[docs] [revamp] - Seed Concept pages

docs/docs-next/docs/concepts/assets/asset-checks.md

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+---
+title: "Asset checks"
+sidebar_position: 7
+---
+
+# Asset checks

docs/docs-next/docs/concepts/assets/asset-dependencies.md

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+---
+title: "Asset dependencies"
+sidebar_position: 3
+---
+
+# Asset dependencies

docs/docs-next/docs/concepts/assets/asset-materialization.md

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+---
+title: "Asset materialization"
+sidebar_position: 2
+---
+
+# Asset materialization

docs/docs-next/docs/concepts/assets/asset-metadata.md

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+---
+title: "Asset metadata"
+sidebar_position: 4
+---
+
+# Asset metadata

docs/docs-next/docs/concepts/assets/thinking-in-assets.md

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+---
+title: "Thinking in assets"
+sidebar_position: 1
+---
+
+# Thinking in assets

docs/docs-next/docs/concepts/automation.md

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+# Automation

docs/docs-next/docs/concepts/automation/declarative-automation.md

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+---
+title: "Declarative Automation"
+sidebar_position: 1
+---
+
+# Declarative Automation

docs/docs-next/docs/concepts/automation/schedules.md

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+---
+title: "Schedules"
+sidebar_position: 1
+---
+
+# Schedules

docs/docs-next/docs/concepts/automation/sensors.md

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+---
+title: "Sensors"
+sidebar_position: 2
+---
+
+# Sensors

docs/docs-next/docs/concepts/execution.md

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+# Execution

docs/docs-next/docs/concepts/execution/dagster-daemon.md

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+---
+title: "Dagster daemon"
+sidebar_position: 1
+---
+
+# Dagster daemon

docs/docs-next/docs/concepts/execution/run-coordinators.md

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+---
+title: "Run coordinators"
+sidebar_position: 4
+---
+
+# Run coordinators

docs/docs-next/docs/concepts/execution/run-executors.md

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+---
+title: "Run executors"
+sidebar_position: 3
+---
+
+# Run executors

docs/docs-next/docs/concepts/execution/run-launchers.md

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+---
+title: "Run launchers"
+sidebar_position: 2
+---
+
+# Run launchers

docs/docs-next/docs/concepts/io-managers.md

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+---
+title: "I/O managers"
+---
+
+# I/O managers

docs/docs-next/docs/concepts/ops-jobs.md

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+# Ops and jobs

docs/docs-next/docs/concepts/ops-jobs/job-configuration.md

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+---
+title: "Job configuration"
+sidebar_position: 1
+---
+
+# Job configuration

docs/docs-next/docs/concepts/ops-jobs/ops-vs-assets.md

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+---
+title: "Ops vs. assets"
+sidebar_position: 1
+---
+
+# Ops vs. assets

docs/docs-next/docs/concepts/partitions.md

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+---
+title: "Partitions"
+---
+
+# Partitions

docs/docs-next/docs/concepts/resources.md

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+# Resources

docs/docs-next/docs/tutorial/quick-start.md

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----
-title: Quickstart
-description: Learn how to quickly get up and running with Dagster
-last_update: 
-    date: 2024-08-10
-    author: Pedram Navid
----
 
