What is the purpose of Azure Databricks?

Azure Databricks is designed to provide a collaborative environment for big data analytics. This platform is built on Apache Spark, which makes it particularly well-suited for large-scale data processing and analytics tasks. Its collaborative features allow data scientists, engineers, and business analysts to work together seamlessly in notebooks, share insights, and execute code from different programming languages like Python, Scala, R, or SQL. This fosters teamwork and efficient workflow, making it easier to develop and deploy data models and analytics solutions.

The focus on providing a collaborative workspace aligns with its objectives to enhance productivity and streamline data workflows in organizations. This environment can accommodate different stakeholder roles, allowing for easy sharing of data insights and collaborative programming, which is essential for effective big data analytics.

While the other options address important aspects of data management and analysis, they do not capture the core purpose of Azure Databricks as effectively. Processing relational databases, implementing data governance frameworks, and serving as a data visualization tool are relevant tasks in the data ecosystem but are not the primary functions of Azure Databricks itself. Its strength lies in acting as a unified platform for big data analytics and fostering collaboration among users.