Connections

Amazon S3 and Redshift

Buckets, Parquet, and Redshift.

Built by Eagle
Website aws.amazon.com
CategoryData platforms
Profiled schema
  • Curated tables and views12,360 rows
  • Column lineage16,480 rows
  • Refresh timestamps20,600 rows
  • Partition and freshness metadata24,720 rows
Proposed treatments
  • Curated tables and viewsLocation0.9
  • Column lineageCustomer0.9
  • Refresh timestampsTime0.7
  • Partition and freshness metadataAP invoice0.7

Overview

Amazon S3 and Redshift reads tables and views from the warehouse or lakehouse so existing curated data can feed the model without a second pipeline.

How it works

Eagle connects with a scoped credential, profiles the selected schemas, and treats each table as a candidate business object. Column-level lineage is captured so the Data Tree can trace a derivation back to the warehouse table it reads.

Configure

  1. 1Create a scoped credential with read access to the schemas Eagle should see.
  2. 2Pick the schemas or tables to profile.
  3. 3Confirm which tables become business objects and which are ignored.

What it brings in

Curated tables and viewsColumn lineageRefresh timestampsPartition and freshness metadata
Method · IAM role