ETL Pipeline Patterns

Extract, Transform, Load architectures for scalable data processing

Overview

ETL (Extract, Transform, Load) pipeline patterns provide structured approaches to moving and processing data from source systems to target destinations. These patterns ensure data quality, consistency, and reliability in data-driven applications.

Common ETL Patterns

Batch Processing

Process large volumes of data in scheduled batches, ideal for historical data analysis and reporting.

  • Scheduled data processing
  • High throughput capabilities
  • Cost-effective for large datasets

Real-time Streaming

Continuous data processing for immediate insights and real-time decision making.

  • Low-latency processing
  • Event-driven architecture
  • Immediate data availability

Lambda Architecture

Hybrid approach combining batch and stream processing for comprehensive data handling.

  • Batch and speed layers
  • Fault tolerance
  • Historical and real-time views

AWS ETL Services

Data Integration

  • AWS Glue - Serverless ETL service
  • Data Pipeline - Workflow orchestration
  • Step Functions - State machine coordination

Stream Processing

  • Kinesis Data Streams - Real-time data streaming
  • Kinesis Analytics - Stream analytics
  • Lambda - Event-driven processing

Data Storage

  • S3 - Data lake storage
  • Redshift - Data warehouse
  • DynamoDB - NoSQL database

Best Practices

  • Data Quality: Implement validation and cleansing at each stage
  • Error Handling: Design robust retry and dead letter queue mechanisms
  • Monitoring: Track pipeline performance and data lineage
  • Security: Encrypt data in transit and at rest
  • Scalability: Design for variable data volumes and processing loads

Infrastructure as Code Samples

CloudFormation Templates

AWS Glue ETL Samples

Comprehensive collection of AWS Glue ETL jobs, crawlers, and data catalog configurations

GitHub Repository

Kinesis Analytics ETL Patterns

Real-time ETL patterns using Kinesis Data Analytics with SQL and Apache Flink applications

GitHub Repository

Data Lake ETL Solution

Complete data lake architecture with ETL pipelines, data governance, and analytics capabilities

GitHub Repository

Terraform Modules

Step Functions ETL Orchestration

Step Functions workflows for orchestrating complex ETL pipelines with error handling and retries

GitHub Repository

Glue Data Catalog Terraform

AWS Glue Data Catalog setup with databases, tables, and crawler configurations

GitHub Repository

Lambda ETL Functions

Serverless ETL functions using Lambda with event triggers and data processing capabilities

GitHub Repository

AWS Whitepapers & Documentation

Building Data Lakes on AWS

Comprehensive guide to building data lakes with ETL patterns, governance, and analytics on AWS

AWS Whitepaper

AWS Analytics and Data Lakes

Platform overview of AWS analytics services for building modern ETL and data processing pipelines

AWS Platform

AWS Glue Developer Guide

Complete documentation for AWS Glue ETL service including best practices and advanced patterns

AWS Documentation