AWS Glue - ETL Service
Description
Comprehensive understanding of AWS Glue for serverless ETL operations, including data catalog, crawlers, jobs, and workflows.
Learning Objectives
Learners will master AWS Glue for serverless ETL processing, create and manage data catalogs, develop ETL jobs using both visual and code-based approaches, implement data quality checks, and orchestrate complex data workflows.
Topics (9)
Overview of AWS Glue ecosystem, serverless architecture, integration with other AWS services, and service limitations.
Data catalog concepts, table definitions, schema management, partitioning, and metadata best practices.
Crawler configuration, scheduling, schema evolution handling, and optimization strategies for various data sources.
ETL job creation, PySpark/Scala development, job parameters, error handling, and optimization techniques.
Visual ETL development, drag-and-drop interface, pre-built transformations, and code generation capabilities.
Workflow design, trigger configuration, job dependencies, error handling, and integration with other orchestration tools.
Data quality rules, monitoring and alerting, job metrics, CloudWatch integration, and troubleshooting techniques.
Performance tuning, resource allocation, partition optimization, job bookmarking, and cost optimization strategies.
DataBrew interface, data profiling, recipe development, data quality assessment, and integration with ETL workflows.