Managing a Data Lake on AWS
summary
Describes a serverless AWS data lake built with Kinesis Data Streams + Firehose ingestion, S3 storage with Bronze/Silver/Gold zones, Glue/Apache Spark for transformation into columnar formats like Parquet, and Athena/Trino as the SQL query layer feeding QuickSight. Notes IDC’s 10x every‑5‑years growth to justify purpose‑built tooling, explains partitioning strategy, column‑ and row‑level access via a metadata catalog, asynchronous query pattern (API Gateway → queue → Lambda → temp S3 → notification) and cost levers like S3 storage classes.
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chapters (18)
- 0:00Introduction to data lakes
- 3:40Building blocks of a data lake
- 5:44Data stream ingestion explained
- 9:14Storing data in S3 buckets
- 10:59Transforming data for analytics
- 13:03Optimizing storage formats
- 16:56Three-tier data lake architecture
- 19:06Metadata catalog and query layer
- 23:48Securing the data lake
- 26:38Consuming data with BI tools
- 28:45Asynchronous data access patterns
- 31:56Machine learning use cases
- 33:44Business intelligence dashboards
- 35:28Value of data-driven decisions
- 36:54Hands-on learning resources
- 39:23Q&A: Data lake vs warehouse
- 40:28Q&A: Cost monitoring tips
- 41:52Q&A: Data duplication prevention
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