Airflow & Dask
Overview
This section covers Apache Airflow and Dask for massively parallel data processing workflows in the Sravz platform. Learn how to build scalable data pipelines for processing thousands of stock tickers, fundamentals, and historical quotes.
What You’ll Learn
- Apache Airflow: Workflow orchestration and DAG management
- Dask: Distributed parallel computing for financial data
- Task Mapping: Dynamic task generation for scalable workflows
- Data Pipeline Optimization: Processing 5000+ tickers efficiently
- Airflow 3 Upgrade: Migration from Airflow 2 to 3
Use Cases
Stock Data Processing
- Generate statistics for 5200+ stock tickers in parallel
- Upload ticker fundamentals to S3/MongoDB
- Process historical quotes for ETFs and mutual funds
Workflow Orchestration
- Schedule daily data updates
- Coordinate multi-stage data pipelines
- Handle failures and retries gracefully
Documentation Index
- Stock Quotes Stats Generation - Airflow + Dask massively parallel 5200 stock quotes stats generation
- Ticker Fundamentals Uploader - Airflow task mapping for ticker fundamentals upload
- ETF Tickers Uploader - Get list of US ETF tickers using task mapping
- ETF Historical Quotes Uploader - Upload historical ETF quotes to S3
- Mutual Funds Uploader - Upload mutual funds fundamentals using task mapping
- Airflow 2 to 3 Upgrade - Migration guide from Airflow 2 to Airflow 3
Technologies
- Apache Airflow 3.x: Workflow orchestration
- Dask: Distributed parallel computing
- Python: Data processing scripts
- S3: Data storage
- MongoDB: Metadata storage
Quick Start
Start with the Stock Quotes Stats Generation to see Airflow and Dask in action processing thousands of tickers in parallel.
Related: Python | Backend Services