ETL challenges on IOT projects. Pedro Martins Head of Implementation

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1 ETL challenges on IOT projects Pedro Martins Head of Implementation

2 Outline What is Pentaho Pentaho Data Integration (PDI) Smartcity Copenhagen Example of Data structure without an OLAP schema Telematics Many-to-many Caterpillar Predictive Analytics

3 Pentaho

4 EXABYTES OF DATA At the Beginning of a Data Revolution We are ONLY here! 40% Machine Data 50X Growth 77% of data relevant to enterprises will be unstructured

5 New Technology Paradigms have Emerged The universe of data is exploding and data is connecting people and things New data architectures must blend and analyze all data regardless of source Users will consume analytics in new ways: data driven apps

6 WHAT WE BELIEVE Big data is completely altering the market requirements for analytics Even big data needs to play well with others. The most powerful insights come from blending data on demand and at the source. You can t predict tomorrow with yesterday s tools.

7 The Opportunity for Big Data is greatest when it can seamlessly connect to the rest of the data in your organization EXABYTES BIG DATA PETABYTES WEB TERABYTES CRM GIGABYTES TRANSACTIONS ERP INTERACTIONS INCREASING DATA VARIETY AND COMPLEXITY

8 Pentaho Enabling the Data Driven Business Modern, cohesive data integration and business analytics platform Full spectrum of advanced analytics for all key roles Embeddable, cloud-ready analytics Big data blending for analytics in real-time environments Broadest and deepest big data integration

9 Pentaho Enabling the Data Driven Business Innovation through open source Open, pluggable, purpose-built for the future Early sustained leadership in big data ecosystem with technology innovation Critical mass achieved Over 1,200 commercial customers Over 10,000 production deployments

10 Our Commitment A future proof data foundation Big (all) data blending without boundaries Powerful analytics for the next generation

11 Our Differentiation Modern Unified Data Integration and Business Analytics Platform 2014, Pentaho. All Rights Reserved. Pentaho.com Worldwide +1 (866)

12 Pentaho Brings Data Together Platform Supports Evolving Big Data Architectures Network Location Web Existing Process or Pentaho NoSQL Hadoop Cluster Data Integration Analytic DB Analytics Social Media On-Demand Integration & Blending Customer Provisioning Existing ETL Tool Or Pentaho Data Integration Billing EDW Existing ETL Tool or PDI Data Marts BI Tools

13 Data Integration

14 Pentaho Data Integration Data-agnostic solution for ETL, orchestration, blending, and more What is PDI? Graphical user interface to create drag-anddrop data transformation pipelines Data integration engine that is powerful, portable, and scalable Software that can be easily extended and embedded into other applications

15 Integrate ALL Data in an Intuitive Way A user-friendly graphical interface to build complete data 100+ Transformation Steps Drag & drop Development 100% GUI-based Configuration Model, Analyze, and Visualize as you go

16 Integrate ALL Data in an Intuitive Way Visual Big Data integration that speeds time to value and reduces risk Visual design for ETL & MapReduce jobs, eliminating complex Java coding MapReduce executed natively in cluster, 15x faster than hand-coding YARN enables elastic resource usage & Pentaho ETL clusters on-demand End-to-end no-code data integration & analytics solutions for MongoDB Supports Cassandra, Hbase, and more Easy integration to analytical DBs & more Adaptive Big Data Layer Reduces risk and expands choice by insulating your architecture from changing technology and vendors

17 SmartCity Copenhagen Hitachi Consulting (HCC) + Pentaho

18 The Idea Smart City Combine multiple information and communication technologies to manage and optimize a city s assets, namely water, energy consumption and emissions Pentaho Create an abstract common data layer and present it using custom, rich and interactive screens Develop a platform to inform the user and additional stakeholders about consumption and also the possibility of sharing the data and competing with friends

19 The Concept Three distinct user types for the application: Anonymous Citizen Primary Secondary Business and City Screen flow and information displayed are controlled via user type

20 The Project Hitachi created: Data model Stored Procedures Pentaho created: PDI transformations Dashboard screens Identity Server Dec 2015 Kickoff Jan 2016 Design approved Feb Dummy data March Real data May 2016 Go-live

21 Methodology Design Design Workshop (UX) Technical Workshop Setup Install servers (Pentaho, etc.) Install Databases Set up JIRA, Version Control Implementation ETL Dashboards Write queries Prepare QA UAT Run QA tests Allow access to a selected number of customers Evaluate performance Deploy Promote Prod environment Documentation Conduct Knowledge Transfer session for the Support teams Support Enable plan for L1, L2, L3 + L4 support Review what went well, not so well Any other business (AOB)

22 The Team Backend: 3 Pentaho staff 1-2 FTE UX team: 2 Pentaho staff 0.5 FTE Endpoints: 3 HCC staff 1.5 FTE Testing: 2 HCC staff 2 FTE Frontend: 2 Pentaho staff 1-2 FTE

23 Interesting notes and challenges PDI used both as a layer to obtain and persist data Smart parameterization of PDI transformations where the same transformation does slightly different tasks based on parameter inputs On the same transformation, fetch data from a database, access a REST endpoint, parse JSON output and validate data using JavaScript Security required interaction with an authentication endpoint (REST call) where a security token was obtained and needed to persist data Dashboard flow and linkage different per User type using the same set of dashboards.

