Which types of processing does the processing layer of a modern data architecture support?

The processing layer of a modern data architecture primarily supports SQL-based processing, which allows for structured querying and manipulation of data stored in databases or data warehouses. This capability is essential for organizations that rely on conducting complex queries, aggregating data, and performing analytical operations to derive insights from their datasets.

SQL-based processing enables data engineers and analysts to interact with data using a widely understood and standardized language, making it easier to manage and analyze large volumes of data effectively. Furthermore, the ability to leverage SQL for processing allows for integration with various tools and services that interact seamlessly with databases.

In contrast, while batch processing and near real-time processing are vital aspects of data architecture, they are generally categorized separately from SQL-based processing. Batch processing refers to the execution of a series of jobs on large volumes of data at once, often on a scheduled basis, while near real-time processing is associated with processing data almost instantly as it arrives. These processing types can indeed use SQL, but the question specifically highlights the support for SQL as a critical function of the processing layer.