To run these exercises, follow each instructions below in this readme.

1. Storage Settings
2. Basics of PySpark, Spark Dataframe, and Spark Machine Learning
3. Spark Machine Learning Pipeline
4. Hyper-parameter Tuning
5. MLeap (requires ML runtime)
6. Spark PyTorch Distributor (requires ML runtime)
7. Structured Streaming (Basic)
8. Structured Streaming with Azure Event Hubs or Kafka
9. Delta Lake
10. MLflow (requires ML runtime)
11. Orchestration with Azure Data Services
12. Delta Live Tables
13. Databricks SQL

• Create Azure Databricks resource in Microsoft Azure.
  When you create a resource, please select Premium plan.
• After the resource is created, launch Databricks workspace UI by clicking "Launch Workspace".
• Create a compute (cluster) in Databricks UI. (Select "Compute" menu and proceed to create.)
  Please select runtime in ML (not a standard runtime).
• Clone this repository by running the following command. (Or download HandsOn.dbc.)
  git clone https://github.com/tsmatz/azure-databricks-exercise
• Import HandsOn.dbc into your Databricks workspace as follows.
  • Select "Workspace" in Workspace UI.
  • Go to user folder, click your e-mail (the arrow icon), and then select "import".
  • Pick up HandsOn.dbc.
• Open the imported notebooks and attach above compute (cluster) in every notebooks. (Select compute (cluster) on the top of notebook.)
• Please make sure to run "Exercise 01 : Storage Settings (Prepare)", before running other notebooks.

Note : You cannot use Azure trial (free) subscription, because of the limited quota. When you're in Azure free subscription, please promote to pay-as-you-go. (The credit in free subscription will be reserved, even when you transit to pay-as-you-go.)

Tsuyoshi Matsuzaki @ Microsoft