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In progressAWS Competency

Data & Analytics Competency

Practice area covering warehouses, streaming pipelines, and managed ETL that stay operable after the engineering team leaves.

Status
In progress
Reviewed
In progress
Renews
—
References
Pending approval

What it covers

Modelled from decisions backward.

The common failure is a technically correct warehouse nobody queries. Validation examines modelling discipline, data quality practice, and whether the platform is actually used.

Dimensional modelling

Facts and dimensions designed for the query patterns that follow.

Pipelines

Batch and streaming ingest, orchestrated with dependency-aware retries.

Data quality

Assertions on volume, freshness, and nulls that fail the load, not the report.

Self-serve BI

A semantic layer so two dashboards cannot disagree on a metric.

Validation criteria

What AWS assessed.

The criteria AWS assesses for this competency, and our current status against each. Status is maintained in the CMS from AWS Partner Central.

How validation works

Production pipelines

Ingest and transformation running at volume with monitoring in place.

In progress

Modelling review

Warehouse schemas examined against query patterns and governance needs.

In progress

Adoption evidence

Named business users querying the platform without engineering support.

In progress

Cost modelling

Warehouse run cost projected and validated against actual spend.

In progress

Services

What we build it on.

  • Redshift
  • Kinesis
  • Glue
  • Athena
  • S3
  • QuickSight
  • Step Functions

Team on this practice

Data engineering sits inside the main bench — the same people build venture analytics and client warehouses.

Work with this practice

Building a platform people will query?

Discovery comes first — a short, bounded review that ends in an architecture, a scope, and a price. You keep the output either way.