linkedin.com/company/naukri.com/) , [Shine.com](https://www.linkedin.com/company/shinecom/), and [Monster](https://www.linkedin.com/company/monster/) they are just overhyped. + +Here are 30 websites to find remote jobs that pay in USD: + +1. [Athyna](https://www.linkedin.com/company/athyna/) + +Best site for finding your dream remote job and working for companies from all around the world. + +🔗 Link: [www.athyna.com](http://www.athyna.com/) + +2. [Wellfound](https://www.linkedin.com/company/wellfoundhq/) + +- Unique jobs +- Top Companies +- Over 130,000 jobs + +🔗 Link: [https://wellfound.com/jobs](https://wellfound.com/jobs) + +3. [RemoteOK](https://www.linkedin.com/company/remoteok/) + +- Over 600,000 jobs +- Work from anywhere +- The no. 1 remote job board + +🔗 Link: [https://remoteok.com](https://remoteok.com/) + +4. [Remotive](https://www.linkedin.com/company/remotive.io/) + +- Over 30,000 jobs +- Vetted tech companies +- Full remote job opportunities + +🔗 Link: [https://remotive.com](https://remotive.com/) + +5. [Remote](https://www.linkedin.com/company/remote.com/).co + +- Hand curated +- Grow remotely +- 146 remote companies + +🔗 Link: [https://lnkd.in/eYgwD4bB](https://lnkd.in/eYgwD4bB) + +6. [FlexJobs](https://www.linkedin.com/company/flexjobs-com/) - Vetted Remote & Flexible Jobs + +- 50+ categories +- Over 42,000 jobs +- 5,509 companies + +🔗Link: [https://www.flexjobs.com](https://www.flexjobs.com/) + +7. [JustRemote](https://www.linkedin.com/company/justremote/) + +- Jobs that fit your life +- Fully and partially remote +- Top remote working companies + +🔗Link: [https://lnkd.in/d5ZqAXm](https://lnkd.in/d5ZqAXm) + +8. [PowerToFly](https://www.linkedin.com/company/powertofly/) + +-2987 Remote jobs + +- Jobs tailored to your skillset +- Land a job at a company committed to diversity & inclusion + +🔗Link: [https://powertofly.com/](https://powertofly.com/) + +9. [RemoteWoman](https://www.linkedin.com/company/remotewoman/) + +-Women have the opportunity to work from home, enhancing their productivity and flexibility + +🔗 Link: [remotewoman.com](http://remotewoman.com/) + +10. Toptal + +- Top Companies +- Exclusive network +- Community of experts + +🔗Link: [https://www.toptal.com](https://www.toptal.com/) + +11. [Working Nomads](https://www.linkedin.com/company/working-nomads/) + +-100% remote jobs +-Work from anywhere +-For digital working nomads + +🔗 Link: [https://lnkd.in/efQwAr7V](https://lnkd.in/efQwAr7V) + +12. Simply Hired (simply [hired.com](http://hired.com/)) + +13. Angel List ([angel.co/Jobs](http://angel.co/Jobs)) + +14. Virtual Vocations ([virtaalvocations.com](http://virtaalvocations.com/)) + +15. Stack Overflow Jobs (Stack [Overflow.com/Jobs](http://overflow.com/Jobs)) + +16. Remote Work (Remote [work.com](http://work.com/)) + +17. Dice ([dice.com](http://dice.com/)) + +18. Jobspresso ([Jobspresso.co](http://jobspresso.co/)) + +19. Upwork ([upwork.com](http://upwork.com/)) + +20. Outsourcely ([Outsourcely.com](http://outsourcely.com/)) + +21. Europe Remotely ([europeremiely.com](http://europeremiely.com/)) + +22. [WeWork](https://www.linkedin.com/company/wework/) + +23. Remote ok Europe ([remoteok.io](http://remoteok.io/)) + +24. Flex Jobs ([flexjobs.com](http://flexjobs.com/)) + +25. Remole of Asia ([remoteok.io/asia](http://remoteok.io/asia)) + +26. Remote Jobs [remotejobs](https://www.linkedin.com/company/remote-jobs/) + +27.remote4me : Customize your remote job search based on location, salary, and more. + +🔗 Link ([https://remote4me.com/](https://remote4me.com/)) + +28. [AutoApply.Jobs](http://autoapply.jobs/) Jobs + +[https://autoapply.jobs/](https://autoapply.jobs/) + +29. @Finalclub + +Transform your LinkedIn connections into email lists with this ChatGPT-Powered Email Finding Extension. + +[https://finalscout.com](https://finalscout.com/) + +30. [Yoodli AI Communication Coach](https://www.linkedin.com/company/yoodli/): + +Enhance your online meeting job interviews with private, real-time speech coaching. + +[https://app.yoodli.ai/](https://app.yoodli.ai/) + ### General Interest Job Sites with Remote Search Filters - idealista diff --git a/docs/management/project-management/intro.md b/docs/management/project-management/intro.md index deb69de428d..bb877962f3b 100755 --- a/docs/management/project-management/intro.md +++ b/docs/management/project-management/intro.md @@ -185,6 +185,8 @@ A business sends an RFQ when they already know what they want to buy and are loo A business sends an RFP when they need more information about a product or service, or when they have a problem but aren't sure how to solve it. RFPs are often used for complex projects, and they describe the scope of a project in detail and ask suppliers to come up with different solutions. The goal of an RFP is to find the right partner for the project based on capabilities and compatibility, not just cost. +- Add references and case studies + [Calligo-Cloud-RFP-Guide.pdf](https://www.calligo.io/wp-content/uploads/2023/09/Calligo-Cloud-RFP-Guide.pdf) ### Others diff --git a/docs/management/project-management/pm101.md b/docs/management/project-management/pm101.md index 5d91906a3e8..f8aae154471 100755 --- a/docs/management/project-management/pm101.md +++ b/docs/management/project-management/pm101.md @@ -401,3 +401,7 @@ This process involves identifying people, groups and organizations who will be i - Plan stakeholder engagement - Manage stakeholder engagement - Monitor stakeholder engagement + +## Work Breakdown Structure (WBS) + +[What is Work Breakdown? - HelpDocs & User Setup | LinearB](https://linearb.helpdocs.io/article/tpxx3m8cwb-work-breakdown) diff --git a/docs/media/Course-Time-Series-Analysis_Intro-Time-Series-image4.jpg b/docs/media/Course-Time-Series-Analysis_Intro-Time-Series-image4.jpg deleted file mode 100755 index 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