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Copy file name to clipboardExpand all lines: articles/synapse-analytics/overview-faq.md
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A: Yes, you can use your third-party business applications, like Tableau and Power BI, to connect to SQL pool and SQL on-demand. Spark supports IntelliJ.
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### Q: Does Azure Synapse Analytics provide APIs
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A: Yes, we provide an SDK to programmatically interact with Azure Synapse Analytics. More information is available [here] on which operations are supported by Synapse.
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## Next steps
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-[Create a workspace](quickstart-create-workspace.md)
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---
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title: SQL on-demand (preview)
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description: Learn about Synapse SQL on-demand in Azure Synapse Analytics.
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services: sql-datawarehouse
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author: vvasic-msft
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services: synapse analytics
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author: filippopovic
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ms.service: synapse-analytics
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ms.topic: overview
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ms.date: 04/15/2020
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ms.author: vvasic
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ms.author: fipopovi
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ms.reviewer: jrasnick
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---
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# SQL on-demand (preview) in Azure Synapse Analytics
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# SQL on-demand (preview) in Azure Synapse Analytics
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Every Azure Synapse Analytics workspace (preview) comes with SQL on-demand (preview) endpoints that you can use to query data in the lake.
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SQL on-demand is a query service over the data in your data lake. It enables you to democratize access to all your data by providing a familiar T-SQL syntax to query data in place, without a need to copy or load data into a specialized store. In addition, the T-SQL interface enables seamless connectivity from the widest range of business intelligence and ad-hoc querying tools, as well as the most popular drivers.
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SQL on-demand is a query service over the data in your data lake. It enables you to access your data through the following functionalities:
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- A familiar T-SQL syntax to query data in place without the need to copy or load data into a specialized store.
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- Integrated connectivity via the T-SQL interface that offers a wide range of business intelligence and ad-hoc querying tools, including the most popular drivers.
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SQL on-demand is a distributed data processing system, built for large scale of data and compute, enabling you to analyze your Big Data in seconds to minutes, depending on the workload. Thanks to built-in query execution fault-tolerance, the system provides high reliability and success rates even for long-running queries involving vert large data sets.
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SQL on-demand is a distributed data processing system, built for large scale of data and compute. SQL on-demand enables you to analyze your Big Data in seconds to minutes, depending on the workload. Thanks to built-in query execution fault-tolerance, the system provides high reliability and success rates even for long-running queries involving large data sets.
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SQL on-demand is serverless, hence there is no infrastructure to setup or clusters to maintain. A default endpoint for this service is provided within every Azure Synapse workspace, so you can start querying data as soon as the workspace is created. There is no charge for resources reserved, you are only being charged for the data scanned by queries you run, hence this model is a true pay-per-use model.
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If you use Spark in your data pipeline, for data preparation, cleansing or enrichment, you can [query any Spark tables](develop-storage-files-spark-tables.md) you've created in the process, directly from SQL on-demand. Use Private Link to bring SQL on-demand endpoint into your private virtual network by mapping it to a private IP address. Keep your data secured using familiar SQL-based security mechanisms.
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If you use Spark in your data pipeline, for data preparation, cleansing or enrichment, you can [query any Spark tables](develop-storage-files-spark-tables.md) you've created in the process, directly from SQL on-demand. Use [Private Link](../security/how-to-connect-to-workspace-with-private-links.md) to bring your SQL on-demand endpoint into your [managed workspace VNet](../security/synapse-workspace-managed-vnet.md).
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## Who is SQL on-demand for
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If you need to explore data in the data lake, gain insights from it or optimize your existing data transformation pipeline, you can benefit from using SQL on-demand. It is suitable for the following scenarios:
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- Basic discovery and exploration - Quickly reason about the data in various formats (Parquet, CSV, JSON) in your data lake, so you can plan how to extract insights from it.
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- Logical data warehouse – Provide a relational abstraction on top of raw or disparate data without relocating and transforming data, allowing always up-to-date view of your data.
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- Data transformation - Simple, scalable, and performant way to transform data in the lake using T-SQL, so it can fed to BI and other tools, or loaded into a relational data store (Synapse SQL databases, Azure SQL Database, etc.).
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- Data transformation - Simple, scalable, and performant way to transform data in the lake using T-SQL, so it can be fed to BI and other tools, or loaded into a relational data store (Synapse SQL databases, Azure SQL Database, etc.).
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Different professional roles can benefit from SQL on-demand:
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- Data Engineers can explore the lake, transform and prepare data using this service, and simplify their data transformation pipelines. For more information, check this tutorial
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- Data Scientists can quickly reason about the contents and structure of the data in the lake, thanks to features such as OPENROWSET and automatic schema inference
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- Data Analysts can [explore data and Spark tables](develop-storage-files-spark-tables.md) created by Data Scientists or Data Engineers using familiar T-SQL language or their favorite tools that can connect to SQL on-demand
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- BI Professionals can quickly [create Power BI reports on top of data in the lake](tutorial-connect-power-bi-desktop.md) and Spark tables
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- Data Engineers can explore the lake, transform and prepare data using this service, and simplify their data transformation pipelines. For more information, check this [tutorial](tutorial-data-analyst.md).
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- Data Scientists can quickly reason about the contents and structure of the data in the lake, thanks to features such as OPENROWSET and automatic schema inference.