24 Architecture Pentaho BA Server Electricity Company RDBMS API REST/ SQL Pentaho Data Integration AJAX CTools REST Pentaho Data Integration SQL Mondrian (OLAP) + DSP Analyzer

25 Solution description Two different solutions: 1. Standard analytical solution: CTools dashboards using PDI as a data source. PDI used SQL steps to retrieve data from Database 2. Transactional screens: CTools screens displaying analytical data and providing input screens to provide the settings screen for the application. PDI was used to retrieve and send information against a custom API exposed via stored procedures

26 Standard Analytical solution (case 1) CTools dashboards use a data abstraction layer (CDA) All data comes in JSON CDA makes the calls to the right service, usually: MDX queries against Mondrian, when ROLAP cubes and a schema are defined SQL directly against a RDBMS PDI (which connects via JDBC, REST or file access) We ll see a demo of a dashboard using a PDI source

27 Operational Analytics (case 2) All as before, taking into account that PDI can run transformations to do more than just retrieving data Dashboards have input screens which drive JS parameters: Parameters are collected and sent into PDI PDI can insert/update values into the operational system, instead of just retrieving information Settings can be read via a standard PDI query on dashboard load

28 Functional Data Model USER * ADDRESS * METER * READING * FRIENDS A User represents a real person registered in the system A User can friend other Users and compete with them A User can create several Addresses An Address can have several Meters A Meter has several Readings

29 ETL

30 Demo Dashboard with PDI source

31 Welcome screen

32 Account screen

33 Register screen

34 Pros/cons Smartcity Copenhagen Strengths PDI-based backend brought speed of development Inputs can be driven to operational DB Weaknesses By not using OLAP, all security needs to be explicit in all queries Pentaho BA server was not tested for operational purposes (not an ERP nor CRM tool) Opportunities Migrate the RDBMS internal schemas into OLAP schemas Work with Product Management to improve the BA server for massive user input Threats Development on top of PDI dummy data masks eventual data problems Hackers may control inputs to DB

35 Telematics use case Many-to-many challenges

36 About this Telematics Customer Telematics provides SaaS to assist customers manage fleets of vehicles. Devices are wired into the onboard computers of vehicles (OBD) and transmit GPS and sensor data over a mobile network. Benefits include (not all are applied now): Determine location and route of the vehicle View vehicle performance metrics including engine diagnostics Monitor driver behavior Route vehicles to save time and fuel Monitor unsafe conditions such as speeding, hard breaking and unauthorized use And this is all visible in near real time

37 Enterprise Vehicle Fleet Management Business Challenge Needed a scalable solution to extend driver behavior and location intelligence to customers Deliver multi-tenant embedded analytics including near real-time capabilities Pentaho Benefits Revenue-generating OEM deployment enabling fast response to safety and breakdown risks Accelerated development time and flexibility to ingest and blend emerging data sources

38 Enterprise Vehicle Fleet Management Vehicle Sensor Data Custom ETL Process Real-time Query Analytics Embedded in SaaS Application External Fleet Managers Driver & Customer Data Geo-location Data Business Analytics Internal Users

39 Interesting notes and challenges Three data streams: real-time (RT), Pseudo/quasi real-time, analytical Project started with analytical Backend was Oracle-based, with a strong hint that it will migrate to Hadoop in the near future Several fleets could be used, with several vehicles. A driver could drive many vehicles and vice-versa Dimension Fact Dimension Dimension Dimension Fact Dimension Dimension

40 The solution for many-to-many Several approaches were taken: Create separate cubes for Vehicles and Vehicle Groups Force a single selection for Vehicle Groups In situations where only vehicle utilization per day was needed, simple rules were created: If any driver touches this car today, then its utilization is 100% for that day While other OLAP tools (such as MS Analysis services) support manyto-many, none of them have the right performance for big cardinalities Many-to-many relationships are avoid in industry, for any tool

41 Monetize My Data Marine Asset Intelligence

42 Marine Asset Intelligence Business Challenge Create predictive forecasts about customer behavior to increase product value Efficiently govern and present data to end users to promote self-service analytics Pentaho Benefits Significant time and cost savings for endusers who could not develop dashboarding capabilities on their own Managed ETL improving security and job oversight to ensure data quality and integrity

43 Marine Asset Intelligence Fleet Data via Satellite Data Integration Data Integration Data Marts Data Scientist Local Machine & Server Data Business Users Analytics Server Cross Department Data End Users

44 Summary Pentaho provides many out-of-the-box features to perform ETL in a quick and reliable manner Data modelling on top of structured data will continue in the future, but it must play well with Big Data BI tools are migrating to a point where all sources of data are blended into one Data Lake, using Big Data technology Analytics are moving towards Predictive, Prescriptive and Preemptive modes

45 Questions and Discussion

46 Thank You

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