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AWS · data lake · analytics

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.

Typ BlockdiagrammStandard Ogata / standard controls textbook conventionEngine schematex-blockdiagramAktualisiert 24.9.2026
AWS Data Lake Analytics
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Die Anfrage

“Create an AWS data lake analytics diagram with application databases, clickstream logs, Kinesis and Glue, an S3 data lake, Glue Data Catalog, Athena, Redshift, QuickSight, and SageMaker.”

Dann probieren SieAdd an AWS Lake Formation governance layer for the S3 data lake.Add an Amazon EMR transformation job after the data lake.
Was diese Zeichnung zeigt

Die dahinterstehenden Entscheidungen verstehen.

01Ingestion

Collect application and clickstream data with Kinesis and Glue.

02Central storage

Land the data in S3.

03Query choices

Support Athena and Redshift consumers.

04Business output

Feed QuickSight dashboards and SageMaker models.

Use it for a service-level overview of AWS analytics architecture.

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FAQ

Häufige Fragen

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.