AWS Data Engineering
This page introduces the essential building blocks of data engineering on AWS. It’s structured to help aspiring data engineers understand the key tools, concepts, and patterns that are necessary for designing reliable, scalable, and cost-effective data pipelines.
1. Data Ingestion & Transformation
- Understand streaming and batch ingestion using services like Amazon Kinesis, MSK, and S3.
- Use AWS Glue and AWS Lambda to transform and clean raw data.
- Convert data formats (CSV, Parquet, JSON) and handle schema changes over time.
- Use orchestration tools such as Step Functions, EventBridge, and Apache Airflow (MWAA) for end-to-end workflows.
2. Data Storage & Cataloging
- Select the right data store: Redshift for warehousing, DynamoDB for key-value access, S3 for lake storage.
- Create data catalogs using AWS Glue and automate schema discovery with crawlers.
- Manage hot and cold data using S3 lifecycle rules and Glacier tiering.
- Apply indexing, partitioning, and compression to optimize storage and querying.
3. Data Operations & Monitoring
- Automate data flows with Lambda, Step Functions, and scheduled jobs.
- Monitor data quality and health using CloudWatch Logs and metrics.
- Analyze data with Athena, Redshift, and QuickSight.
- Build dashboards or trigger alerts using SNS and SQS.
4. Data Security & Governance
- Secure data with IAM roles, Secrets Manager, and PrivateLink.
- Use encryption in transit and at rest via AWS KMS.
- Maintain visibility with audit logs from CloudTrail and centralized logging with CloudWatch.
- Handle sensitive data responsibly using Macie and Lake Formation for access controls.
This guide is a living document and will continue to grow with detailed examples, explanations, and visual diagrams. Whether you're studying or exploring the field, this is your launchpad into the AWS data engineering world.