Azure Databricks Workspace

How is Azure Databricks different from Apache Spark?

A. While Azure Databricks is built on Apache Spark, it includes several enhancements for performance, security, and usability, including a fully managed service, automated scaling, and an optimized runtime.

What Databricks service of Azure offers on top of the Apache Spark?

A. Azure Databricks service offers some advanced features on top of the Spark platform including

• Secure cloud storage integration
• ACID transaction via Delta Lake integration
• Unity catalog for metadata management
• Cluster management
• Photon query engine
• Notebooks and workspaces
• Administration controls
• Optimized Spark Runtime
• Automation tools

What languages can I use in Azure Databricks notebooks?

A. Azure Databricks supports multiple languages, including Python, R, Scala, SQL, and Java. You can use any of these languages in a single notebook by specifying the language at the beginning of a cell.

How do I ingest data into Azure Databricks?

A. Data can be ingested into Azure Databricks using different methods such as Azure Data Factory, directly from Azure storage accounts, or using built-in connectors and APIs to load data from external sources.

Imagine you are working in a sales department of a retail company. You need to analyze the data of the customers and sales to gain insights into customer behavior, product performance, and sales trends. But there is a huge amount of data to analyze, and we need such a tool that helps analyze large data and get trends of it. Microsoft Azure provides such a service which helps in processing big data and analytics purposes, Azure Databricks. This article, let us understand more about Azure data bricks and creating a Databricks workspace in Azure.