Build Intelligent Microservices via Machine Learning and Big Data Analytics

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Kai Wähner Technology Evangelist kontakt@kai-waehner.de LinkedIn @KaiWaehner www.kai-waehner.de O’Reilly Software Architecture Conference 2016 (London, UK) How to apply big data analytics and machine learning to real-time processing of microservice events

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© Copyright 2000-2016 TIBCO Software Inc. Key Take-Aways Ø Insights are hidden in Historical Data on Big Data Platforms Ø Machine Learning and Big Data Analytics find these Insights by building Analytics Models Ø Event Processing uses these Models (without Redevelopment) to take Action in Real Time

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Machine Learning …. allows computers to find hidden insights without being explicitly programmed where to look.

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Real World Examples of Machine Learning Spam Detection Search Results + Product Recommendation Picture Detection (Friends, Locations, Products) Machine Learning is already present in daily life… Now, every enterprise is beginning to leverage it! The Next Disruption: Google Beats Go Champion

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Decision Tree – Product Pass / Fail by Equipment Sensor Readings Bad Product Good Product Step 8 Temperature < 122 C >= 122 C Step 2 Recipe A B Step 11 Pressure TV Color Display Problem

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Decision Tree – Training and Test Data Sets

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© Copyright 2000-2016 TIBCO Software Inc. Ensemble Tree Algorithms • Random Forest, Gradient Boosting Machine (GBM) • Method – Average many simple trees • Sample the data: fit a simple tree • Re-sample the data; up-weighting the observations that weren’t fitted well in previous model • Continue adding trees until fit is good • Save all the trees and average them • Better fit + prediction than single trees

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© Copyright 2000-2016 TIBCO Software Inc. Analytics Maturity Model Immediate Long-Term Competitive Advantage Value to the Organization A good Big Data Analytics platform can provide value to the organization across the full spectrum of use cases Self-service Dashboards Event Processing Advanced Analytics Measure Diagnose Predict Optimize Alert Automate Analytics Maturity Visual Analytics Event Processing Analytics

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© Copyright 2000-2016 TIBCO Software Inc. Analytics Maturity Model Immediate Long-Term Competitive Advantage Value to the Organization Visual Analytics Event Processing Advanced Analytics Measure Diagnose Predict Optimize Alert Automate Analytics Maturity A good Big Data Analytics platform can provide value to the organization across the full spectrum of use cases Analytics

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© Copyright 2000-2016 TIBCO Software Inc. Analytics Maturity Model Immediate Long-Term Competitive Advantage Value to the Organization Self-service Dashboards Event Processing Advanced Analytics Measure Diagnose Predict Optimize Alert Automate Analytics Maturity A good Big Data Analytics platform can provide value to the organization across the full spectrum of use cases Visual Analytics Event Processing Analytics

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© Copyright 2000-2016 TIBCO Software Inc. From a Business Case to Proactive Actions Model Present Data Wrangling Signals Dashboards SAP Historian Production Well Filter Enrich Merge Shape Explore Clean Assemble Data Business Case Increase Productivity Grow Revenue Completions Visualize GeoLocation Production Value Theses Reduce Risk G&G Equipment Decision, Action Prediction Action Develop Model Pressure Temperature Production Interrupt Drill Bit Movement Equipment Failure

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What is Predictive Analytics?

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© Copyright 2000-2016 TIBCO Software Inc. Variety of Data in Enterprises Custom GUI-driven data access via SDK Siebel eBusiness Local data sources Access Excel STDF Drag-and-drop MySQL SQL Server Oracle Information Services (join, transform, reusable, parameterized, dynamic query for in-memory use) Databases JDBC/ODBC Hadoop SFDC PostgreSQL Teradata Netezza Etc. XML RDBMS Flat Files Spread- sheets Web Services Oracle E-Business RDBMS RDBMS RDBMS SAP BW SAP R/3 D A T A F A B R I C Salesforce ODBC OLE DB SqlClient Direct connection Oracle TeradataAster MS SSAS Teradata Direct Query (dynamically query and retrieve data for visualization and analysis) Databases MySQL Etc. OBIEE Netezza Hadoop

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cust_id dept sku dollar gift date 1 104 C 12003 2.40 FALSE 2016-10-17 2 105 A 12005 62.85 FALSE 2016-10-17 3 102 C 12007 69.23 TRUE 2016-10-17 4 104 B 12004 9.33 FALSE 2016-10-18 5 105 C 12010 14.16 TRUE 2016-10-18 6 101 B 12003 90.43 FALSE 2016-10-19 7 103 C 12005 90.97 FALSE 2016-10-19 n … … … … … … cust_id A B C total # orders first_dat e last_dat e 1 100 21.76 23.67 0.00 45.43 2 2016-10- 19 2016-10- 20 2 101 0.01 74.65 0.00 74.66 3 2016-10- 19 2016-10- 20 3 102 0.00 60.92 50.29 111.21 6 2016-10- 17 2016-10- 20 4 103 0.00 0.00 52.30 52.30 2 2016-10- 19 2016-10- 20 © Copyright 2000-2016 TIBCO Software Inc. Data Munging - Transformations

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Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) 1. to maximize insight into a data set 2. uncover underlying structure 3. extract important variables 4. detect outliers and anomalies 5. test underlying assumptions 6. develop parsimonious models 7. determine optimal factor settings © Copyright 2000-2016 TIBCO Software Inc. Exploratory Data Analysis

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What is Predictive Analytics?

