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History to analyze the growth of the databases size over a given period. Additional to the minimum, maximum and average size per week the growth of average size related to the former week is calculated.
Find how much disk space is used on a database in a week. (Between disk space usage differences)
This post was authored by Joseph Sirosh, Corporate Vice President, Microsoft Data Group
Leveraging intelligence out of the ever-increasing amounts of data can make the difference between being the next market disruptor or being relegated to the pages of history. Today at the Microsoft Data Amp online event, we will make several product announcements that can help empower every organization on the planet with data-driven intelligence. We are delivering a comprehensive data platform for developers and businesses to create the next generation of intelligent applications that drive new efficiencies, help create better products, and improve customer experiences.
I encourage you to attend the live broadcast of the Data Amp event, starting at 8 AM Pacific, where Scott Guthrie, executive VP of Cloud and Enterprise, and I will describe product innovations that integrate data and artificial intelligence (AI) to transform your applications and your business. You can stream the keynotes and access additional on-demand technical content to learn more about the announcements of the day.
Today, youâ€™ll see three key innovation themes in our product announcements. The first is the close integration of AI functions into databases, data lakes, and the cloud to simplify the deployment of intelligent applications. The second is the use of AI within our services to enhance performance and data security. The third is flexibilityâ€”the flexibility for developers to compose multiple cloud services into various design patterns for AI, and the flexibility to leverage Windows, Linux, Python, R, Spark, Hadoop, and other open source tools in building such systems.Hosting AI where the data lives
A novel thread of innovation youâ€™ll see in our products is the deep integration of AI with data. In the past, a common application pattern was to create statistical and analytical models outside the database in the application layer or in specialty statistical tools, and deploy these models in custom-built production systems. That results in a lot of developer heavy lifting, and the development and deployment lifecycle can take months. Our approach dramatically simplifies the deployment of AI by bringing intelligence into existing well-engineered data platforms through a new computing model: GPU deep learning. We have taken that approach with the upcoming release of SQL Server, and deeply integrated deep learning and machine learning capabilities to support the next generation of enterprise-grade AI applications.
So today itâ€™s my pleasure to announce the first RDBMS with built-in AIâ€”a production-quality Community Technology Preview (CTP 2.0) of SQL Server 2017. In this preview release, we are introducing in-database support for a rich library of machine learning functions, and now for the first time Python support (in addition to R). SQL Server can also leverage NVIDIA GPU-accelerated computing through the Python/R interface to power even the most intensive deep-learning jobs on images, text, and other unstructured data. Developers can implement NVIDIA GPU-accelerated analytics and very sophisticated AI directly in the database server as stored procedures and gain orders of magnitude higher throughput. In addition, developers can use all the rich features of the database management system for concurrency, high-availability, encryption, security, and compliance to build and deploy robust enterprise-grade AI applications.
We have also released Microsoft R Server 9.1, which takes the concept of bringing intelligence to where your data lives to Hadoop and Spark, as well as SQL Server. In addition to several advanced machine learning algorithms from Microsoft, R Server 9.1 introduces pretrained neural network models for sentiment analysis and image featurization, supports SparklyR, SparkETL, and SparkSQL, and GPU for deep neural networks. We are also making model management easier with many enhancements to production deployment and operationalization. R Tools for Visual Studio provides a state-of-the-art IDE for developers to work with Microsoft R Server. An Azure Microsoft R Server VM image is also available, enabling developers to rapidly provision the server on the cloud.
In the cloud, Microsoft Cognitive Services enable you to infuse your apps with cognitive intelligence. Today I am excited to announce that the Face API, Computer Vision API, and Content Moderator are now generally available in the Azure Portal. Here are some of the different types of intelligence that cognitive services can bring to your application:
Azure Data Lake Analytics (ADLA) is a breakthrough serverless analytics job service where you can easily develop and run massively parallel petabyte-scale data transformation programs that compose U-SQL, R, Python, and .NET. With no infrastructure to manage, you can process data on demand, scale instantly, and pay per job only. Furthermore, weâ€™ve incorporated the technology that sits behind the Cognitive Services inside U-SQL directly as functions. Now you can process massive unstructured data, such as text/images, extract sentiment, age, and other cognitive features using Azure Data Lake, and query/analyze these by content. This enables what I call â€śBig Cognitionâ€”itâ€™s not just extracting one piece of cognitive information at a time, and not just about understanding an emotion or whether thereâ€™s an object in an individual image, but rather itâ€™s about integrating all the extracted cognitive data with other types of data, so you can perform powerful joins, analytics, and integrated AI.
