1. Course recap
Hello there, and welcome back. We are now at the end of the Introduction to Databricks SQL course! We covered a lot throughout the course, so let us review some of the highlights of the course.
2. SQL in the lakehouse architecture
To begin this course, we learned how the Databricks Data Intelligence Platform spans across many languages, with SQL being one of them. Databricks SQL is based on the open ANSI standard, and provides a SQL-native environment to do any workload a user might want to deliver.
3. Databricks SQL for data engineering
We then dove into data engineering tasks within Databricks SQL. We learned how to ingest data with tools like Auto Loader or directly from files. We then could build our production quality data pipelines with all the transformations and data optimizations that we need to create our Medallion Architecture, from Bronze all the way to Gold.
We also learned some more advanced techniques for data engineering, such as how to satisfy CDC data pipeline requirements using the MERGE statement.
4. Databricks SQL for data analysis
Finally, we ended our course by learning how to use Databricks SQL for data analysis. We learned that the Databricks platform provides all the necessary tools to create queries, visualizations, and dashboards directly in the platform. We also learned how Partner Connect allows us to use our favorite BI platform with Databricks underneath.
We also learned that Databricks SQL can handle our more complex analytical applications, such as using sub-queries and window functions.
5. Thank you!
And with that, we have officially reached the end of the Introduction to Databricks SQL course. Thank you for taking the time to learn with me, and I hope that you have taken something away from this course. Please continue to check out the other courses that DataCamp has to offer!