- A serverless resource model and a dedicated resource model
- It provides predictable performance and cost workloads
- Used with unplanned or ad-hoc workloads, to handle the descriptive and diagnostic analytical options
- and accelerate the time to insight across data warehouses and big data systems.
- Allow users to create data-driven workflows to orchestrate data movement and transform data at scale.
Technologies in an Integrated Console
- SQL Technologies: Integrated analytics to implement enterprise data warehousing solutions using the standard T-SQL language queries, dedicated SQL pool relational tables, and columnar storage to reduce the cost of storing the huge data.
- It performs the complex analysis, and the Synapse dedicated SQL pool will use the PolyBase feature to query the big data stores. Thus, improves data retrieval performance, allowing a user to run a massive analytics scale that returns the results in a few seconds.
- The Spark technologies used for big data solutions
- The pipelines capabilities for ETL/ ELT data operations
- Azure services such as Power BI, Azure CosmosDB, Azure Machine Learning
- Azure Data Catalog, Azure Data Lake Storage, Azure Databricks, Azure HDInsight. For the data stored on Azure Data Lake storage Hadoop, Spark, and machine learning algorithms will be used to prepare and train that data.
- It supports different languages such as SQL, Python, .NET, Java, Scala, and R.