Introduction to Workflow Orchestration

Key Concepts

  1. Orchestration Analogy:
    • Orchestration is compared to a musical orchestra.
    • Different instruments (like violins, trumpets, flutes) produce different sounds and need to be played at specific times.
    • A conductor ensures all instruments play in harmony, similar to how orchestration tools manage different pieces of code.
  2. Kestra Overview:
    • Kestra is an all-in-one automation and orchestration platform.
    • Supports ETL processes, batch data pipelines, and workflow scheduling (routine or event-based).
    • Offers flexibility: no-code, low-code, and full-code options.
    • Allows writing workflows in various languages (Python, Rust, C).

  1. Monitoring and Visualization:
    • Provides a topology view to visualize workflow stages.
    • Gantt view for monitoring task progress and logs.
    • Integrates with over 600 plugins including cloud platforms (AWS, GCP), Databricks, Snowflake, DBT.

Workflow Components

  1. Workflow Definition:
    • ID: Unique identifier for the workflow.
    • Namespace: Organizational unit (like a folder) to store workflows.
  2. User-Defined Parameters (UDPs):
    • Parameters passed at the start of workflow execution.
    • Example: brand and price.
  3. Tasks:
    • Define the actions in the workflow.
    • Can include extracting data from APIs, running Python scripts, transforming data, etc.
    • Tasks can pass data between each other.

Example Workflow

  1. Pipeline Description:
    • Simple pipeline with extract, transform, and query tasks.
  2. Workflow Components:
    • ID: Unique name for the workflow.
    • Namespace: Example Zoom Camp.
    • UDPs: Parameters like brand and price.
  3. Tasks:
    • Extract Task: Fetches data from dummyjson.com.
    • Transform Task: Python script to process and transform the data.
    • Query Task: SQL query to further process the data.
  4. Execution and Monitoring:
    • Gantt View: Visualizes task execution order and status.
    • Logs: Provides insights into task execution.
    • Outputs: Displays generated data like JSON files.

Practical Application

  1. Building ETL Pipelines:
    • Extract data (e.g., New York taxis data).
    • Load into PostgreSQL database.
    • Further load into Google Cloud Storage and BigQuery.
  2. Dynamic Workflows:
    • Use parameters to control workflow execution without modifying the code.
  3. Scheduling and Backfills:
    • Schedule workflows to run automatically.
    • Run backfills to handle missed executions.
  4. Production Deployment:
    • Deploy Kestra on Google Cloud.
    • Use Git for version control and syncing workflows.

Additional Resources

  • YouTube Video Series: Detailed tutorials on Kestra.
  • Documentation: Comprehensive guides and examples.
  • Slack Community: Support and discussion with peers.

Conclusion

  • Kestra is a powerful tool for orchestrating complex workflows and pipelines.
  • Its flexibility and integration capabilities make it suitable for various use cases.
  • The upcoming videos will cover practical applications of Kestra in ETL and cloud operations.

Demo Overview

  1. Pipeline Execution:
    • ID and Namespace: Organize and identify workflows.
    • UDPs: Parameters like brand and price.
    • Tasks: Extract, transform, and query tasks with data passing between them.
  2. Execution Monitoring:
    • Gantt View: Visualize task progress.
    • Logs and Outputs: Inspect task logs and output data.
  3. Practical Example:
    • Extract data, transform it, and query it to produce a useful output.

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