Understand the key components of Databricks running on Microsoft Azure.
- [Instructor] So the Databricks offering…around Apache Spark has three component areas,…as I mentioned previously.…So let's dive a little bit deeper into them.…So, at the top level, you have the…Databricks collaborative workspace…and that's expressed as Databricks Interactive…where you can work with your cluster…through a Jupyter-style notebook.…So this is a browser-based integration…that we'll be working with very frequently in this course.…I like to think of it as a smart IVE…because it has information about…not only the execution, but also visualizations…and documentation.…
There's also a Databricks production layer at this level…and there are APIs, which you can work with.…The Databricks runtime includes…Apache Spark core, also Databricks I/O,…and now Databricks Serverless,…which we'll be exploring in addition…to running Databricks on more traditional cloud-based VMs.…And surrounding that is Databricks Enterprise Security,…which is more and more an absolute…requirement for many of my customers.…
Now in this course, we're going to look at…
- Business scenarios for Apache Spark
- Setting up a cluster
- Using Python, R, and Scala notebooks
- Scaling Azure Databricks workflows
- Data pipelines with Azure Databricks
- Machine learning architectures
- Using Azure Databricks for data warehousing
Skill Level Intermediate
1. Big Data on Azure Databricks
2. Core Azure Databricks Workloads
Use a notebook with scikit-learn11m 29s
3. Scaling Azure Databricks Workloads
4. Data Pipelines with Azure Databricks
5. Machine Learning Architectures
Next steps1m 1s
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