Install SQL Server 2019 big data tools

THIS TOPIC APPLIES TO:yesSQL Server noAzure SQL DatabasenoAzure Synapse Analytics (SQL DW) noParallel Data Warehouse

This article describes the client tools that should be installed for creating, managing, and using SQL Server 2019 Big Data Clusters. The following section provides a list of tools and links to installation instructions. Before deploying a big data cluster, configure the tools marked required on Windows or Linux.

Big data cluster tools

The following table lists common big data cluster tools and how to install them:

Tool Required Description Installation
python Yes Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Many parts of big data clusters for SQL Server use python. Install python
azdata Yes Command-line tool for installing and managing a big data cluster. Install
kubectl1 Yes Command-line tool for monitoring the underlying Kubernetes cluster (More info). Windows | Linux
Azure Data Studio Yes Cross-platform graphical tool for querying SQL Server. Install
Data Virtualization extension Yes Extension for Azure Data Studio that provides a Data Virtualization wizard. Install
Azure CLI2 For AKS Modern command-line interface for managing Azure services. Used with AKS big data cluster deployments (More info). Install
mssql-cli Optional Modern command-line interface for querying SQL Server (More info). Windows | Linux
sqlcmd For some scripts Legacy command-line tool for querying SQL Server (More info). Windows | Linux
curl 3 For some scripts Command-line tool for transferring data with URLs. Windows | Linux: install curl package

1 You must use kubectl version 1.13 or later. Also, the version of kubectl should be plus or minus one minor version of your Kubernetes cluster. If you want to install a specific version on kubectl client, see Install kubectl binary via curl (on Windows 10, use cmd.exe and not Windows PowerShell to run curl).

Tip

To use kubectl with a previously deployed cluster on Azure Kubernetes Service (AKS), you must set the cluster context with the following Azure CLI command:

az aks get-credentials --name <aks_cluster_name> --resource-group <azure_resource_group_name>

2 You must be using Azure CLI version 2.0.4 or later. Run az --version to find the version if needed.

3 If you are running on Windows 10, curl is already in your PATH when running from a cmd prompt. For other versions of Windows, download curl using the link and place it in your PATH.

Which tools are required?

The previous table provides all of the common tools that are used with big data clusters. Which tools are required depends on your scenario. But in general, the following tools are most important for managing, connecting to, and querying the cluster:

  • azdata
  • kubectl
  • Azure Data Studio
  • Data Virtualization extension

The remaining tools are only required in certain scenarios. Azure CLI can be used to manage Azure services associated with AKS deployments. mssql-cli is an optional but useful tool that allows you to connect to the SQL Server master instance in the cluster and run queries from the command line. And sqlcmd and curl are required if you plan to install sample data with the GitHub script.

Install python offline

  1. On a machine with internet access, download one of the following compressed files containing Python:

    Operating system Download
    Windows https://go.microsoft.com/fwlink/?linkid=2074021
    Linux https://go.microsoft.com/fwlink/?linkid=2065975
    OSX https://go.microsoft.com/fwlink/?linkid=2065976
  2. Copy the compressed file to the target machine and extract it to a folder of your choice.

  3. For Windows only, run installLocalPythonPackages.bat from that folder and pass the full path to the same folder as a parameter.

    installLocalPythonPackages.bat "C:\python-3.6.6-win-x64-0.0.1-offline\0.0.1"
    

Download and install Azure Data Studio

Azure Data Studio provides capabilities and features specifically for SQL Server Big Data Clusters.

Get the latest Azure Data Studio.

For details about the latest release, see the release notes.

Next steps

After configuring the tools, deploy a SQL Server 2019 big data cluster to Kubernetes in the Cloud or on-premises. For more information, see the following deployment articles:

For More info about big data clusters, see What are SQL Server 2019 Big Data Clusters?.