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ValidatedAWS Competency

Data & Analytics Competency

Validated for warehouses, streaming pipelines, and managed ETL that stay operable after the engineering team leaves.

Status
Validated
Reviewed
January 2026
Renews
January 2028
References
2 submitted

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.

Data & Analytics validation required evidence of production pipelines under real load, plus demonstrated client adoption of the resulting platform.

How validation works

Production pipelines

Ingest and transformation running at volume with monitoring in place.

Met

Modelling review

Warehouse schemas examined against query patterns and governance needs.

Met

Adoption evidence

Named business users querying the platform without engineering support.

Met

Cost modelling

Warehouse run cost projected and validated against actual spend.

Met

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.