Learn the collocations for orchestrating pipelines, monitoring quality, backfilling data, and handling failures.
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The data platform team used Apache Airflow to ___ pipelines and manage dependencies between jobs.
Orchestrate pipelines is the standard data engineering collocation for coordinating the scheduling and execution of dependent data transformation steps. 'Manage along' and 'control out' are informal. 'Run around' does not capture the dependency management aspect of orchestration.
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The team built Great Expectations checks to ___ quality at every stage of the ingestion pipeline.
Monitor quality is the standard data engineering collocation for continuously observing data quality metrics and alerting on anomalies. 'Check along' and 'watch around' are informal. 'Track out' is not a standard phrase in a data quality context.
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When the historical data was missing from the warehouse, the team had to ___ data for the past 90 days.
Backfill data is the standard data engineering collocation for reprocessing historical data to populate a dataset that was missing or incorrect. 'Reload along' and 'reprocess around' are informal. 'Replay out' is not standard.
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The pipeline included retry logic and dead-letter queues to ___ failures gracefully without data loss.
Handle failures is the standard data pipeline collocation for managing errors and exceptions in a pipeline in a controlled way. 'Manage along' and 'deal around' are informal. 'Process out' does not convey error handling semantics.
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After the upstream schema changed, the team had to ___ downstream dependencies to prevent broken jobs.
Audit downstream dependencies is the standard data engineering collocation for systematically reviewing the impact of schema or pipeline changes on downstream consumers. 'Check along' and 'review around' are informal. 'Inspect out' is not standard.