Microsoft SQL Server 2016 R Services

Transcription

1 Microsoft SQL Server 2016 R Services

2 SQL Server 2016: Everything built-in built-in built-in built-in built-in built-in $2, SQL Server SQL Server SQL Server #1 #2 #3 Oracle is #5 $480 $120 Microsoft Tableau Oracle SQL Server Oracle MySQL SAP HANA TPC-H Self-service BI per user at massive scale In-memory across all workloads Consistent experience from on-premises to cloud 2

3 從資料到決策和行動 資料 $1.6 trillion 决策 行動 價值

4 微軟先進分析產品 Cortana Analytics Suite SQL Server 2016

5 典型先進分析的生命週期 準備 Modeling Model Ingest Transform Explore Model Deploy ƒ(x) 投入生產 Score Score Visualize Measure

6 資料科學家應該是關注創建 / 測試模型 Data scientist 準備 Modeling Model Ingest Transform Explore Model Deploy ƒ(x) 投入生產 Score Score Visualize Measure

7 但現實是... Data scientist focus time 準備 80% 15% Modeling Model Ingest Transform Explore Model Deploy ƒ(x) 5% 投入生產 Score Score Visualize Measure

8 先進分析是一項團隊運動 model 決定 Preparation 投入生產

9 什麼是 R? 開源 lingua franca Global community Ecosystem Big data Analytics, computing, modeling Millions of users 7,000+Packages Scalability

10 CRAN: The Comprehensive R Archive Network Open Source lingua franca Analytics, Computing, Modeling In addition to CRAN, Bioconductor, GitHub, and others distribute R packages

11 為什麼 R? 大量人才知道如何使用 角色使用 創建效率 更容易 保護重要的資料 可擴充正在進行計算的資料

12 開源 R 的挑戰 $? Uncertain total cost of ownership and return on investment Integrating R with existing and ever changing data infrastructures Scale and Performance Data movement restricts access for efficient data modeling

13 微軟 R 的好處 開源 微軟 R 好處 Big Data In-memory bound Hybrid memory & disk scalability Operates on bigger volumes & factors Speed of Analysis Enterprise Readiness Analytic Breadth & Depth Commercial Viability Single threaded Parallel threading and Processing Shrinks analysis time Community support Commercial support Delivers full service production support innovative analytic packages Risk of deployment of open source Leverage and optimize open source packages plus Big Data ready packages Commercial licenses Supercharges R Eliminates risk with open source

14 Faster And More Scalable

15 Parallelized, Remote Executing Algorithms Data step Data import Delimited, fixed, SAS, SPSS, OBDC Variable creation & transformation Recode variables Factor variables Missing value handling Sort, merge, split Aggregate by category (means, sums) Descriptive statistics Min/max, mean, median (approx.) Quantiles (approx.) Standard deviation Variance Correlation Covariance Sum of squares (cross-product matrix for set variables) Pairwise cross tabs Risk ratio & odds ratio Cross-tabulation of data (standard tables & long form) Marginal summaries of cross tabulations Statistical tests Chi Square Test Kendall Rank Correlation Fisher s Exact Test Student s t-test Sampling Subsample (observations & variables) Random sampling Predictive models Sum of squares (cross-product matrix for set variables) Multiple linear regression Generalized linear models (GLM) exponential family distributions: binomial, Gaussian, inverse Gaussian, Poisson, Tweedie. Standard link functions: cauchit, identity, log, logit, probit. User defined distributions & link functions. Covariance & correlation matrices Logistic regression Classification & regression trees Predictions/scoring for models Residuals for all models Custom parallelization PEMA-R API rxdatastep rxexec Simulation Simulation (e.g., Monte Carlo) Parallel random number generation K-Means Cluster analysis Classification Decision trees Decision forests Gradient-boosted decision trees Naïve Bayes

16 In-database advanced analytics Extensibility Example solutions Real-time operational analytics without moving data Data Scientist Interacts directly with data R integration? Sales forecasting Warehouse efficiency Predictive maintenance Credit risk protection Analytics library R with in-memory scalability SQL Developer/DBA Manage data and analytics together T-SQL interface Relational data Built into SQL Server 2016

17 minutes External Access In Database rows

18 Flexibility & Agility 寫一次部署在任何地方 No model re-writes across platforms No re-writes from modeling to scoring Hybrid modeling & scoring Model on premises, score on premises Model on premises, score in the cloud Model on cloud, score on premises Prepare Model Parallelized Models SQL Server Score

19 微軟 R 部分的客戶 Financial Services Digital Media & Retail Manufacturing & High Tech Healthcare & Pharma Government & Academia Analytics Service Providers

20 SQL Server 2016 R Services ( In-database) $ In-DB analytics Parallel threading and processing Easy to operationize Developers, DBAs and Data Scientists can use their preferred tools Model on-premises, score in cloud or vice versa Easy way to overcome memory limitations -enabling limits of larger data sets Included in SQL Server 2016 Reuse and optimization of existing R code Reduced recoding and training costs

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