Predictive Analysis using Microsoft SQL Server R Services
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The document discusses predictive analysis using Microsoft SQL Server R Services and introduces the capabilities of R and its integration with SQL Server for advanced analytics. It covers the implementation process, benefits for R and SQL users, and the various tools available for building and running predictive models. Additionally, it compares R and Python for machine learning, highlighting their respective strengths and applications in data science.
![Diagnostic
[Interactive Dashboards]
Prescriptive
[Recommendations & Automation]
Predictive
[Machine Learning]
Descriptive
[Reports]
What should
I do?
What will
happen?
Why did it
happen?
What
happened? Insight
From data to decisions and actions](https://image.slidesharecdn.com/predictiveanalysisusingmicrosoftsqlserverrservices-181118114558/85/Predictive-Analysis-using-Microsoft-SQL-Server-R-Services-4-320.jpg)
![• New stored procedure
EXEC sp_execute_external_script
@language = N’R’,
@script = N’[R code goes here]’,
@input_data_1 = N’[SQL input]’
[ , @input_data_1_name = N‘InputDataSet’ ]
[ , @output_data_1_name = N’OutputDataSet’ ]
[ , @params = N’parameter’ ]
WITH RESULT SETS (([SQL output]));
input_data_1_name and
output_data_1_name are optional
and default to InputDataSet and
OutputDataSet respectively
Operationalized R](https://image.slidesharecdn.com/predictiveanalysisusingmicrosoftsqlserverrservices-181118114558/85/Predictive-Analysis-using-Microsoft-SQL-Server-R-Services-18-320.jpg)
Predictive Analysis using Microsoft SQL Server R Services
- 1. Predictive Analysis using Microsoft SQL Server R Services Advanced Analytics Introduction
- 2. Speaker • Fisnik Doko Microsoft Certified Trainer • 21 active certificates - Microsoft • MCSD: App Builder | Web applications • MCSE: Data Management and Analytics | Data Platform • MCSE: Cloud Platform and Infrastructure • Software Architect • Consultant • Speaker
- 3. Content • Advanced Analytics Introduction • What is R? • What is Microsoft R? • Microsoft SQL Server R Services • R for machine learning • Demo • R and Python
- 4. Diagnostic [Interactive Dashboards] Prescriptive [Recommendations & Automation] Predictive [Machine Learning] Descriptive [Reports] What should I do? What will happen? Why did it happen? What happened? Insight From data to decisions and actions
- 5. Retail & Consumer Products Healthcare Financial Services & Insurance Government Manufacturing € Industries applying advanced analytics
- 6. Typical Predictive Analytics Process • Prepare: Assemble, cleanse, profile and transform diverse data relevant to the subject OperationalizeModelPrepare SQL Query Data Science R in Database
- 7. What is • A statistics programming language • A data visualization tool • Open source • 2.5+M users • Taught in most universities • Thriving user groups worldwide • 15 000+ free algorithms in CRAN • Machine Learning includes 400+ R packages • Scalable to big data • New and recent grad’s use it Language Platform Community Ecosystem • Rich application & platform integration R
- 9. Revolution Analytics • Revolution Analytics was founded in 2007 • On January 23, 2015 Microsoft purchased Revolution Analytics
- 10. Microsoft R Client • Freely available and based on Microsoft Open R • Run locally • Can install any open source R packages • Limited to two threads • Datasets must fit in memory • Chunking data is not available • Can interact with R Server
- 11. What is Microsoft R Server? • Renamed to Microsoft Machine Learning Server (SQL 2017) • Added support for the full data science lifecycle of Python • Multithreaded Performance, parallelization, and distributed • RevoScaleR package machine learning, supports data science at scale • MicrosoftML package for distributed machine learning • Operationalization functions for deploying to remote servers
- 12. What is SQL Server R Services? • An implementation of Microsoft R Server, optimized for SQL Server • Intended to run R code stored within the database • Supports enterprise-scale data science • Helps you embrace the highly popular open source R language in your business. • R processes execute outside of the database engine • Security is handled by SQL Server Trusted Launchpad
- 13. Set up SQL Server R Services (In-Database) • Step 1: Install R Services (In-Database) on SQL Server 2017 • Step 2: Enable R Service • Step 3: Launchpad Service
- 15. R in SQL Server
- 16. Develop • R Tools for Visual Studio • RStudio
- 17. Running R code from SQL Server • Run R code from SQL Server using the sp_execute_external_script stored procedure • You can: • Run arbitrary R code • Provide input parameters that can be referenced by the R code • Specify an input dataset • Return an output dataset, plot od model
- 18. • New stored procedure EXEC sp_execute_external_script @language = N’R’, @script = N’[R code goes here]’, @input_data_1 = N’[SQL input]’ [ , @input_data_1_name = N‘InputDataSet’ ] [ , @output_data_1_name = N’OutputDataSet’ ] [ , @params = N’parameter’ ] WITH RESULT SETS (([SQL output])); input_data_1_name and output_data_1_name are optional and default to InputDataSet and OutputDataSet respectively Operationalized R
- 19. 1. Transform Data 2. Evaluate data 3. Build model 4. Save model to stored proc. SQL Server 2017 Web App Deploy
- 20. Powerful R Capabilities SQL Server • Meeting the Needs of R and SQL Users With One Platform • R users can: • Load, transform, visualize, learn from data assets in SQL • Create or “train” predictive models • Scale R analytics to big data using SQL Server R Services • Connect to SQL from R Tools for Visual Studio or third party IDEs (R Studio) • Deploy and operationalize applications that use these predictions • SQL Users can: • Embed R to access predictive analytics from SQL • Run R scripts and Modeling algorithms from T-SQL scripts and within stored procedures • Extend R capabilities to data engineers and application developers • Easily embed prediction into BI and custom applications
- 21. Demonstration • Build a predictive model using R and SQL Server ML Services • Ski rental business - predict the number of rentals that we will have on a future date
- 22. Why use R for machine learning? • Statistical analysis comprises three common tasks: • Data transformation • Data visualization • Data modeling • R provides an array of packages to help you perform these tasks • R also provides programming constructs to build a workflow of operations • R is interactive; you can quickly prototype your operations • R packages can be written using compiled languages, for speed • View the results using the Visualize command on the output ports
- 23. Why use Python for machine learning? • Fully-fledged programming language • Portable, and runs on many different operating systems • Frequently used to provide the glue to integrate components developed in different languages • Excellent for transforming data between formats • More complex than R; it supports advanced OO features • Packages developed in other compiled languages can be easily incorporated
- 24. Selecting the appropriate language • R is favored by data scientists because it expresses statistical concepts concisely • R is favored by programmers because it is more general purpose and powerful • R has a broader range of statistical packages available • Python has a more consistent syntax • Both languages can interoperate with each other