This is the repository for Mastering Data Engineering and Analytics with Databricks, published by Orange AVA™
In today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide.
Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics.
This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals.
Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space.
● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases.
● Optimize query performance and efficiently manage cloud resources for cost-effective data processing.
● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation.
● Build and deploy real-time data processing solutions for timely and actionable insights.
● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale.