Azure training webinar series

Watch the training today!

Azure Databricks is a first-party Microsoft solution that can support the full range of data engineering and data science activities, including data management and transformation, streaming analytics, and machine learning.

In this three-part training series, we’ll teach you how to get started with Azure Databricks, begin to understand its capabilities and how to put it into production in your own infrastructure to run workloads 10-100x faster than non-Databricks platforms, with the security and scale of Azure.

You should consider watching these sessions if you are a data engineer or data scientist interested in learning to use Azure Databricks, and how it can make an impact on your team.

The data engineering session

Learn how to build your own Azure Databricks ETL pipeline, starting with ingestion, moving through transformation, and loading your data into a SQL Data Warehouse. Learn about how easy it is to use Azure Databricks and how you can run workloads up to 10-100x faster than non-Databricks platforms.

The streaming analytics session

In the second training of our Azure Databricks Training series, we’ll teach you how to connect directly to data sources like TCP/IP sockets and the Kafka messaging system, transform and output data, and finally create compelling continuously-updated visualizations to drive greater impact for your teams.

The data science session

In the third of the three-part training series, we’ll show you how you can use MLlib with Azure Databricks to train your own models, run replicable experiments, and deploy into production with fewer failing jobs to accelerate your organization’s data science efforts.

The Azure training sessions to include


  • Batch ingest using Databricks
  • How to transform data using Spark SQL and DataFrames
  • Using the SQL Data Warehouse connector to load data into SQL Data Warehouse
Streaming analytics

  • Connecting to TCIP and Kafka as streaming sources
  • Use the DataFrame API to transform streaming data
  • Output the results to various sinks
  • Use Databricks visualization feature to create a continuously updated visualization of processed streaming data.
Data science

  • Estimators, Transformers and ML Pipelines
  • How to train an ML Model
  • How to save and read an ML Model
  • How to make predictions with an ML Model