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© Copyright 2000-2016 TIBCO Software Inc. “…as a next-generation data discovery capability that automatically finds and explains insights from advanced analytics to business users or citizen data scientists” Smart Data Discovery (for the Business User) Leverage Machine Learning without the help of a Data Scientist

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Advanced Analytics and Big Data Tools (for Data Scientists) Many more ….

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R with Revolution Analytics (now Microsoft) © Copyright 2000-2016 TIBCO Software Inc. Open Source GPL License (including its restrictions) http://www.revolutionanalytics.com/webinars/introducing-revolution-r-open-enhanced-open-source-r-distribution- revolution-analytics

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TIBCO has rewritten R as a Commercial Compute Engine • Latest statistics scripting engine: S a S-PLUS® a R a TERR • Runs R code including CRAN packages Engine internals rebuilt from scratch at low-level • Redesigned data objects, memory management • High performance + Big Data TERR is licensed from TIBCO • TERR Installs (free) with Spotfire Analyst / Desktop + other TIBCO products • Spotfire Server can manage all TERR / R scripts, artifacts for reuse • Standalone Developer Edition • Supported by TIBCO • No GPL license issues © Copyright 2000-2016 TIBCO Software Inc. TERR - TIBCO’s Enterprise Runtime for R

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Apache Spark MLlib © Copyright 2000-2016 TIBCO Software Inc. Spark ML is Spark’s machine learning library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering and collaborative filtering. General Data-processing Framework à However, focus is especially on Analytics (at least these days) x

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© Copyright 2000-2016 TIBCO Software Inc. H2O.ai An Extensible Open Source Platform for Analytics • Best of Breed Open Source Technology • Easy-to-use Web UI and Familiar Interfaces • Data Agnostic Support for all Common Database and File Types • Massively Scalable Big Data Analysis • Real-time Data Scoring (“Nanofast Scoring Engine”) http://www.h2o.ai/

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TIBCO Spotfire with R / TERR Integration © Copyright 2000-2016 TIBCO Software Inc. Let the business user leverage Analytic Models (created by the Data Scientist) to find insights! Example: Customer Churn with Random Forest Algorithm • ‘refresh model’ button lives a ‘random forest algorithm’ • requires no a priori assumptions at all, it just always works • The business user doesn’t need to know what random forest is to be empowered by it Select variables for the model

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© Copyright 2000-2016 TIBCO Software Inc. SaaS Machine Learning • Managed SaaS service for building ML models and generating predictions • Integrated into the corresponding cloud ecosystem • Easy to use, but limited feature set and potential latency issues if combined with external data or applications http://docs.aws.amazon.com/machine-learning/latest/dg/tutoria

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© Copyright 2000-2016 TIBCO Software Inc. PMML (Predictive Model Markup Language ) • XML-based de facto standard to represent predictive analytic models • Developed by the Data Mining Group (DMG) • Easily share models between PMML compliant applications (e.g. between model creation and deployment for operations)

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What is Prescriptive Analytics?

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© Copyright 2000-2016 TIBCO Software Inc. Operational Intelligence and Human Interaction Actions by Operations Human decisions in real time informed by up to date information 65 Automated action based on models of history combined with live context and business rules Machine-to-Machine Automation

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© Copyright 2000-2016 TIBCO Software Inc. What Kind of Streaming Analytics do you need? Visual IDE (Dev, Test, Debug) Simulation (Feed Testing, Test Generation) Live UI (monitoring, proactive interaction) Maturity (24/7 support, consulting) Integration (out-of-the-box: ESB, MDM, etc.) Library (Java, .NET, Python) Query Language (often similar to SQL) Scalability (horizontal and vertical, fail over) Connectivity (technologies, markets, products) Operators (Filter, Sort, Aggregate) Time to Market Streaming Frameworks Streaming Products Slow Fast Streaming Concepts

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© Copyright 2000-2016 TIBCO Software Inc. Comparison of Stream Processing Frameworks and Products Slide Deck from JavaOne 2015: http://www.kai-waehner.de/blog/2015/10/25/ comparison-of-stream-processing-frameworks-and-products/ Updated slide deck coming in November 2016 (Big Data Spain, Madrid)

