Python Data Source API | Learn how to build custom Python Data Sources in Databricks
In this 3-part series, you'll master creating custom data readers for Apache Spark that can ingest data from ANY source.
📹 PART 1: Generating Fake Asset Data using Python Data Source API
In this tutorial, you'll learn:
✅ Understanding PySpark's Python Data Source API
✅ Building custom DataSource and DataSourceReader classes
✅ Implementing partitioning for parallel data generation
✅ Creating realistic fake financial asset data
✅ Registering custom data sources with spark.dataSource.register()
✅ Reading custom formats using spark.read.format()
🎯 What We Build:
A production-ready custom data source that generates fake asset portfolio data including:
Asset IDs and Names (Apple Inc, Microsoft, Real Estate, etc.)
Asset Classes (Equity, Fixed Income, Real Estate, Alternatives)
Financial Metrics (BMV, EMV, Cash Flow, Total Return %)
Geographic Regions and Currencies
⏱️ Timestamps:
00:00 - Introduction to Python Data Source API
01:56 - Databricks Demo
04:10 - Building the Asset Data Generator using Faker
05:32 - Implementing Partitioning Strategy
📚 Resources:
Databricks Documentation: docs.databricks.com
PySpark Data Source API Docs: spark.apache.org/docs/latest/api/python/tutorial/sql/python_data_source.html
🔔 UPCOMING VIDEOS:
Part 2: Creating Custom Data Sources from REST API
Part 3: Reading PDFs with Custom Data Sources and LLM Integration for AI-Powered Summarization
💡 Perfect for:
Data Engineers
PySpark developers
Anyone building data pipelines
ETL/ELT professionals
#databricks #pyspark #dataengineering #python #apachespark #bigdata #tutorial
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