The following labs are currently available, before attempting any of the labs be sure you have followed the instructions in Lab Setup. When you are done with the labs, be sure to follow the instructions under Cleanup to delete your lab environment and avoid unneeded costs.

Follow the steps in this section to setup your environment to complete the labs.

AdventureWorks is an e-commerce retailer who is looking to improve how they manage the data assets produced by their platform. As a starting point they would like collect their data in a manner that enables easier exploration and prepares the data for downstream analytics processes that can yield new insights. AdventureWorks has asked you to process and prepare their flat file data for weblogs, users, and products into a tabular format that offers better query performance and can be queried using SQL.

In the lab you will learn how to use Spark SQL (and PySpark) to batch process a 10GB text file dataset, quickly explore its content, identify issues with the data, clean and format the data and load it into global tables to support downstream analytics. You will also learn how to automate these steps using Azure Data Factory (ADF), and a Databricks Notebook activity.

AdventureWorks would like to create some visualizations of their data to better understand their customers. They are interested in using the powerful visualization capabilities of Power BI and its ability to allow them to share those visualizations, but aren't sure how they can pull in the data to create the dashboards.

They have provided all the weblogs, users, and product tables that you need to quickly explore the data. You will prepare the data to be used in Power BI and explore the data using Spark SQL and Databricks' built-in visualizations. Finally, you will import the data into Power BI Desktop to create interactive dashboards and reports.

AdventureWorks would like to add a snazzy product recommendations feature to their website and email marketing campaigns that, for every user in their system, can recommend the top 10 products they might be interested in purchasing. AdventureWorks has provided you with the tables for users, products and weblogs that contains all of the data you need.

In this lab, you will train a recommendation model using Spark's built-in collaborative filtering algorithm - Alternating Least Squares (ALS). Then you will use the model to pre-compute the user to product recommendation for every user and save this in a table. Then you will query from this table to quickly get the 10 product recommendations for a given user.

AdventureWorks has asked for the ability to extend their product recommendations feature, integrating the trained Alternating Least Squares (ALS) recommendation model to make predictions against streaming weblog data.

In this lab, you will use Spark Structured Streaming to query the data, and run the streamed data against the ALS recommendation model, getting product recommendations for a given user.

When you are done with the labs be sure to follow these instructions to cleanup your Azure subscription and avoid unnecessary costs.