Platforms and tools for data science projects
Microsoft provides a full spectrum of analytics resources for both cloud or on-premises platforms. They can be deployed to make the execution of your data science projects efficient and scalable. Guidance for teams implementing data science projects in a trackable, version controlled, and collaborative way is provided by the Team Data Science Process (TDSP). See Team Data Science Process roles and tasks, for an outline of the personnel roles, and their associated tasks that are handled by a data science team standardizing on this process.
The main recommended Azure resource for TDSP is Azure Machine Learning. Examples in Azure Architecture Center sometimes show Azure Machine Learning used with other Azure resources. These other analytics resources available to data science teams using the TDSP include:
- Data Science Virtual Machines (both Windows and Linux CentOS)
- HDInsight Spark Clusters
- Azure Synapse Analytics
- Azure Data Lake
- HDInsight Hive Clusters
- Azure File Storage
- SQL Server 2019 R and Python Services
- Azure Databricks
In this document, we briefly describe the resources and provide links to the tutorials and walkthroughs the TDSP teams have published. The articles will show you how to these resources step by step to build your intelligent applications. More information on these resources is available on their product pages.
Data Science Virtual Machine (DSVM)
The data science virtual machine offered on both Windows and Linux by Microsoft, contains popular tools for data science modeling and development activities. It includes tools such as:
- Microsoft R Server Developer Edition
- Anaconda Python distribution
- Jupyter notebooks for Python and R
- Visual Studio Community Edition with Python and R Tools on Windows / Eclipse on Linux
- Power BI desktop for Windows
- SQL Server 2016 Developer Edition on Windows / Postgres on Linux
It also includes ML and AI tools like xgboost, mxnet, and Vowpal Wabbit.
Currently DSVM is available in Windows and Linux CentOS operating systems. Choose the size of your DSVM (number of CPU cores and the amount of memory) based on the needs of the data science projects that you plan to execute on it.
To learn how to execute some of the common data science tasks on the DSVM efficiently, see 10 things you can do on the Data science Virtual Machine
Azure HDInsight Spark clusters
Apache Spark is an open-source parallel processing framework that supports in-memory processing to boost the performance of big-data analytic applications. The Spark processing engine is built for speed, ease of use, and sophisticated analytics. Spark's in-memory computation capabilities make it a good choice for iterative algorithms in machine learning and for graph computations. Spark is also compatible with Azure Blob storage (WASB), so your existing data stored in Azure can easily be processed using Spark.
When you create a Spark cluster in HDInsight, you create Azure compute resources with Spark installed and configured. It takes about 10 minutes to create a Spark cluster in HDInsight. Store the data to be processed in Azure Blob storage. For information on using Azure Blob Storage with a cluster, see Use HDFS-compatible Azure Blob storage with Hadoop in HDInsight.
TDSP team from Microsoft has published two end-to-end walkthroughs on how to use Azure HDInsight Spark Clusters to build data science solutions, one using Python and the other Scala. For more information on Azure HDInsight Spark Clusters, see Overview: Apache Spark on HDInsight Linux. To learn how to build a data science solution using Python on an Azure HDInsight Spark Cluster, see Overview of Data Science using Spark on Azure HDInsight. To learn how to build a data science solution using Scala on an Azure HDInsight Spark Cluster, see Data Science using Scala and Spark on Azure.
Azure Synapse Analytics
Azure Synapse Analytics allows you to scale compute resources easily and in seconds, without over-provisioning or over-paying. It also offers the unique option to pause the use of compute resources, giving you the freedom to better manage your cloud costs. The ability to deploy scalable compute resources makes it possible to bring all your data into Azure Synapse Analytics. Storage costs are minimal and you can run compute only on the parts of datasets that you want to analyze.
For more information on Azure Synapse Analytics, see the Azure Synapse Analytics website. To learn how to build end-to-end advanced analytics solutions with Azure Synapse Analytics, see The Team Data Science Process in action: using Azure Synapse Analytics.
Azure Data Lake
Azure Data Lake is as an enterprise-wide repository of every type of data collected in a single location, prior to any formal requirements, or schema being imposed. This flexibility allows every type of data to be kept in a data lake, regardless of its size or structure or how fast it is ingested. Organizations can then use Hadoop or advanced analytics to find patterns in these data lakes. Data lakes can also serve as a repository for lower-cost data preparation before curating the data and moving it into a data warehouse.
