AWS
AWS Data Lake Analytics
This AWS data lake analytics diagram collects application databases and clickstream logs through Kinesis and Glue before storing them in an S3 data lake. The lake supports a Glue Data Catalog, Athena queries, a Redshift warehouse and SageMaker models. Athena and Redshift both feed QuickSight dashboards. It gives a clear service-level view of data movement and consumption without defining table formats, data-retention policies or individual pipeline schedules.
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CASE ANALYSIS
Scenario
Showing how AWS sources become governed analytics products.
Key decisions
- Ingestion: Collect application and clickstream data with Kinesis and Glue.
- Central storage: Land the data in S3.
- Query choices: Support Athena and Redshift consumers.
- Business output: Feed QuickSight dashboards and SageMaker models.
When to reuse this
Use it for a service-level overview of AWS analytics architecture.
FAQ
Frequently asked questions
Why use S3 as a data lake?
S3 provides durable object storage that many AWS analytics services can read from or write to.
What is the Glue Data Catalog?
It stores metadata that helps AWS analytics services discover data assets.
When would a team use Athena and Redshift?
Athena suits queries directly on data in S3, while Redshift supports warehouse-oriented analytical workloads.
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