Deciding between a time-based and a data-driven trigger

Lakeflow Jobs support both scheduled (time-based) and event-driven triggers so pipelines can run either on a fixed cadence or as soon as new data arrives. Choosing the right trigger depends on whether upstream data availability is predictable (schedule) or irregular/dependent on external events (file arrival, another job completion). Data-driven triggers reduce latency and wasted compute by avoiding runs when no new data exists.

Must-know

• Time-based (Scheduled) triggers run a job at a fixed cadence defined by cron syntax, best when data lands predictably (e.g., nightly batch loads).
• File arrival triggers start a job automatically when new files appear in a specified cloud storage location, ideal for irregular or event-driven ingestion patterns.
• Using a file arrival trigger avoids unnecessary runs and reduces latency compared to polling on a fixed schedule when upstream arrival times vary.
• Job (task) dependencies within a workflow (upstream/downstream task ordering) are distinct from triggers: dependencies control execution order within a job, while triggers control when the whole job starts.
• A job can be chained to run after another job completes (job-to-job dependency via trigger), useful when pipeline stages depend on the success of a prior pipeline rather than a clock time.
• Continuous or near-real-time processing needs are typically better served by data-driven triggers or streaming, not tightly-spaced time-based schedules, which waste compute checking for data that isn't there.

Coverage checked against the published exam guide on Jul 26, 2026.

These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.