Azure Data Lake Store (ADLS) is a no-limit cloud HDFS storage system that works with ADLA and other big data services for petabyte-scale data. We are announcing the general availability of Azure Data Lake Analytics and Azure Data Lake Store in the Azure North Europe region.
Yet another powerful integration of data and AI is the seamless integration of DocumentDB with Spark to enable machine learning and advanced analytics on top of globally distributed data. To recap, DocumentDB is a unique, globally distributed, limitless NoSQL database service in Azure designed for mission-critical applications. Designed as such from the ground up, it allows customers to distribute their data across any number of Azure regions worldwide, guarantees low read and write latencies, and offers comprehensive SLAs for data-loss, latency, availability, consistency, and throughput. You can use it as either your primary operational database or as an automatically indexed, virtually infinite data lake. The Spark connector understands the physical structure of DocumentDB store (indexing and partitioning) and enables computation pushdown for efficient processing. This service can significantly simplify the process of building distributed and intelligent applications at global scale.
Iâ€™m also excited to announce the general availability of Azure Analysis Services. Built on the proven business intelligence (BI) engine in Microsoft SQL Server Analysis Services, it delivers enterprise-grade BI semantic modeling capabilities with the scale, flexibility, and management benefits of the cloud. Azure Analysis Services helps you integrate data from a variety of sourcesâ€”for example, Azure Data Lake, Azure SQL DW, and a variety of databases on-premises and in the cloudâ€”and transform them into actionable insights. It speeds time to delivery of your BI projects by removing the barrier of procuring and managing infrastructure. And by leveraging the BI skills, tools, and data your team has today, you can get more from the investments youâ€™ve already made.Stepping up performance and security
Performance and security are central to databases. SQL Server continues to lead in database performance benchmarks, and in every release we make significant improvements. SQL Server 2016 on Windows Server 2016 holds a number of records on the Transaction Processing Performance Council (TPC) benchmarks for operational and analytical workload performance, and SQL Server 2017 does even better. Iâ€™m also proud to announce that the upcoming version of SQL Server will run just as fast on Linux as on Windows, as youâ€™ll see in the newly published 1TB TPC-H benchmark world record nonclustered data warehouse performance achieved with SQL Server 2017 on Red Hat Enterprise Linux and HPE ProLiant hardware.
SQL Server 2017 will also bring breakthrough performance, scale, and security features to data warehousing. With up to 100x faster analytical queries using in-memory Columnstores, PolyBase for single T-SQL querying across relational and Hadoop systems, capability to scale to hundreds of terabytes of data, modern reporting, plus mobile BI and more, it provides a powerful integrated data platform for all your enterprise analytics needs.
In the cloud, Azure SQL Database is bringing intelligence to securing your data and increasing database performance. Threat Detection in Azure SQL Database works around the clock, using machine learning to detect anomalous database activities indicating unusual and potentially harmful attempts to access or exploit databases. Simply turning on Threat Detection helps customers make databases resilient to the possibility of intrusion. Other features of Azure SQL Database such as auto-performance tuning automatically implement, tune, and validate performance to guarantee the most optimal query performance. Together, our intelligent database management features help make your database more secure and faster automatically, freeing up scarce DBA capacity for more strategic work.Simple, flexible multiservice AI solutions in the cloud
We are very committed to simplifying the development of AI systems. Cortana Intelligence is a collection of fully managed big data and analytics services that can be composed together to build sophisticated enterprise-grade AI and analytics applications on Azure. Today we are announcing Cortana Intelligence solution templates that make it easy to compose services and implement common design patterns. These solutions templates have been built on best practice designs motivated by real-world customer implementations done by our engineering team, and include Personalized Offers (for example, for retail applications), Quality Assurance (for example, for manufacturing applications), and Demand Forecasting. These templates accelerate your time to value for an intelligent solution, allowing you to deploy a complex architecture within minutes, instead of days. The templates are flexible and scalable by design. You can customize them for your specific needs, and theyâ€™re backed by a rich partner ecosystem trained on the architecture and data models. Get started today by going to the Azure gallery for Cortana Intelligence solutions.
