Which platform utilizes distributed Spark compute for data engineering and data science?

Azure Databricks is the platform that utilizes distributed Spark compute for data engineering and data science. It is a collaborative environment built on top of Apache Spark, which allows users to process large volumes of data quickly and efficiently by leveraging distributed computing. Databricks provides a unified analytics platform that integrates with Azure services, making it easier for data professionals to build, train, and deploy machine learning models while handling big data workloads.

The power of distributed computing in Azure Databricks enables users to scale their data processing dynamically, which is essential for tasks involving large datasets or complex data engineering workflows. The platform supports a variety of languages, such as Python, R, SQL, and Scala, allowing data scientists and engineers to work in their preferred environment.

While Azure Machine Learning is a powerful tool for building and deploying machine learning models, it does not specifically focus on distributed Spark compute as its core feature. Azure Functions and Azure Logic Apps, on the other hand, are more oriented toward serverless computing and workflow automation, respectively, rather than big data processing and analytics.