Study Notes 2.2.1: Workflow Orchestration with Kestra

Introduction to Workflow Orchestration

Key Concepts

Orchestration Analogy:

Orchestration is compared to a musical orchestra.
Different instruments (like violins, trumpets, flutes) produce different sounds and need to be playe…


This content originally appeared on DEV Community and was authored by Pizofreude

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).

Image description

  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.


This content originally appeared on DEV Community and was authored by Pizofreude


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