For more information on Azure Data Lake, see Introducing Azure Data Lake. To learn how to build a scalable end-to-end data science solution with Azure Data Lake, see Scalable Data Science in Azure Data Lake: An end-to-end Walkthrough
Azure HDInsight Hive (Hadoop) clusters
Apache Hive is a data warehouse system for Hadoop, which enables data summarization, querying, and the analysis of data using HiveQL, a query language similar to SQL. Hive can be used to interactively explore your data or to create reusable batch processing jobs.
Hive allows you to project structure on largely unstructured data. After you define the structure, you can use Hive to query that data in a Hadoop cluster without having to use, or even know, Java or MapReduce. HiveQL (the Hive query language) allows you to write queries with statements that are similar to T-SQL.
For data scientists, Hive can run Python User-Defined Functions (UDFs) in Hive queries to process records. This ability extends the capability of Hive queries in data analysis considerably. Specifically, it allows data scientists to conduct scalable feature engineering in languages they're mostly familiar with: the SQL-like HiveQL and Python.
For more information on Azure HDInsight Hive Clusters, see Use Hive and HiveQL with Hadoop in HDInsight. To learn how to build a scalable end-to-end data science solution with Azure HDInsight Hive Clusters, see The Team Data Science Process in action: using HDInsight Hadoop clusters.
Azure File Storage
Azure File Storage is a service that offers file shares in the cloud using the standard Server Message Block (SMB) Protocol. Both SMB 2.1 and SMB 3.0 are supported. With Azure File storage, you can migrate legacy applications that rely on file shares to Azure quickly and without costly rewrites. Applications running in Azure virtual machines or cloud services or from on-premises clients can mount a file share in the cloud, just as a desktop application mounts a typical SMB share. Any number of application components can then mount and access the File storage share simultaneously.
Especially useful for data science projects is the ability to create an Azure file store as the place to share project data with your project team members. Each of them then has access to the same copy of the data in the Azure file storage. They can also use this file storage to share feature sets generated during the execution of the project. If the project is a client engagement, your clients can create an Azure file storage under their own Azure subscription to share the project data and features with you. In this way, the client has full control of the project data assets. For more information on Azure File Storage, see Get started with Azure File storage on Windows and How to use Azure File Storage with Linux.
SQL Server 2019 R and Python Services
R Services (In-database) provides a platform for developing and deploying intelligent applications that can uncover new insights. You can use the rich and powerful R language, including the many packages provided by the R community, to create models and generate predictions from your SQL Server data. Because R Services (In-database) integrates the R language with SQL Server, analytics are kept close to the data, which eliminates the costs and security risks associated with moving data.
R Services (In-database) supports the open source R language with a comprehensive set of SQL Server tools and technologies. They offer superior performance, security, reliability, and manageability. You can deploy R solutions using convenient and familiar tools. Your production applications can call the R runtime and retrieve predictions and visuals using Transact-SQL. You also use the ScaleR libraries to improve the scale and performance of your R solutions. For more information, see SQL Server R Services.
The TDSP team from Microsoft has published two end-to-end walkthroughs that show how to build data science solutions in SQL Server 2016 R Services: one for R programmers and one for SQL developers. For R Programmers, see Data Science End-to-End Walkthrough. For SQL Developers, see In-Database Advanced Analytics for SQL Developers (Tutorial).
Appendix: Tools to set up data science projects
Install Git Credential Manager on Windows
If you're following the TDSP on Windows, you need to install the Git Credential Manager (GCM) to communicate with the Git repositories. To install GCM, you first need to install Chocolaty. To install Chocolaty and the GCM, run the following commands in Windows PowerShell as an Administrator:
iwr https://chocolatey.org/install.ps1 -UseBasicParsing | iex
choco install git-credential-manager-for-windows -y
Install Git on Linux (CentOS) machines
Run the following bash command to install Git on Linux (CentOS) machines:
sudo yum install git
Generate public SSH key on Linux (CentOS) machines
If you're using Linux (CentOS) machines to run the git commands, you need to add the public SSH key of your machine to your Azure DevOps services. This way the machine is recognized by the Azure DevOps Services. First, you need to generate a public SSH key and add the key to SSH public keys in your Azure DevOps services security setting page.
To generate the SSH key, run the following two commands:
ssh-keygen cat .ssh/id_rsa.pub
Copy the entire ssh key including ssh-rsa.
Log in to your Azure DevOps Services.
Click <Your Name> at the top-right corner of the page and click security.
Click SSH public keys, and click +Add.
Paste the ssh key copied into the text box and save.
This article is maintained by Microsoft. It was originally written by the following contributors.
- Mark Tabladillo | Senior Cloud Solution Architect
To see non-public LinkedIn profiles, sign in to LinkedIn.
- Understand data science for machine learning
- Introduction to Azure Machine Learning
- Databricks Data Science & Engineering