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Databricks project originally initiated by UC Berkeley and then latter known as Apache Spark. It provides all analytics and Big Data processing service. Currently it is part of Microsoft and named as Microsoft Azure Databricks .  We are using Databricks in Azure platform as  a single platform for Big Data processing and Machine Learning. It provides data science and data engineering teams with a fast, easy and collaborative Spark-based platform on Azure.  In this post, I'll walk through how you can connect with Azure storage, read your files and write them in your Azure SQL. Basic Data Engineering steps. Implementing machine learning inelegancy or drill down level data aggregation is out of scope. Let's start by creating Databricks first. To create Databricks, search Azure Databricks in your Azure portal and click on it to create. Once created, you can see this at your Azure portal. Click on it to go to this resource home page. At the home page, click on Launch Workspace bu

In this post, I'll walk through creation of Azure Synapse Analytics(workspace preview) through Azure UI. And here is my earlier post link where I walked through the Azure Synapse Analytics(formerly SQL DW) . Okay, lets start on the creation process. Search 'Azure Synapse' on creation search box and then click on Azure Synapse Analytics(workspace preview) . And then follow the steps and click on create. Once the Synapse Analytics gets created, go to the resource and land on the home overview page. This is looks like below. Overview page holds all major information. Note, SQL Server and default DB creation is part of the creation process. So, you will have the information about DB server and other details. Click on Open under 'Open Synapse Studio' box land you on the Synapse workspace.  This could be pretty familiar t you if you worked with Azure Data Factory. On the extreme left navigation, you have  Home ,  DB, Pipeline ,  Monitoring  etc icon which are same as Azu