What are the two computational models supported by Azure Synapse Analytics?

Azure Synapse Analytics supports two primary computational models: SQL Pools and Spark Pools. SQL Pools are used for traditional data warehousing activities, allowing users to execute SQL queries over structured data to gain insights and perform analytics. This approach is well-suited for scenarios where relational data management is required, leveraging T-SQL (Transact-SQL) for querying.

On the other hand, Spark Pools provide a framework for big data processing using Apache Spark, which is renowned for its ability to handle large-scale data processing and analytics across diverse data types, including structured, semi-structured, and unstructured data. This model is particularly advantageous for data science and machine learning tasks, as it supports various programming languages, such as Python, Scala, and R, making it flexible for a wide range of analytics options.

Together, these two computational models enable Azure Synapse Analytics to cater to different data processing needs and technologies, allowing organizations to effectively analyze and derive insights from their data regardless of its format.