Also, AppSource is a single destination to discover and seamlessly try business apps built by partners and verified by Microsoft. Partners like KenSci have already begun to showcase their intelligent solutions targeting business decision-makers in AppSource. Now partners can submit Cortana Intelligence apps at AppSource â€śList an appâ€ť page.Cross-platform and open source flexibility
Whether on-premises or in the cloud, cross-platform compatibility is increasingly important in our customersâ€™ diverse and rapidly changing data estates. SQL Server 2017 will be the first version of SQL Server compatible with Windows, Linux, and Linux-based container images for Docker. In addition to running on Windows Server, the new version will also run on Red Hat Enterprise Linux, SUSE Enterprise Linux Server, and Ubuntu. It can also run inside Docker containers on Linux or Mac, which can help your developers spend more time developing and less on DevOps.Getting started
It has never been easier to get started with the latest advances in the intelligent data platform. We invite you to join us to learn more about SQL Server 2017 on Windows, Linux, and in Linux-based container images for Docker; Cognitive Services for smart, flexible APIs for AI; scalable data transformation and intelligence from Azure Data Lake Store and Azure Data Lake Analytics; the Azure SQL Database approach to proactive threat detection and intelligent database tuning; new solution templates from Cortana Intelligence; and precalibrated models for Linux, Hadoop, Spark, and Teradata in R Server 9.1.
Join our Data Amp event to learn more! You can go now to the Microsoft Data Amp online event for live coverage starting at 8 AM Pacific on April 19. Youâ€™ll also be able to stream the keynotes and watch additional on-demand technical content after the event ends. I look forward to your participation in this exciting journey of infusing intelligence and AI into every software application.
This post is authored by Nagesh Pabbisetty, Partner Director of Program Management at Microsoft
Expert data scientists are adopting Advanced Analytics (AA) and Machine Learning (ML) at a rapid pace. This pace can be significantly increased when enterprise-grade AA and ML are available within environments where the customersâ€™ data is, infusing intelligence into mission-critical applications is made much easier and, enterprises can turn to a single vendor to make the world of AA and ML synthesized and supported with the SLAs they have come to expect. At Microsoft, our mission has been to make this vision of ambient intelligence a reality for our customers. We took the first step with Microsoft R Server 9.0, and this follow on release includes significant innovations such as:
You can immediately download Microsoft R Server 9.1 from MSDN and Visual Studio Dev Essentials. It comes packed with tons of value built on top of the latest open source R engine that makes R enterprise-class. Also check out R Client for Windows and R Client for Linux.State of the Art Machine Learning Bring Machine Learning to where your data is
With Microsoft R Server 9.0 release, we provided Machine Learning algorithms battle-tested by Microsoft as MicrosoftML package, available as a part of SQL Server R Services and Microsoft R ServerÂ 9.0 on Windows. We have now made these MicrosoftML algorithms portable and distributed to run on Linux, Windows, and the most popular distributions of Hadoop — Cloudera, Hortonworks, MapR, in addition to SQL Server 2016: Fast linear with L1 and L2 regularization, Fast boosted decision tree, Fast random forest, Logistic regression, with support for L1 and L2 regularization, GPU-accelerated Deep Neural Networks (DNNs) with convolutions, Binary classification using a One-Class Support Vector Machine. This blog demonstrates the use of Microsoft ML algorithms on Hadoop and Spark.Pre-trained Cognitive Models
We make it easy for enterprises to infuse intelligence into their Line of Business (LOB) applications. Conventional methods require significant investments of time and effort to hand-craft Machine Learning models from scratch. Harnessing decades of work on cognitive computing in the context of Bing, Office 365 and Xbox, we are delivering the first installment of pre-trained cognitive models that accelerate time to value. Further, these models can be re-trained with your data and optimized for your business.
