Build a Real-Time CDC Pipeline: DynamoDB → Iceberg with VARIANT Support with EMR Serverless 8.0.0

Описание: 🚀 Build a Production-Ready CDC Pipeline from DynamoDB to Iceberg

Learn how to capture real-time database changes from DynamoDB and load them into Apache Iceberg tables using AWS Lambda, S3, and EMR Serverless. This tutorial demonstrates handling complex nested JSON with Iceberg's VARIANT type (format v3).

📋 What You'll Learn:
Set up DynamoDB Streams for Change Data Capture (CDC)
Build a Lambda function to process and compress CDC events
Create time-partitioned data in S3 with JSON.GZ compression
Write PySpark jobs to MERGE data into Iceberg tables
Handle nested JSON properties using VARIANT data types
Deploy serverless infrastructure with AWS SAM/Serverless Framework
Submit jobs to EMR Serverless for scalable processing

🏗️ Architecture:
DynamoDB Streams → Lambda → S3 (compressed) → Spark → Iceberg v3

💻 Tech Stack:
AWS DynamoDB Streams
AWS Lambda (Python 3.11)
Amazon S3 & S3 Tables
Apache Iceberg v3 with VARIANT support
AWS EMR Serverless (Spark 4.0)
Serverless Framework

🔗 Resources:
https://github.com/soumilshah1995/dyn...

⏱️ Timestamps:
0:00 - Introduction & Architecture Overview
2:15 - DynamoDB Table Setup with Streams
5:30 - Lambda CDC Processor Implementation
10:45 - S3 Partitioning Strategy
13:20 - EMR Serverless Configuration
16:40 - Spark ETL Job with VARIANT Support
22:10 - Testing the Pipeline End-to-End
25:30 - Querying Iceberg Tables
27:45 - Production Considerations & Best Practices

🎯 Use Cases:
✓ Real-time data warehousing
✓ Event sourcing and audit logs
✓ Analytics on operational data
✓ Building data lakes with schema evolution

💡 Pro Tips:
Handles schema evolution automatically
Zero data loss with DynamoDB Streams
Cost-effective with serverless architecture
Scales automatically with EMR Serverless

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#DataEngineering #AWS #ApacheIceberg #CDC #DynamoDB #Serverless #BigData #Spark #DataPipeline #CloudComputing