1. Wrap Up
Hey there! Thank you so much for taking the time to go through this course. I know we covered a lot, so let's quickly review what we learned.
2. Why the Lakehouse?
To start with, we learned that the lakehouse architecture, created by Databricks, is the combination of a data warehouse and a data lake, combining the best of both worlds.
3. Databricks for data engineering
Next, we learned how Databricks functionality enables data engineers to build efficient and performant data pipelines on their biggest datasets, using tools like Delta Live Tables and Structured Streaming, all built on Apache Spark.
4. Databricks for data warehousing
Then we learned how Databricks provides data warehouse functionality in the lakehouse and enables data analysts to perform their SQL analyses at scale within the lakehouse using a scalable ANSI SQL paradigm, and even building visualizations and dashboards on their datasets.
5. Databricks for machine learning
Finally, we concluded by learning how Databricks can satisfy all of the requirements for end-to-end machine learning development, enabling more high-quality models in production.
6. Congratulations!
Congratulations on completing this course! Now, you have all the knowledge to leverage the Databricks platform for your data workloads fully. If you wish to dive deeper into any of the topics mentioned in this course, there are a variety of other courses on DataCamp that can help, as well as training directly from Databricks. You put in a lot of work and should be proud of that! Now, go solve some of the world's biggest problems!