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‎docs/advanced-analytics/what-is-sql-server-machine-learning.md‎

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ms.prod: sql
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ms.technology: machine-learning
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ms.date: 09/10/2018
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ms.date: 11/06/2018
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ms.topic: overview
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author: HeidiSteen
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ms.author: heidist
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SQL Server 2017 Machine Learning Services is an add-on to a database engine instance, used for executing R and Python code on SQL Server. Code runs in an extensibility framework, isolated from core engine processes, but fully available to relational data as stored procedures, as T-SQL script containing R or Python statements, or as R or Python code containing T-SQL.
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If you previously used [SQL Server 2016 R Services](r/sql-server-r-services.md), Machine Learning Services in SQL Server 2017 is the next generation of R support, with updated versions of base R, RevoScaleR, MicrosoftML, and other libraries introduced in 2016.
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If you previously used [SQL Server 2016 R Services](r/sql-server-r-services.md), Machine Learning Services in SQL Server 2017 is the next generation of R support, with updated versions of base R, RevoScaleR, MicrosoftML, and other libraries introduced in 2016.
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In Azure SQL Database, [Machine Learning Services (with R)]((https://docs.microsoft.com/azure/sql-database/sql-database-connect-query-r)) is currently in public preview.
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The key value proposition of Machine Learning Services is the power of its enterprise R and Python packages to deliver advanced analytics at scale, and the ability to bring calculations and processing to where the data resides, eliminating the need to pull data across the network.
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‎docs/advanced-analytics/what-s-new-in-sql-server-machine-learning-services.md‎

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ms.prod: sql
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ms.date: 09/24/2018
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ms.date: 11/06/2018
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<a name="azure-sql-database-roadmap"></a>
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## Azure SQL Database roadmap
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## Machine Learning Services in Azure SQL Database
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There is currently no support for Machine Learning Services (R and Python) in Azure SQL Database. Support is on the roadmap but there is no projected release date at this time.
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Machine Learning Services (with R) in Azure SQL Database is in public preview. For more information, see [Quickstart: Use Machine Learning Services (with R) in Azure SQL Database (preview)](https://docs.microsoft.com/azure/sql-database/sql-database-connect-query-r).
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## Next steps
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‎docs/analysis-services/tabular-models/data-sources-supported-ssas-tabular.md‎

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---
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title: "Data sources supported in SQL Server Analysis Services tabular 1200 models | Microsoft Docs"
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ms.date: 11/05/2018
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ms.date: 11/07/2018
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ms.prod: sql
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ms.custom: tabular-models
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|Data feeds<br /><br /> (used to import data from Reporting Services reports, Atom service documents, Microsoft Azure Marketplace DataMarket, and single data feed)|Atom 1.0 format<br /><br /> Any database or document that is exposed as a Windows Communication Foundation (WCF) Data Service (formerly ADO.NET Data Services).|`.atomsvc` for a service document that defines one or more feeds<br /><br /> .atom for an Atom web feed document|Microsoft Data Feed Provider for [!INCLUDE[ssGemini](../../includes/ssgemini-md.md)]<br /><br /> .NET Framework data feed data provider for [!INCLUDE[ssGemini](../../includes/ssgemini-md.md)]|
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|Office Database Connection files||.odc||
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<a name="dnu">[1]</a> Using **ACE 14 OLE DB provider** to connect to file data types **is not recommended**. If you must retain your tabular 1200 and lower compatibility level models, export your data to a csv file type, import to SQL database, and then connect to and import from the database. However, it's recommended you upgrade to tabular 1400 compatibility level (SQL Server 2017 and later) and use **Get Data** in SSDT to select and import your file data source. Get Data uses structured data source connections, which provide greater stability than ACE 14 OLE DB provider connections.
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<a name="dnu">[1]</a> Using **ACE 14 OLE DB provider** to connect to file data types **is not recommended**. If you must retain your tabular 1200 and lower compatibility level models, export your data to a csv file type, import to SQL database, and then connect to and import from the database. However, it's recommended you upgrade to tabular 1400 compatibility level (SQL Server 2017 and later) and use **Get Data** in SSDT to select and import your file data source. Get Data uses structured data source connections provided by the Power Query data engine, which are more stable than ACE 14 OLE DB provider connections.
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## <a name="bkmk_supported_ds_dq"></a> Supported data sources for DirectQuery models

‎docs/analytics-platform-system/whats-new-analytics-platform-system.md‎

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**Data types**
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- [VARCHAR(MAX)][], [NVARCHAR(MAX)][] and [VARBINARY(MAX)][]. These LOB data types have a maximum size of 2 GB. To load these objects use [bcp Utility][]. Polybase and dwloader do not currently support these data types.
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- [VARCHAR(MAX)][], [NVARCHAR(MAX)][] and [VARBINARY(MAX)][]. These LOB data types have a maximum size of 2 GB. To load these objects use [bcp Utility][]. PolyBase and dwloader do not currently support these data types.
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- [SYSNAME][]
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- [UNIQUEIDENTIFIER][]
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- [NUMERIC][] and DECIMAL data types.

