Implementing Lakeflow Jobs — DP-750 Study Guide

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Included in this chapter:

Trigger	Fires when	Latency	Typical use
Scheduled	A quartz cron time is reached	Exact clock time	Fixed-cadence batch ETL
File arrival	New files land in a UC location	About one minute	Irregular file drops
Table update	A monitored UC table gets new data	After the upstream commit	Chaining to an upstream table
Continuous	The previous run ends (always-on)	Near real time	Always-on streaming
Manual	You click Run now or call the API	On demand	Ad-hoc and external orchestration

Cheat sheet

Each task chooses job compute, serverless, or an existing all-purpose cluster
Job parameters are key-value pairs passed to every task
Set Run as, max concurrent runs, and tags when configuring a job
Tasks can share one job cluster to cut startup cost
A notebook task reads job parameters with dbutils.widgets.get
Lakeflow Jobs supports scheduled, file-arrival, table-update, continuous, and manual triggers
Continuous keeps a job always-on; file arrival launches a run per batch of files
A table-update trigger runs a job when a monitored table changes
File-arrival triggers monitor a Unity Catalog external location or volume
A scheduled trigger uses a quartz cron expression with a timezone
Pause a schedule to stop runs without losing the job definition
Queueing controls what happens when a scheduled run overlaps a running one
Notifications attach at the job or the individual task level
Notifications go to email or admin-configured system destinations
Duration thresholds warn when a run is late or slow
Continuous jobs auto-restart the whole run with exponential backoff
SDP pipelines auto-recover; non-continuous jobs use per-task retries
Restart run cancels the active run and resets the backoff period

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Cheat sheet