I’d like to tell you about Azure Databricks. If you don’t know what that is, Azure Databricks provides an end-to-end, managed Apache Spark platform optimized for the cloud. It’s a fast, easy and collaborative analytics platform designed to help bridge the gap between data scientists, data engineers and business decision-makers using the power of Databricks on Azure.

Azure Databricks uses Microsoft Azure Active Directory as its security infrastructure and it’s optimized for ease of use, as well as ease of deployment within Azure. It features optimized connectors to Azure storage platforms (e.g. Data Lake and Blob Storage) for the fastest possible data access, and one-click management directly from the Azure console.

Some key features are:

Auto-scaling – This feature makes scaling much quicker and allows you to scale up or down as you need.

Auto-terminator – Helps you control the costs of your compute time, as well as assist you in preventing cost overruns (a concern for many cloud users).

Notebook Platform – The Notebook Platform supports standard languages (SQL, Python and R for example) and it builds a whole discussion environment around those platforms, enhancing collaboration amongst teams.

Here are some simple steps to get you started:

• First, you’re going to prepare your data by ingesting it from your Azure storage platform, which has native support with Azure Databricks.
• Next, you’re going to do any kind of transformation you need on your ingested data and store it in a Data Warehouse.
• From here, you’ll want to start to perform analytics on your data. These platforms are built for lots of data and you’ll have the capability to explore large data sets in real time, as well as the ability to explore very quickly.
• Lastly, you’re going to display the data. Databricks has native support for tools like Power BI to build your dashboards and analytics models.

So, Azure Databricks provides an end-to-end data solution. You can quickly spin up a cluster or do advanced analytics with this powerful platform. And with it, you can create and monitor robust pipelines that will help you dig deep and better understand your data, allowing you to make better business decisions.