We now offer a Sentiment Analysis pre-trained cognitive model, using which you can assess the sentiment of an English sentence/paragraph with just a few lines of code. With the Image Featurizer pre-trained cognitive model, you can derive up to 5,000 features on a given image, and use that to compare similarity between two images. This blog shows you how to benefit from the power of image featurizers and more details of Sentiment Analysis are covered in this blog.Combining the best of Microsoft Innovation and Open Source
We are delivering on the promise of embracing the best of open source, and pairing it with the best of Microsoft innovation. With this release, within the same R script, you can mix and match functions from RevoScaleR and Microsoft ML packages with popular open source packages like SparklyR and through it, H2O. Refer to this blog for examples on how to get the best of both worlds!Optimized Algorithm for Pleasingly Parallel
One of the most popular advanced analytics use cases is Pleasingly Parallel where you run massively parallel computations on partitions that are grouped by one or more attributes.Â These embarrassingly parallel use cases are common across industries:
We have generalized the pattern and provided a highly performant, simple, and flexible RxExecBy() function within RevoScaleR, to address these use cases. Furthermore, this function is portable across all platforms that support Microsoft R Server — Windows, Linux, Hadoop, SQL Server. More details on how to choose the best algorithm for Pleasingly Parallel use-cases are available here.
This release also includes support for Optimized Row Columnar (ORC) file format which provides a highly efficient way to store Hive data, and distributed merge for Spark compute context, RxMerge().Enterprise-Grade Operationalization
We recognize that easy, secure, and high-performance operationalization is essential for Tier-1 enterprises, at scale, to derive maximum value from their analytics investments. Microsoft R Server 9.1 release continues strengthening the power of operationalization. See this blog for more details.
The innovations in Microsoft R Server 9.1 are available to SQL Server 2016 customers; an easy upgrade of R services in SQL Server 2016 as described in this doc and in this blog post, is all you need. The machine learning and pleasingly parallel enhancements listed in the previous section are fully supported on SQL server as well. SQL Server is the first database in the world that has in-database Machine Learning!Real-time scoring
With R Services in SQL Server 2016, we set the industry benchmark for high throughput scoring at 1 Million predictions per second. Now, we have improved single row scoring performance significantly, up to two orders of magnitude better than earlier versions. Real-time scoring is supported on models trained using both RevoScaleR and MicrosoftML algorithms & transforms. With this release, SQL Server understands these models natively and scores inputs without the need of R interpreter and associated overhead, delivering significantly better performance.Flexible R package management
In 9.0.1 release of Microsoft R Server we added functionality in RevoScaleR package that enables users to install, uninstall and manage packages on SQL Server without requiring administrative access to the SQL Server machine. Data scientists and other non-admin users can install packages in specific databases, user or group scope. In this release, we have added the rxSyncPackages API to ensure that the user-installed packages are not lost if the SQL Server node goes down or if the database is migrated. The list of packages and the permissions is maintained in a server table and this API ensures that the required packages are installed on the file system.SQL Server Machine Learning Services – Python Preview
SQL Server 2016 brought you in-database analytics with SQL Server R Services. With CTP 1 of SQL Server 2017, MicrosoftML provided in-database Machine Learning.Â CTP 2.0 of SQL Server 2017 brings you SQL Server Machine Learning Services that embraces both R and Python. Data Scientists can now choose from a huge collection of analytics and machine learning algorithms across R and Python communities to execute in-database and get their job done much more effectively. CTP 2.0 enables collaboration between traditional data scientists with strong R backgrounds and computer scientists with strong Python backgrounds, to deliver the best business ROI.
Additionally, the real-time scoring and flexible package management functionality listed above for SQL Server R Services is also available in the CTP2 release as part of Machine Learning services. Refer to this blog on how to get the best of both R and Python worlds!Customer & ISV Partnerships Engaging with Customers
â€śWorking with Microsoft R Server for our data science needs at eToro has been a key factor in our success. The tools are appropriate for all levels of data scientist skills from beginners to seasoned professionals. Using Microsoft R Server, we were able to quickly run large scale statistical simulations in a distributed manner that ensured the robustness of our machine learning models. This partnership was instrumental in meeting our business goals and we look forward to using the continuing innovation coming out on Microsoft R Server!â€ť — Moti Goldklang, Director of Trading Systems, eToro.
We are committed to finding more ways for our customers to connect with us, to understand how to get the most out of their investments and provide feedback to influence product direction.Â We offer a variety of ways customers can engage closely with Microsoft and provide product feedback.
User Voice: As a customer focus team, we are interested in listening to your feedback and to help us steer our product capability we are launching User Voice for Microsoft R today. You can partake in discussion and cast your vote on features that youâ€™d like to see us enable. We are listening!Authoring Tools from Microsoft and Partners
I am happy to announce that we have a number options to help you develop Microsoft R based applications, both from Microsoft and from our partners. R Tools for Visual Studio (RTVS) is now Generally Available, and brings support for Microsoft R into Visual Studio. In addition, we also have Python Tools for Visual Studio (PTVS) for your Python development. In addition, we have worked with MicroStrategy, Alteryx and KNIME, and, augmented open source Rattle, to give you more choices.