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© Copyright 2000-2016 TIBCO Software Inc. Visual Coding for Streaming Analytics • Streaming Operators • Connectivity • Visual Development • Testing & Simulation • Mature Tooling / Support • Middleware Integration

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• Reactive – Run to failure • Preventive – Scheduled service (reliability) • Condition-based – Monitor condition (sensors) • Predictive – Predict failures • Proactive – Deploy automatic actions Evolution of Equipment Maintenance Strategies

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Scenario: Predictive Scrapping of Parts in an Assembly Line Goal: Scrap parts as early as possible automatically to reduce costs in a manufacturing process. Question: When to scrap a part in Station 1 instead of doing re-work or sending it to Station 2? Station 1 Station 2 Cost Before 9€ 7€ 13€ Total Cost 29€ (or more) Scrap? Scrap?

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Fast Data Architecture for Predictive Maintenance Operational Analytics Operations Live UI CSV Batch JSON Real Time XML Real Time Streaming Analytics Action Aggregate Rules Analytics Correlate Live Datamart Continuous query processing Alerts Manual action, escalation HISTORICAL ANALYSIS Data Scientists Flume HDFS Spotfire R / TERR HDFS Hadoop (Cloudera) StreamBase TIBCO Fast Data Platform H2O Oracle RDBMS Avro Parquet … PMML Internal Data

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TIBCO Spotfire with H2O Integration Data Discovery / Data Mining (“Are parts that repeat a station more likely scrap parts?”)

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TIBCO Live Datamart Operational Intelligence (“Monitor the manufacturing process and change rules in real time!”) Live Dartmart Desktop Client

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TIBCO Live Datamart Operational Intelligence (“Monitor the manufacturing process and change rules in real time!”) Live Dartmart Web API

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TIBCO Spotfire + StreamBase + H2O.ai + Live Datamart Live Demo Live Demo

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© Copyright 2000-2016 TIBCO Software Inc. TIBCO Accelerator for Apache Spark 1. Fast Data Preparation for IoT Dozens of enterprise and IoT data preparation adapters: MQTT, Databases; inbound creation of HDFS, Parquet, Hbase, Avro… 2. Spotfire Model Discovery Template Use Spotfire to explore Spark data lake, create predictive model, train in H20, and deploy to Streaming Analytics. 3. Operationalize Predictive Models Zookeeper deployment to StreamBase nodes living in Spark cluster via H20, PMML, TERR models 4. Streaming Analytics for Automation Automate action based on predictive models – make offers to customers, stop fraudulent transactions, alert. 5. Monitor & Retrain Model Monitor behavior of model, retrain when necessary. 6. Drag & Drop for Business Solution Developers Code-free development environment for work with H20, HDFS, Avro, TERR The TIBCO Accelerator for Spark is a TIBCO engineered, light-weight open-source fast- start for systems to stream data into Spark, discover patterns in Spark with Spotfire, and operationalize the insights on Big Data. FUNCTIONAL COMPONENTS

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© Copyright 2000-2016 TIBCO Software Inc. 12 Factor Apps for Cloud Native Microservices Codebase One codebase tracked in revision control, many deploys. Dependencies Explicitly declare and isolate dependencies. Config Store config in the environment. Backing Services Treat backing services as attached resources. Build, Release, Run Strictly separate build and run stages. Processes Execute the app as one or more stateless processes. Port Binding Export services via port binding. Concurrency Scale out via the process model. Disposability Maximize robustness with fast startup and graceful shutdown. Dev / Prod Parity Keep dev, staging, and prod as similar as possible. Logs Treat logs as event streams. Admin Processes Run admin/mgmt tasks as one-off processes. https://12factor.net/

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© Copyright 2000-2016 TIBCO Software Inc. Why Containers? http://www.slideshare.net/andersjanmyr/docker-the-future-of-devops Containers enable: • Lightweight deployment • Automation • Better resource utilization • Scaling up and down quickly • Platform agnostic deployment • Innovation and Fail Fast Concepts • Standardization ? Ø The Open Container Initiative (OCI) Ø Docker Fork Discussions (!!!)

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© Copyright 2000-2016 TIBCO Software Inc. DevOps Elements – Culture and Technology! Process Tools Automation Culture Continuous Integration/ Continuous Development APIs Microservices Frequent releases Collaboration

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© Copyright 2000-2016 TIBCO Software Inc. Real Time Streaming Analytics time 1 2 3 4 5 6 7 8 9 Event Streams Apply your intelligent (micro)service to any event. Microservice event. Application event. Legacy event. IoT event. You name it.

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Questions? Please contact me! Kai Wähner Technology Evangelist at TIBCO kontakt@kai-waehner.de @KaiWaehner www.kai-waehner.de LinkedIn