Deploy workloads using Lakeflow Jobs
Setting up a Lakeflow Job in Azure Databricks follows a sequence of design and configuration steps.
1. Define the workflow goal
First, clarify what the Job is supposed to do. This means identifying the business logic or data process you want to automate: ingesting new data, transforming it, training a model, producing a report, or publishing results to downstream systems.
2. Break the workflow into tasks
Next, decompose that workflow into tasks. A task is a single unit of work, such as running a notebook, executing a Python script, launching a Delta Live Table pipeline, or querying a SQL warehouse. At this stage you also decide how tasks depend on one another—whether they run in sequence, in parallel, or only conditionally.
3. Choose triggers
Decide when and how the Job should run. You can pick a time-based schedule, a file-arrival trigger that responds to new data landing, a continuous trigger for always-on execution, or a manual/external trigger controlled by API calls or upstream systems. The choice depends on the data arrival pattern and business requirements.
4. Configure compute resources
Each task needs compute to run on. Conceptually you choose between serverless jobs compute (easy, managed), classic job clusters (customizable), or SQL warehouses (for SQL tasks). You also decide whether tasks should share compute (reducing startup costs) or run on isolated compute (providing stronger isolation and flexibility).
5. Set operational parameters
To make the Job production-ready, configure cross-cutting behaviors: concurrency limits, retries, timeouts, alerts, and notifications. Add parameters so tasks can be reused in different contexts (e.g., dev, test, prod). Version control integration (Git) and tagging further support maintainability and governance.
6. Monitor and iterate
Once the Job runs, use system tables and run history to track performance, check for failures, and optimize. Conceptually, this step closes the loop: you don’t just “set and forget” a Job, you monitor, tune compute usage, refine triggers, and adjust tasks as requirements evolve.
By following these steps, you can efficiently deploy and manage your data processing and analytical workloads using Lakeflow Jobs, using the platform's capabilities for big data and machine learning projects.