Microsoft has been contributing to the R Community to ensure that there is an open source WYSIWYG tool to do big data analytics in the community. We have enhanced the popular Rattle package to support Microsoft R Server capabilities. You can download the latest, and stay abreast with the developments here.
With Alteryx Designer 11.0, a self-service analytics workflow tool from Alteryx, business analysts and data scientists can work with Microsoft R Server and SQL Server R Services. In the words of Neil Ryan, Product Manager at Alteryx, â€śAt Alteryx we’re acutely aware of the challenge of getting faster insights from very large datasets. When it comes to computation-intensive machine learning, it’s even more important to leverage existing hardware resources and keep the processing as close as possible to where the data lives. That’s why we’re excited about our partnership with Microsoft. By leveraging Microsoft R Server and SQL Server’s in database analytics, our customers are scaling their analytics to the size of their data through a consistent, code-free, drag and drop interface for both data preparation and modeling within SQL Server.â€ť More details are in this blog post.
Microsoft and KNIME have partnered to bring Microsoft R capabilities to the KNIME platform. â€śKNIME has added the option to reach out to Microsoft R from KNIME Analytics Platform to make a scalable and enterprise ready R integration part of any KNIME workflow,â€ť says Michael Berthold, CEO of KNIME. Here is an example of how this works and you can see it in action here.
MicroStrategy has made Microsoft R runtime accessible from MicroStrategy Desktop. â€śMicroStrategy is embracing Microsoft R in our analytics platform tools to bring the power of advanced analytics and machine learning to our customers. We just announced this at MicroStrategy World and you can read more about this here,â€ť says Sandipto Banerjee, VP Data Group & Advanced Technologies, MicroStrategy.Getting Started
The best place to get started is our comprehensive documentation site, which introduces concepts, platforms, features, code samples, and how-tos. Our vibrant blogs include R Server Blog that wasÂ launched earlier this year on all thing R, R Tiger Team which covers deep technical insights on Microsoft R Server, and the Revolutions R Blog which highlights both Microsoft R and open-source R innovations. Together, these blogs provide a plethora of articles, tips and tricks for novices and experts alike. I welcome you to check these out and leave us your comments.
Check out the free Data Science with Microsoft SQL Server 2016 eBook that covers whatâ€™s new, installing & configuring R Services, and how to develop full applications through walkthroughs.
Want to get certified and show your mastery in data science? We have your covered via several courses at Microsoft LearnAnalytics and Microsoft Academy! We have several training partners that can help you train your teams on advanced analytics and machine learning!Solution Templates
Check out the R Solution Templates that will walk you through how to develop a solution using Microsoft R Server, from beginning to end. In addition, with the click of a button, you can deploy these templates to an AzureVM and see the entire application in action. You can follow the links to github and use the code as a starting point for your own solution, and accelerate time to value!
In our last release, we provided a Solutions Template for Campaign Optimization using SQL Server R Services.Â Now, we have added a solution template for the Azure HDInsight platform on Spark compute context.Â In the words of Anindya Palit, EVP Affine Analytics, â€śPartnering with Microsoft allowed Affineâ€™s extensive analytics experience in marketing to be transformed into a solution for optimizing lead generation through Campaign Optimization. We were able to quickly ramp up and build the solution utilizing the power of R Services within SQL Server.â€ť
Hospital Length Of Stay (LOS) is the latest solution template built on SQL Server R Services. Dr. Greg Mckelvey, Head of Clinical Insights, KenSci, says â€śThe Hospital ‘Length of Stay Prediction’ solution shows how you can build a potentially life-saving machine learning solution by leveraging the power of R within SQL Server. By predicting how long an admitted patient is likely to stay at the hospital based on clinical history, labs and vital, the solution enables doctors and nurses to better manage patient flow and coordinate post-discharge patient care.â€ťAzure VMs
Microsoft R Server 9.1 will be released as Azure VMs in Azure Marketplace, Data Science VMs, and on Azure HDInsight. VMs were available on CentOS 7.2 and Ubuntu 16.04. Now, we have added support for RHEL 7.2, and made all VMs available in China.Microsoft R Client
With our current release, we are delivering Microsoft R Client on the Linux platform for the first time, in addition to Windows. R Client is available on all four popular flavors of Linux â€“ RHEL, CentOS, Ubuntu, and SuSE.Â Please check out R Client for Windows and R Client for Linux.In Summary
I am proud of how we are making R enterprise-grade through Microsoft R portfolio of products and services, building on top of open source R in fully compatible ways. Adopting advanced analytics and machine learning requires a holistic approach that transcends technology, people and processes; we continue to deliver more handholding to ensure that enterprise users are set up for success! With the 9.1 release, you have in-database analytics and machine learning in a variety of platforms, develop powerful analytics models leveraging both open source and Microsoft innovation, deploy them at scale, and easily integrate into line-of-business systems to maximize ROI.Â We invite you to get started with Microsoft R Server 9.1.
