Glossary Library

Technical GlossaryData Engineering and AI Infrastructure

Idempotency

The property of producing a stable, non-duplicated result even when the same operation is run multiple times.

Idempotency is essential for reliable reruns in data pipelines. When a job fails and is triggered again, the system must not create duplicate records or corrupt the data. This becomes especially critical in distributed systems, batch backfills, and stream redelivery scenarios. Non-idempotent data flows often create silent but destructive quality problems.

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Explore these concepts to continue your artificial intelligence journey.

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Change Data Capture

An approach for tracking data changes in source systems and propagating them to downstream systems in near real time.

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Data Collection SLA

An operational service-level framework that defines timeliness, completeness, and availability standards for data flows.

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Data Observability

A monitoring approach that aims to detect data issues, anomalies, and silent quality degradation early.