An Introduction to Azure Databricks

Take a look at how Azure Databricks is making it easier to execute AI in the cloud

Recorded April 2018
Azure Databricks is an exciting new service in Azure for AI, data engineering, and data science. Built on Apache Spark, Azure Databricks is capable of processing and modeling data of all sizes and shapes, and it integrates seamlessly with Azure services. It also provides a collaborative environment where users can work together in a secure, interactive workspace. This unified platform can make it easier and provide a faster time-to-market for AI solutions built on Azure

In BlueGranite's recent webinar, we gave a brief overview of Azure Databricks and explored where and how it fits into the Azure Data Platform. Our experts led a short demonstration of the core functionality within Azure Databricks, along with a quick look at the Azure Databricks workspace, connecting to and loading data from Azure Data sources, exploring, transforming, and modeling data in Databricks notebooks, and visualizing data with Power BI. Check out the recording below!


For more information on how BlueGranite can help your organization take full advantage of Azure Databricks, check out our workshop and proof of concept offerings.

 Webinar GOALS

  • Explore and learn about the capabilities of the Azure Databricks environment.
  • Gain an understanding of how Azure Databricks fits into the existing Azure Data Platform and what that means for your organization.
  • Get a first-hand look at Azure Databricks' fast, secure, and collaborative workspace.
  • Learn about Azure Databricks' powerful impact to AI, data engineering, and data science.


  • Hosted live & recorded via GoToWebinar
  • Content is intended for data scientists, data engineers, data analysts, and anyone looking to learn more about Azure Databricks


Mike CornellBG_blogbio_MikeCornell.png
Mike is a previous BlueGranite employee. His specializations include big data platforms, cloud data platforms, advanced analytics, and data visualization and exploration. Mike's technology interests include the Azure Data Platform, Databricks, Hadoop Data Platform, Spark, R and Python for data analysis, Power BI, and SQL Server. Check out Mike's personal blog here.

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