software-engineering-eng-data

Designing a data pipeline with quality gates requires mapping the lifecycle from generation to serving, defining quality test dimensions like accuracy and completeness, and setting operational thresholds with monitoring for freshness, volume, and distribution.

Choosing between batch and streaming ingestion involves using a decision matrix based on latency requirements and applying streaming semantics like idempotency or exactly-once strategies to select the right architecture for your data pipeline.

Data contracts prevent breaking downstream consumers by enforcing backward-compatible schema evolution protocols and defining clear expectations for semantics, SLOs, and ownership across the data pipeline lifecycle.

Data quality dimensions to test in an ELT pipeline include accuracy, completeness, consistency, timeliness, uniqueness, and validity, which must be translated into concrete checks with defined operational thresholds.

Setting up observability SLOs for data pipelines involves specifying monitoring metrics across freshness, volume, schema changes, data distribution, and lineage to ensure reliable transformation and serving stages.

Planning schema migrations for analytics or ML serving without disruption requires establishing backward-compatible schema change protocols and data contracts that define ownership and SLOs to protect downstream consumers.