Putting It All Together

1. Putting it all together

In practice, lakehouse concepts don't exist in isolation. Let's walk through a real scenario that ties them all together.

2. The scenario

Imagine you've just joined a retail company's data team. There's an existing medallion pipeline that processes daily sales data. Your job is to verify the setup: pick the right compute, confirm the data flows correctly through bronze, silver, and gold, make sure governance is in place, and prepare the project for automated deployment. This is a realistic first week for a data engineer on any Databricks team.

3. Step 1: Choose the right compute

First, compute. The pipeline runs on a schedule every night - there's no one sitting at a notebook interacting with it. That means a jobs cluster is the right choice. You'd configure it with the latest LTS runtime for stability, enable autoscaling so it handles volume spikes without over-provisioning, and let it auto-terminate when the job finishes. This is Chapter 2 in action - matching the cluster type to the workload.

4. Step 2: Verify the medallion pipeline

Next, verify the data. You query the bronze table and see raw records - over a million rows with some nulls and duplicates. The silver table is cleaner - nulls removed, types enforced, fewer rows. The gold table aggregates everything into daily revenue by region - just 365 rows for the year. This is the medallion architecture from Chapter 1, and you can confirm it's working by tracing the data quality progression.

5. Step 3: Check governance

Then you check governance. In Unity Catalog, you pull up the gold table and trace its lineage - confirming it reads from silver_sales, which reads from bronze_sales. You verify that only the analytics team has SELECT access to the gold table, and that the external reporting partner has a Delta Share configured. This is Chapter 3 - Unity Catalog for lineage and access control, Delta Sharing for external access. Everything checks out.

6. Step 4: Prepare for deployment

Finally, deployment. You open the Asset Bundle and review the databricks-dot-yml file. The nightly ETL job is defined as a resource with a 3 AM cron schedule. The production target points to the shared production folder. You run bundle validate to catch any errors, and once it passes, the bundle is ready for CI/CD deployment. This is Chapter 4 - infrastructure as code, ready to promote from dev to production with a single command.

7. Summary

That's the full picture. Choose the right compute for the workload. Verify your data flows through the medallion layers correctly. Confirm governance is in place with Unity Catalog and Delta Sharing. And prepare your deployment with an Asset Bundle. These four steps are what a data engineer actually does on a Databricks team, and now you have the knowledge to do each one confidently. One last exercise to go, and then we'll wrap up.

8. Let's practice!

Time for the capstone. You'll work through a multi-step scenario that integrates everything from this course.

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