Data Platform Practices

Data platforms need product ownership, contracts, and operational controls. Treat datasets, topics, jobs, and dashboards as production interfaces.

Ownership

Every important data asset should identify:

• Business owner and technical owner.
• Producer and consumer teams.
• Data classification and retention requirements.
• Freshness, quality, and availability expectations.
• Backfill and replay responsibilities.

Contracts

Use explicit contracts for events, tables, and APIs.

• Version schemas and validate compatibility in CI.
• Document required fields, optional fields, and semantics.
• Publish ownership and support expectations.
• Avoid breaking consumers silently.
• Track lineage for regulated or high-impact data.

Quality checks

Data quality checks should run where failure is actionable.

Common checks include:

• Freshness and completeness.
• Null, range, and uniqueness constraints.
• Referential integrity.
• Volume anomalies.
• Schema drift.
• Dead-letter and replay rates.

Backfills and replays

Backfills and replays are production changes.

• Require a plan, owner, blast radius, and rollback strategy.
• Test on representative data before full execution.
• Rate-limit when downstream systems can be overwhelmed.
• Record what data changed and why.
• Communicate consumer impact before and after execution.

Retention and deletion

Retention is an architecture decision, not a storage cleanup task. Define deletion behavior, legal hold handling, encryption, and audit requirements before the platform accumulates sensitive data indefinitely.