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AWS Glue ETL Pipeline

Serverless ETL pipeline using AWS Glue for data transformation and loading with automatic schema discovery.

Medium Complexity

Technologies & Stack

AWS GlueAWS S3AWS RedshiftPythonApache Spark

Pipeline Flow

1

Data Catalog

Automatically discover and catalog data sources

AWS Glue Data CatalogAWS Glue Crawler
2

ETL Job

Transform data using serverless Spark jobs

AWS Glue ETLApache SparkPython
3

Target Storage

Load processed data to data warehouse or data lake

AWS RedshiftAWS S3AWS RDS

Use Cases

Data lake ETL
Data warehouse population
Real-time data processing
Schema evolution
Data migration

Advantages

Serverless and fully managed
Automatic scaling
Built-in data catalog
Integration with AWS services

Challenges

AWS vendor lock-in
Limited customization
Can be expensive
Debugging challenges

When to Use This Architecture

AWS-based data infrastructure
Serverless architecture preference
Managed ETL requirements
Rapid prototyping

Alternative Solutions

Azure Data FactoryGoogle Cloud DataflowApache Airflow on EKSSelf-hosted solutions

Performance Metrics

Latency
Medium (minutes to hours)
Throughput
High (scales automatically)
Scalability
Excellent
Reliability
High
Cost
Medium to High

Key Trade-offs

Cost

Pay-per-use pricing, can be expensive for large datasets

Scalability

Automatic scaling based on data volume and complexity

Vendor Lock-in

Tightly coupled to AWS ecosystem

Architecture Category

Cloud-Native

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