What type of workload is best suited for Azure Databricks SQL?

The choice of querying and visualizing data in relational tables is particularly well-suited for Azure Databricks SQL because this platform is designed to provide a high-performance SQL analytics service. Azure Databricks SQL allows users to run SQL queries directly against structured data and perform interactive analysis seamlessly.

This service is optimized for high concurrent workloads, making it ideal for business intelligence and analytics scenarios where users need to derive insights from large datasets. Its integration with Delta Lake ensures data reliability and consistency, supporting complex queries and visual representations through tools like dashboards and visualization platforms, such as Power BI.

In contrast, the other options are more aligned with different use cases. Running Scala code in notebooks is primarily focused on data engineering or data science activities rather than direct SQL interactions. Training and deploying machine learning models typically leverages the capabilities of Azure Databricks as a Spark-based engine, where the programming model and libraries emphasize machine learning rather than focusing solely on SQL. Batch processing data streams is associated more with real-time data processing and might involve different tools or frameworks better suited for handling streaming data as opposed to the SQL-centric operations Azure Databricks SQL focuses on.