What is Databricks primarily used for?

Databricks is primarily used for big data processing and machine learning, leveraging its capabilities built on Apache Spark. It enables users to process large datasets efficiently through distributed processing, making it well-suited for handling big data workloads. The platform provides a collaborative environment that allows data engineers, data scientists, and analysts to work together on data projects, facilitating the development and deployment of machine learning models.

In addition to its big data processing capabilities, Databricks integrates various machine learning libraries and tools that streamline the ML workflow, including model training, testing, and deployment. This combination of big data processing with machine learning features supports a variety of use cases, from data transformation to predictive analytics.

While data visualization and reporting, database management, SQL querying, and data warehouse optimization are important aspects in the broader data ecosystem, they do not capture the primary functionalities and unique strengths that Databricks offers in terms of large-scale data processing and advanced analytics capabilities.