‎docs/azure-data-studio/release-notes.md‎

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- Use the PROSE Code Accelerator libraries in your Notebook to learn file format and data types for fast data preparation.
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- Azure Resource Explorer
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- The Azure Resource Explorer view lets you browse data-related endpoints for your Azure accounts and create connections to them in Object Explorer. In this release Azure SQL Databases and servers are supported.
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- SQL Server Polybase Create External Table Wizard
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- SQL Server PolyBase Create External Table Wizard
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- Create an external table and its supporting metadata structures with an easy to use wizard. In this release, remote SQL Server and Oracle servers are supported.
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- Query Results Grid performance and UX improvements for large number of result sets.
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- Visual Studio Code source code refresh from 1.23 to 1.26.1 with Grid Layout and Improved Settings Editor (preview).

‎docs/azure-data-studio/sql-server-2019-extension.md‎

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* Support reinstalling Notebook dependencies. This is useful if the user previously closed Azure Data Studio partway through installation.
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* Support canceling cell execution in Notebook.
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* Improved reliability when using Create External Data wizard, specifically when connection errors occur.
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* Block use of Create External Data wizard if Polybase is not enabled or running in the target server.
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* Block use of Create External Data wizard if PolyBase is not enabled or running in the target server.
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* Spelling and naming fixes related to SQL Server 2019 and Create External Data.
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* Removed a large number of errors from the Azure Data Studio debug console.
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* Add code or markdown cells via the buttons in the notebook header. Remove cells with the trash can icon to the left of each cell.
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* Run cells with the play button for code cells, and toggle markdown editing and preview with the eye icon
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## Polybase Create External Table Wizard
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## PolyBase Create External Table Wizard
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* From a SQL Server 2019 instance the *Create External Table Wizard* can be opened in three ways:
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* Right click on a server, choose **Manage**, click on the tab for SQL Server 2019 (Preview), and choose **Create External Table**.

‎docs/big-data-cluster/big-data-cluster-create-apps.md‎

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The **mssqlctl-pre** command-line utility is provided to preview the Python and R application deployment feature. Use the following command to install the utility:
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pip3 install --extra-index-url https://private-repo.microsoft.com/python/ctp-2.1 mssqlctl-pre
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pip3 install --extra-index-url https://private-repo.microsoft.com/python/ctp-2.1 mssqlctlpre
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## Capabilities

‎docs/big-data-cluster/deployment-guidance.md‎

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‎docs/big-data-cluster/quickstart-big-data-cluster-deploy.md‎

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To install the **mssqlctl** CLI tool to manage the SQL Server big data cluster on your client machine, you must first install [Python](https://www.python.org/downloads/) minimum version v3.0 and [pip3](https://pip.pypa.io/en/stable/installing/). `pip` is already installed if you are using a Python version of at least 3.4 downloaded from [python.org](https://www.python.org/).
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If your Python installation is missing the `requests` package, you must install `requests` using `python -m pip install requests` (use `python3` for these commands on Linux). If you already have a `requests` package, upgrade it to latest version using `python -m pip install requests --upgrade`.
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Once you have the AKS cluster deployed, you can execute the below kubectl command to view the cluster configuration. Ensure that kubectl is pointed to the correct cluster context.

‎docs/database-engine/configure-windows/configure-windows-service-accounts-and-permissions.md‎

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- **SQL Server PolyBase Engine** - Provides distributed query capabilities to external data sources.
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- **SQL Server Polybase Data Movement Service** - Enables data movement between SQL Server and External Data Sources and between SQL nodes in PolyBase Scaleout Groups.
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- **SQL Server PolyBase Data Movement Service** - Enables data movement between SQL Server and External Data Sources and between SQL nodes in PolyBase Scaleout Groups.
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## <a name="Serv_Prop"></a> Service Properties and Configuration
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