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- Data Analysts can [explore data and Spark tables](develop-storage-files-spark-tables.md) created by Data Scientists or Data Engineers using familiar T-SQL language or their favorite tools, which can connect to SQL on-demand.
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- BI Professionals can quickly [create Power BI reports on top of data in the lake](tutorial-connect-power-bi-desktop.md) and Spark tables.
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## What do I need to do to start using it?
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SQL on-demand enables existing SQL ad-hoc querying and business intelligence tools to tap into the data lake. As it provides familiar T-SQL syntax, any tool capable to establish TDS connection SQL offerings can [connect to and query Synapse SQL](connect-overview.md) on-demand. You can connect with Azure Data Studio and run ad-hoc queries or connect with Power BI to gain insights in a matter of minutes.
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At this moment, latest version of Azure Data Studio is fully supported. Latest version of SSMS can be used to connect and query but is not supported yet. It will be fully supported until GA. Please make sure you use latest versions of tools.
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## Is full T-SQL supported?
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SQL on-demand offers T-SQL querying surface area, which is slightly enhanced/extended in some aspects to accommodate for experiences around querying semi-structured and unstructured data. Furthermore, some aspects of T-SQL language are not supported due to the design of SQL on-demand, as an example, DML functionality is currently not supported.
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SQL on-demand offers T-SQL querying surface area, which is slightly enhanced/extended in some aspects to accommodate for experiences around querying semi-structured and unstructured data. Furthermore, some aspects of the T-SQL language are not supported due to the design of SQL on-demand, as an example, DML functionality is currently not supported.
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- Workload can be organized using familiar concepts:
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- Databases - SQL on-demand endpoint can have multiple databases.
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Supported T-SQL:
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- Full [SELECT](/sql/t-sql/queries/select-transact-sql?toc=/azure/synapse-analytics/toc.json&bc=/azure/synapse-analytics/breadcrumb/toc.json&view=azure-sqldw-latest) surface area is supported, including majority of SQL functions
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- Full [SELECT](/sql/t-sql/queries/select-transact-sql?toc=/azure/synapse-analytics/toc.json&bc=/azure/synapse-analytics/breadcrumb/toc.json&view=azure-sqldw-latest) surface area is supported, including a majority of SQL functions
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- CETAS - CREATE EXTERNAL TABLE AS SELECT
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- DDL statements related to views and security only
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SQL on-demand has no local storage, only metadata objects are stored in databases. Therefore, T-SQL related to following concepts is not supported:
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SQL on-demand has no local storage, only metadata objects are stored in databases. Therefore, T-SQL related to the following concepts is not supported:
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- Tables
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- Triggers
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- Materialized views
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- DDL statements other than those related to views and security
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- DDL statements other than ones related to views and security
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- DML statements
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### Extensions
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Authorization refers to what a user can do within a SQL on-demand database, and is controlled by your user account's database role memberships and object-level permissions.
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If SQL Authentication is used, SQL user exists only in SQL on-demand and permissions are scoped to the objects in SQL on-demand. Access to securable objects in other services (such as Azure Storage) can't be granted to SQL user directly since it only exists in scope of SQL on-demand. The SQL user needs to use one of the [supported authorization types](develop-storage-files-storage-access-control.md#supported-storage-authorization-types) to access the files.
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If SQL Authentication is used, the SQL user exists only in SQL on-demand and permissions are scoped to the objects in SQL on-demand. Access to securable objects in other services (such as Azure Storage) can't be granted to SQL user directly since it only exists in scope of SQL on-demand. The SQL user needs to use one of the [supported authorization types](develop-storage-files-storage-access-control.md#supported-storage-authorization-types) to access the files.
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If AAD authentication is used, a user can log in into SQL on-demand and other services, like Azure Storage, can grant permissions to the AAD user.
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If AAD authentication is used, a user can sign in to SQL on-demand and other services, like Azure Storage, and can grant permissions to the AAD user.
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### Access to storage accounts
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A user that is logged into SQL on-demand service must be authorized to access and query the files in Azure Storage. SQL on-demand supports following authorization types:
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A user that is logged into the SQL on-demand service must be authorized to access and query the files in Azure Storage. SQL on-demand supports the following authorization types:
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-**Shared access signature (SAS)** provides delegated access to resources in storage account. With a SAS, you can grant clients access to resources in storage account, without sharing account keys. A SAS gives you granular control over the type of access you grant to clients who have the SAS: validity interval, granted permissions, acceptable IP address range, acceptable protocol (https/http).
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-**User Identity** (also known as "pass-through") is authorization type where the identity of the AAD user that logged into SQL on-demand is used to authorize the access to data. Before accessing the data, Azure Storage administrator must grant permissions to AAD user for accessing the data. This authorization type uses AAD user that logged into SQL on-demand, therefore it's not supported for SQL user type.
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-**User Identity** (also known as "pass-through") is an authorization type where the identity of the AAD user that logged into SQL on-demand is used to authorize access to the data. Before accessing the data, Azure Storage administrator must grant permissions to AAD user for accessing the data. This authorization type uses the AAD user that logged into SQL on-demand, therefore it's not supported for SQL user types.
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## Next steps
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Additional information on endpoint connection and querying files can be found in the following articles:
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