What do you understand by Azure Databricks?
Azure Databricks is an Apache Spark-based analytics platform built on Microsoft Azure. Based on Apache Spark, Azure Databricks is used to process large data workloads that allow collaboration between stakeholders to derive actionable insights with a one-click setup, streamlined workflows, and interactive workspace. This unified data and analytics platform is built to enable all data personas: data scientists, data engineers, and data analysts. Typically, it is a managed platform that gives data developers all the tools and infrastructure to focus on data analytics without worrying about managing Databricks clusters, libraries, dependencies, upgrades, and other tasks unrelated to driving insights from data.
How do Azure Databricks work?
Azure Databricks is optimized for Azure and tightly integrated with Azure Data Lake Storage, Azure Data Factory, Azure Machine Learning, Azure Synapse Analytics, Power BI, and other Azure services to store all your data on a simple, open lakehouse and unify all your analytics and AI workloads. Moreover, this Apache-Spark-based platform runs a distributed system behind the scenes. The workload is automatically split across numerous processors and scales up and down on demand—increased efficiency results in direct cost and time savings for massive tasks.
Why do you need Azure Databricks?
To be more crisp and transparent, there are some of the reasons why Azure Databricks is a great analytics platform for your big data workloads.