Introduction and Use cases for working with Azure Databricks

Worried about performance when processing a Data Lake or other large data store? Need to develop in a collaborative environment and visualize the results in Power BI? Azure Databricks, one of the newer components added to Azure, allows users to connect to data sources such as Azure Data Lake, Azure Blob Storage, Azure SQL DW, Cosmos DB, Azure DB, and SQL Server and stream the data using Apache Spark for processing data to create a machine learning [ML] solution and providing the data to Power BI for visualization. Azure Databricks can provide a very quick way of processing data by adding nodes increase performance for tasks, such as analyzing data for a ML solution from an Azure data store. Azure Databricks also includes a collaborative workspace so that using Azure Active Directory, teams of people can create code in a notebook in R or Python and implement the notebook as an Azure Databrick job. The step-by-step demos will include all you need to know to implement Databricks.



Ginger Grant provides consulting services in advanced analytic solutions, including machine learning, data warehousing, data visualization and cube development using the entire Microsoft Data Stack, including SQL Server, T-SQL, SSIS, SSAS, Power BI and Azure. Ginger started working with data to solutions across a wide range of industries including insurance, education, healthcare, finance and transportation. She is a prolific blogger at http://www.desertislesql.com and frequent speaker and as a MCT provides training for training courses, at conferences and events worldwide to introduce more people to current developments and future trends in data. Microsoft has awarded her an MVP in Data Platform since 2016.

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