Microsoft is excited to announce a new preview for the next version of SQL Server!Â We disclosed a name for this next release, SQL Server 2017, today at the Microsoft Data Amp event. Community Technology Preview (CTP) 2.0 is the first production-quality preview of SQL Server 2017, and it is available on both Windows and Linux.Â In this preview, we added a number of new capabilities, including the ability to run advanced analytics using Python in a parallelized and highly scalable way, the ability to store and analyze graph data, and other capabilities that help you manage SQL Server for high performance and uptime, including the Adaptive Query Processing family of intelligent database features and resumable online indexing.
In addition to all these great new features we are excited to announce a world record in the TPC-H 1TB data warehousing workload (non-clustered). The benchmark was achieved with SQL Server 2017 on Red Hat Enterprise Linux and HPE Prolliant server hardware, beating SQL Server 2016 on the same hardware handily.Â This is just the first of many anticipated performance benchmarks for SQL Server 2017 on Linux and Windows, demonstrating SQL Serverâ€™s industry leading performance.Â When taken in conjunction with the fact that SQL Server has had the least vulnerabilities of any major database over the last 7 years in the National Institute of Standards and Technology (NIST) vulnerability database, SQL Server 2017 on Windows and Linux is the best database for your Mission Critical application and data warehouse workloads.
You can try the preview in your choice of development and test environments now.
We also added support for storing and analyzing graph data relationships. This includes full CRUD support to create nodes and edges and T-SQL query language extensions to provide multi-hop navigation using join-free pattern matching. In addition, SQL Server engine integration enables querying across SQL tables and graph data. And, you can use all of your existing SQL Server tools to work with graph data.
With resumable online index rebuild, you can resume a paused index rebuild operation from where the rebuild operation was paused rather than having to restart the operation at the beginning. Additionally, this feature rebuilds indexes using only a small amount of log space. This feature will help pick up right where you left off when an index maintenance job encounters issues, or allow you to split index rebuilds across maintenance windows.
New in SQL Server 2017, weâ€™re adding the Adaptive Query Processing family of intelligent database features. These features automatically keep database queries running as efficiently as possible without requiring additional tuning from database administrators.Â In addition to the previous capability to adjust batch mode memory grants, in CTP 2.0 Adaptive Query Processing adds the batch mode adaptive joins and interleaved execution capabilities. Interleaved execution will improve the performance of queries that reference multi-statement table valued functions by surfacing runtime row counts to the query optimizer.Â Batch mode adaptive joins enables the choice of a queryâ€™s physical join algorithm to be deferred until actual query execution, improving performance based on runtime conditions.
In addition, some functionality that was previously available in SQL Server on Windows is now available on Linux for the first time. This includes:
Another new, key feature enhancement in CTP 2.0 of SQL Server 2017 is the ability to run the Python language in-database to scale and accelerate machine learning, predictive analytics and data science scripts. The new capability, called Microsoft Machine Learning Services, enables Python scripts to be run directly within the database server, or to be embedded into T-SQL scripts, where they can be easily deployed to the database as stored procedures and easily called from SQL client applications by stored procedure call. SQL Server 2017 will also extend Pythonâ€™s performance and scale by providing a selection of parallelized algorithms that accelerate data transforms, statistical tests and analytics algorithms. This functionality and the ability to run R in-database and at scale are only available on Windows Server operating system at this time.
Get started with the preview of SQL Server with our developer tutorials that show you how to install and use SQL Server 2017 on macOS, Docker, Windows, and Linux and quickly build an app in a programming language of your choice.