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Establishing a Resilient Data Lake Architecture on AWS
Phase 1: Realizing the Problem
- Forever E-commerce, a fictional company, is facing challenges in managing their growing data across multiple sources and destinations.
- The CIO, Marcus Preston, wants to ensure data confidence and reliable insights, while the CDO, Eugene Tate, wants to simply store all the data in Amazon S3.
Phase 2: Rethinking the Data Architecture
- The talk covers two types of data: orders from the web application and clickstream data from the website.
- The data is being collected and stored in various ways, leading to challenges in querying and managing the data.
Phase 3: Designing a Resilient Data Lake Architecture
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Raw Data Layer:
- Data is collected from different sources and stored in Amazon S3 in a structured format.
- Considerations for partition structure and S3 scale to handle high-volume ingestion.
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Processed Data Layer:
- Data is cleaned, transformed, and organized into tables with defined schemas.
- Use of Iceberg table format to decouple data storage from table structure, enabling flexibility and scalability.
- Registering tables in the AWS Glue Data Catalog for easy access and management.
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Curated Data Layer:
- Highly tailored data sets are created to answer specific business questions.
- Curated tables are often loaded into a data warehouse like Amazon Redshift for performant queries.
- Lineage and data quality metrics are important for this layer.
Phase 4: Securing and Governing the Data Lake
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Data Mesh Principles:
- Decentralized data ownership and management, with each domain owning its data as a product.
- Applying fine-grained access controls using AWS Lake Formation.
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AWS Glue Data Catalog, AWS Lake Formation, and AWS Data Zones:
- Glue Data Catalog manages table metadata and schema information.
- Lake Formation provides row-level and column-level access controls.
- Data Zones enables self-service data sharing and governance across the organization.
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Security and Encryption:
- Encrypt data at rest and in transit.
- Implement a flexible and scalable security model with multi-account separation.
Phase 5: Optimizing Data Lake Performance
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Partitioning and File Layout:
- Partitioning data based on commonly queried columns can significantly improve query performance.
- Iceberg table format decouples the physical data layout from the logical table structure, enabling flexible partitioning strategies.
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Compression and Sorting:
- Use efficient compression algorithms like Zstandard to reduce storage costs and improve query performance.
- Sorting data can further improve compression and query performance, especially when combined with Iceberg's hidden partitioning feature.
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Query Engine Optimizations:
- Leverage features in Athena, such as workgroups and cost allocation, to manage and optimize query performance.
- Consider using the right compute platform (e.g., Amazon Redshift) for more complex queries.
Phase 6: Preparing for Sustainable Growth
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Monitoring and Alerting:
- Collect metrics from all components of the data lake, including S3, AWS Glue, Amazon Athena, and Amazon MSK.
- Set up dashboards and alarms to monitor key performance indicators (KPIs) for data quality, availability, cost, and security.
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Automation and Scalability:
- Implement continuous monitoring and auditing using CloudWatch and CloudTrail.
- Automate cost management and scale out the data lake as needed, taking humans out of the loop.
Key Takeaways
- Establish a scalable data lake architecture using Amazon S3 as the foundation.
- Implement efficient data ingestion using services like AWS Glue and streaming solutions.
- Optimize query performance and cost through partitioning, file formats, and sorting strategies.
- Secure access and govern the data lake using AWS Lake Formation and the AWS Glue Data Catalog.
- Employ comprehensive logging and auditing to ensure sustainable growth and governance.