Build large-scale transactional data lakes with open table formats (ANT336)
Transactional Data Lakes: Unlocking the Power of Open Table Formats
Key Takeaways
History and Challenges of Data Lakes
The evolution of data storage and processing, from relational databases to data warehouses to Hadoop-based data lakes.
Limitations of traditional data lakes: lack of consistency, integrity, performance, and governance.
The rise of cloud-based data architectures like the lakehouse, combining the benefits of data lakes and data warehouses.
Transactional Data Lakes and Open Table Formats
Introducing transactional data lakes, powered by open table formats like Apache Hudi, Apache Iceberg, and Delta Lake.
Key use cases for transactional data lakes: streaming ingestion, data privacy compliance, and supporting new AI/ML applications.
Customer examples: Zoom and ARA Security leveraging open table formats for their data lake needs.
Diving into Open Table Formats
Architectural components of open table formats, and how they fit between the catalog and file storage layers.
Key features of Apache Iceberg: ACID transactions, schema evolution, partition evolution, data integrity, and performance optimizations.
AWS's support and prioritization of Iceberg across its analytics services.
Optimizing Transactional Data Lakes with AWS
Performance improvements in EMR and Glue, including Shuffle reduction, redundant scan elimination, and adaptive optimizations.
Fine-grained access controls with S3 access grants and Glue Data Catalog views.
Interoperability with AWS analytics services, including SageMaker Lakehouse and Firehose support for Iceberg.
Upcoming open-source projects like xtable and Delta Unified for cross-format interoperability.
Conclusion
AWS provides a comprehensive set of services and features to support transactional data lakes, with flexibility, cost-effectiveness, security, and no vendor lock-in.
AWS is actively contributing to the open-source Iceberg community and prioritizing its features across its analytics services.
Customers are encouraged to share their transactional data lake experiences and journey with AWS.
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