MATLAB in Business Critical Applications Arvind Hosagrahara Principal Technical Consultant
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1 MATLAB in Business Critical Applications Arvind Hosagrahara Principal Technical Consultant The MathWorks, Inc. 1
2 Outline Problem Statement The Big Picture / Financial Applications Reference Architectures and integrations TIBCO Spotfire IBM PureData for Analytics (powered by Netezza) Microsoft SQL Server Handling Big Data and out-of-memory problems (Hadoop, NoSQL databases) MATLAB in Business Critical Applications Conclusions 2
3 Problem Statement Agility: Is it possible to accelerate the development of data analytics? Democratization: Is it possible to make the results and insights from these analytics available to all stakeholders in an organization? Plan / Design Code Build / Test Production: Is it possible to build data analytics algorithms in a flexible, scalable manner that is suitable for production usage and rigor? Operate/ Monitor Deploy Release 3
4 Taking MATLAB into production Prototyping Test Production Agility Lightweight processes Visualization Access to data Expressive highlevel language Integration with best-in-class tooling Verification and validation Correctness Performance Automation of testing Test-driven development Performance Scalability Reliability Governance Auditability Maintenance Recoverability Support and Services 4
5 The Big Picture Reference architectures offer solutions to finance industry applications with an emphasis on selection of the right tool for the task leading to efficient workflows. Big Data Frameworks (eg: Hadoop, Databases) Cloud Solutions (eg: Amazon, Azure, PaaS, IaaS) Analytics Platforms (eg: TIBCO Spotfire ) In-house Enterprise Software Visualization (eg: HighCharts JS) Enterprise Messaging Queues (eg: AMQP) 5
6 Finance Industry Applications Energy Trading Load Forecasting Model and Storage Assets Price Energy Options Risk Modeling Portfolio Optimization MathWorks Consultants were wellqualified, professional, and fast. They understood not only the technical issues but also the business goals, which is essential when working on a core business system. We got more than we expected from MathWorks Consulting. - Dr. Norbert Tönder, RWE The tools we developed with MATLAB are more reliable, scalable, and maintainable than our spreadsheet-based approach. We know the tools will work, we can add new capabilities, and we can update the production system without getting IT involved. - Manuel Arancibia, Horizon Wind Energy 6
7 Reference Architecture MATLAB Analytics with TIBCO Spotfire 7
8 MATLAB Analytics with TIBCO Spotfire 8
9 MATLAB Analytics with TIBCO Spotfire Rapidly create analytics for use in desktop, web, and mobile visualizations and dashboards Run MATLAB scripts or programs within MATLAB sessions (small scale) Access MATLAB analytics on MATLAB Production Server (large scale) MATLAB Develop Refine Test MATLAB Production Server MATLAB TIBCO Spotfire Develop MATLAB Refine Analytics Test 9
10 Reference Diagram Mobile Web Desktop MathWorks. MPSExtension TIBCO Spotfire Web Player MathWorks. MPSExtension HTTP(s) MATLAB Production Server MATLAB Analytics TIBCO Spotfire Server 10
11 MATLAB Analytics Library MATLAB analytics available for use in Spotfire clients LargeTableOut SmallTableOut OptionsPrice OptionsPriceShort 11
12 Increasing Capacity and Redundancy Mobile Web Desktop Load Balancer MathWorks. MPSExtension TIBCO Spotfire TIBCO TIBCO Web Spotfire Web Spotfire Player Player Web Player MathWorks. MPSExtension Load Balancer MATLAB Production Server MATLAB Production Server MATLAB Analytics TIBCO TIBCO Spotfire Spotfire Server Server 12
13 Benefits Rapid development and deployment of MATLAB analytics MATLAB Central management of locked down analytics Scalable and reliable architecture MATLAB Compiler SDK MATLAB Production Server MATLAB Analytics available to all business users TIBCO Spotfire Server MATLAB MATLABProduction Server Devel op MATLAB Refin Analytics e Test 13
14 Reference Architecture MATLAB Analytics with IBM PureData / Netezza 14
15 MATLAB Analytics with IBM PureData IBM PureData (Netezza) IBM PureData (Netezza) Off-Database Pure Data ODBC/JDBC Driver On-Database Low Performance Processes (UDAP or AE) High-Performance Function (UDF / UDTF / UDA) ODBC Database Toolbox JDBC Python Java MATLAB MDCS MPS MCR C++ Lua Workflow Compute Cluster MATLAB Compiler MATLAB Coder 15
16 MATLAB Analytics with IBM PureData 16
17 Building and Running the MATLAB Analytics 1 Generate Code and move to the IBM PureData Appliance 2 Compile the generated code into a shared object for the host and the SPU 3 Register the shared object as a library 4 Use the MATLAB analytic in the User-Defined Function / Table Function / Aggregate [ SELECT Histogram(*) ] 17
18 Benefits Rapid development and deployment of MATLAB analytics MATLAB High Performance Analytics written in MATLAB and generated as C++ MATLAB Analytics available to downstream users as a Stored Procedure/Function MATLAB Coder and/or Simulink Coder with Embedded Coder IBM PureData for Analytics powered by Netezza or Microsoft Netezza 18
19 Reference Architecture MATLAB Analytics with Microsoft SQL Server 19
20 MATLAB Analytics with Microsoft SQL Server Microsoft SQL Server Off-Database ODBC/JDBC Driver On-Database Processes Transactions ODBC JDBC C# Common Language Runtime(CLR) Database Toolbox.NET Assembly MATLAB Component Runtime MPS Client Library MATLAB MATLABProduction Server Devel op MATLAB Refin Analytics e Test MATLAB Compiler SDK 20
21 MATLAB Analytics with Microsoft SQL Server 1 2 MATLAB MATLABProduction Server Devel op MATLAB Refin Analytics e Test 3 21
22 Benefits Rapid development and deployment of MATLAB analytics MATLAB Analytics expressed in MATLAB with nearly all available toolbox functionality MATLAB Analytics available to downstream users as a Stored Procedure/Function MATLAB Compiler SDK MATLAB Production Server Microsoft SQL Server MATLAB MATLABProduction Server Devel op MATLAB Refin Analytics e Test 22
23 Big Data with MATLAB 23
24 Techniques for Big Data in MATLAB Load, Analyze, Discard parfor, datastore, MapReduce Distributed Memory SPMD and distributed arrays out-of-memory in-memory Embarrassingly Parallel Non- Partitionable Complexity 24
25 Big Data Capabilities in MATLAB Memory and Data Access 64-bit processors Memory Mapped Variables Disk Variables Programming Constructs Databases Streaming Datastores Block Processing Parallel-for loops GPU Arrays SPMD and Distributed Arrays MapReduce Platforms Desktop (Multicore, GPU) Clusters Cloud Computing (MDCS on EC2) Hadoop 25
26 MATLAB in Business Critical Applications 26
27 Conclusions: MATLAB Analytics in Production Agility: Yes. Democratization: Yes. Production Quality: Yes. Access and explore data from within MATLAB during prototype development Develop and integrate analytics with enterprise systems Compact, precise language for expression of complex analytics High-quality libraries and support Open and extensible platform for analytics Data Visualization: TIBCO Spotfire, Hadoop: Apache Data warehouses: IBM PureData, Microsoft SQL Server MATLAB products and Services provide a single-stack solution when used with supporting technologies to address production data analytics demands. 27
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