What keeps table definitions for Athena and Spark in one place?
Why D is the answer
AWS Glue Data Catalog
The Data Catalog is the central metadata store.
In detail
- The Glue Data Catalog holds table definitions for both.
- It is metadata, not data.
Use case
An illustrative scenario, not a real incident.
Athena and Spark both read one table definition from the Glue Data Catalog, so they agree on the schema.
See it in different ways
flowchart LR A["Data in S3"] --> B["Glue crawler"] B --> C["Glue Data Catalog"] C --> D["Athena"] C --> E["Redshift Spectrum"]
mindmap
root((Stores))
Redshift
Complex analytics
DynamoDB
Fast key lookups
S3
Data lake
Athena
SQL on S3mindmap
root(("Glue Data Catalog"))
n1["Readers"]
n2["Amazon Athena"]
n3["Spark"]Key point: The Data Catalog centralizes metadata.
Why the other options are wrong
- A. Amazon DynamoDB
- DynamoDB is not the shared catalog.
- B. AWS Config
- Config tracks resources.
- C. Amazon S3 Inventory
- S3 Inventory lists objects.