-# Dagster Tutorial: Building Your First Dagster Project
 
-Welcome to this hands-on tutorial where you'll learn how to build a basic Extract, Transform, Load (ETL) pipeline using Dagster. By the end of this tutorial, you'll have created a functional pipeline that extracts data from a CSV file and transforms it.
-
-## What You'll Learn
-
-- How to set up a basic Dagster project
-- How to create Software-Defined Assets (SDAs) for each step of the ETL process
-- How to use Dagster's built-in features to monitor and execute your pipeline
-
-## Prerequisites
-
-- Basic Python knowledge
-- Python 3.7+ installed on your system, see [installation guide](tutorial/installation.md) for more details
-
-## Step 1: Set Up Your Dagster Environment
-
-First, set up a new Dagster project.
-
-1. Open your terminal and create a new directory for your project:
-
-   ```bash title="Create a new directory"
-   mkdir dagster-quickstart
-   cd dagster-quickstart
-   ```
-
-2. Create a virtual environment and activate it:
-
-   ```bash title="Create a virtual environment"
-   python -m venv venv
-   source venv/bin/activate  
-   # On Windows, use `venv\Scripts\activate`
-   ```
-
-3. Install Dagster and the required dependencies:
-
-   ```bash title="Install Dagster and dependencies"
-   pip install dagster dagster-webserver pandas
-   ```
-
-## Step 2: Create Your Dagster Project Structure
-
-Set up a basic project structure:
-
-:::warning
-
-The file structure here is simplified to get quickly started. 
-
-Once you've completed this tutorial, consider the [ETL Pipeline Tutorial](/tutorial/tutorial-etl) to learn 
-how to build more complex pipelines with best practices.
-
-:::
-
-1. Create the following files and directories:
-
-   ```bash title="Project structure"
-   dagster-quickstart/
-   ├── quickstart/
-   │   ├── __init__.py
-   │   └── assets.py
-   ├── data/
-       └── sample_data.csv
-   ```
-
-   ```bash title="Create the project structure"
-   mkdir quickstart data
-   touch quickstart/__init__.py quickstart/assets.py
-   touch data/sample_data.csv
-   ```
-
-
-
-2. Create a sample CSV file as a data source. In the `data/sample_data.csv` file, add the following content:
-
-   ```csv
-   id,name,age,city
-   1,Alice,28,New York
-   2,Bob,35,San Francisco
-   3,Charlie,42,Chicago
-   4,Diana,31,Los Angeles
-   ```
-
-## Step 3: Define Your Assets
-
-Now, create the assets for the ETL pipeline. Open `quickstart/assets.py` and add the following code:
-
-```python
-import pandas as pd
-from dagster import asset, Definitions
-
-@asset
-def processed_data():
-    df = pd.read_csv("data/sample_data.csv")
-    df['age_group'] = pd.cut(df['age'], bins=[0, 30, 40, 100], labels=['Young', 'Middle', 'Senior'])
-    df.to_csv("data/processed_data.csv", index=False)
-    return "Data loaded successfully"
-
-defs = Definitions(assets=[processed_data])
-```
-
-This code defines a single data asset within a single computation that performs three steps:
-- Reads data from the CSV file
-- Adds an `age_group` column based on the `age`
-- Saves the processed data to a CSV file
-
-If you are used to task-based orchestrations, this might feel a bit different. 
-In traditional task-based orchestrations, you would have three separate steps,
-but in Dagster, you model your pipelines using assets as the fundamental building block,
-rather than tasks.
-
-The `Definitions` object serves as the central configuration point for a Dagster project. In this code, a `Definitions` 
-object is defined and the asset is passed to it. This tells Dagster about the assets that make up the ETL pipeline 
-and allows Dagster to manage their execution and dependencies.
-
-## Step 4: Run Your Pipeline
-
-:::warning
-
-There should be screenshots here!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-
-:::
-
-1. In the terminal, navigate to your project root directory and run:
-
-   ```bash
-   dagster dev -f quickstart/assets.py
-   ```
-
-2. Open your web browser and go to `http://localhost:3000`
-
-3. You should see the Dagster UI along with the asset. 
-
-3. Click Materialize All to run the pipeline.
-
-4. In the popup that appears, click View to view a run as it executes.
-
-5. Watch as Dagster executes your pipeline. Try different views by selecting the different view buttons in the top-left.
-You can click on each asset to see its logs and metadata.
-
-## Step 5: Verify Your Results
-
-To verify that your pipeline worked correctly:
-
-1. In your terminal, run:
-
-   ```bash
-   cat data/processed_data.csv
-   ```
-
-You should see your transformed data, including the new `age_group` column.
-
-## What You've Learned
-
-Congratulations! You've just built and run your first pipeline with Dagster. You've learned how to:
-
-- Set up a Dagster project
-- Define Software-Defined Assets for each step of your pipeline
-- Use Dagster's UI to run and monitor your pipeline
-
-## Next Steps
-
-- Continue with the [ETL Pipeline Tutorial](/tutorial/tutorial-etl) to learn how to build a more complex ETL pipeline
-- Learn how to [Think in Assets](/concepts/thinking-in-assets)
+# Quickstart