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, Principal Consultant in Advanced Analytics Desert Isle Group

Ginger Grant manages the consultancy Desert Isle Group and shares what she has learned while working with data technology to people around the world. As a Microsoft MVP in Data Platform, Microsoft Certified Trainer and an instructor on DataCamp, she focuses on guiding clients to create solutions using the entire Microsoft Data Stack, which includes SQL Server, Power BI, and Azure Data Cloud components. When not working, she protypes the latest pre-release data technologies, maintains her blog, and spends time on twitter @desertislesql.