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Denodo DataFest 2016: The Role of Data Virtualization in IoT Integration


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Watch the full session: Denodo DataFest 2016 sessions:

Connected use cases are gaining momentum! Data integration is the foundation for enabling these connections. In this session, you will experience first-hand our customer case studies and implementation architectures of IoT solutions.

In this session, you will learn:

• The role of data virtualization in enabling IoT use cases
• How our customers have successfully implemented IoT solutions using data virtualization
• How our product complements other IoT technologies

This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here:

Published in: Data & Analytics
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Denodo DataFest 2016: The Role of Data Virtualization in IoT Integration

  1. 1. O C T O B E R 1 8 , 2 0 1 6 S A N F R A N C I S C O B A Y A R E A , C A #DenodoDataFest RAPID, AGILE DATA STRATEGIES For Accelerating Analytics, Cloud, and Big Data Initiatives.
  2. 2. The Role of Data Virtualization in IoT Integration Lakshmi Randall Head of Product Marketing, Denodo Twitter: @LakshmiLJ
  3. 3. Agenda 1.What’s so important about IoT Integration? 2.How does Denodo support IoT Data Integration? 3.Customer Case Study 3
  4. 4. IoT is invading our kitchen! 4
  5. 5. What’s so important about IoT Integration? 5
  6. 6. The Importance of IoT Integration  Investment in IoT devices is soaring  IoT is proliferating across all business and consumer sectors  Data generated in the IoT offers a Data Monetization Model 6
  7. 7. IOT Investment  2016: IoT hardware purchases surpass $2.5 million per minute.  2021: one million IoT devices procured and installed per hour. IoT Proliferation  2018: 6 billion connected devices require support.  2020: >21 billion connected devices in operation.  2020: industry-specific devices number 2.9 billion (nearly 200% growth since 2015). IoT Investment & Proliferation Milestones 7 Source: Gartner 2016
  8. 8. IoT Monetization  Enhance traditional products with sensors and connectivity  Offer bundled services for connected things (e.g., connected cars)  Collect, Aggregate, Anonymize and Monetize. 8
  9. 9. IoT Use Cases 9 Preventative & Proactive Maintenance Data Monetization (information- oriented products & services) Customer Satisfaction & Retention Operational Efficiency (asset & equipment optimization) Safety Security Fraud Detection Real-time Analytics Patient care
  10. 10. IoT Endpoints for 2020 by Sector 10 Source: Gartner, April 2016
  11. 11. How does Denodo support IoT Data Integration? 11 Data-in-transit and Data-at-rest
  12. 12. Big Data Connectivity BigData and Cloud Databases Connectivity ■ Hadoop Ecosystem: ■ SQL on Hadoop: Hive, Impala, Presto,… ■ HDFS, Parquet, Avro, CSV… ■ Execution of map/reduce Jobs ■ Certified with major Hadoop distributions ■ In-memory platforms: Apache Spark, Presto DB, HANA,… ■ Parallel DWs and Appliances: Vertica, Impala, Teradata, Greenplum,… ■ Cloud RDBMS: Redshift, Snowflake, DynamoDB,… ■ NoSQL (MongoDB, CouchDB, Neo4J, Redis, Oracle NoSQL, Cassandra, etc.) ■ Streaming data (Spark streams, Splunk, IBM Streams, Kafka,…) 12 Enhanced Adapters for Big Data ecosystem
  13. 13. 13 Request-Response: Named adapters for stream services:  Kafka  IBM Streams Streaming: Extend current JMS support with:  Enhanced support for temporary windows  Support for MQTT Enhanced Integration with IoT - Streaming Enhanced Adapters for the Internet of Things Ecosystem JMS MQTT JMS MQTT
  14. 14. Data Ingestion ■ Batch, On-demand and Streaming Data Ingestion ■ Simultaneously supports Batch and Streaming data integration ■ Learns to extract structured data from semi-structured content using Machine Learning ■ Ingest the data in a schema-agnostic way including schema-on-read and multiple schemas 14 Batch, On-demand and Streaming Data Ingestions
  15. 15. Enrich Machine Data and Combine with Other Data Ingest, Integrate & Deliver Persisted (In-memory, Hadoop) Streams (specific time window) Message Queue Machine-generated/Event data Alerts Workflows Operational Processes Analytical Processes Consumers Visualization Data Virtualization Enrich and Combine IoT Data with Other Data Historians Streams ERP/SCM DW Analytical DB MDM Apps Data Marts Hadoop NoSQL
  16. 16. 16 Security Data in Motion – secure channels • Using SSL/TLS • Client-to-Denodo and Denodo-to-source • Available for all protocols (JDBC, ODBC, ADO.NET and WS) Data at Rest – secure storage • Cache: third party database. Can leverage its own encryption mechanism • Swapping to disk: serialized temporarily stored in a configurable folder that can be encrypted by the OS Encryption/Decryption and Data Masking • Support for custom decryption for files and web services • Transparent integration with RDBMs encryption Authentication and Authorization • LDAP/AD, Kerberos support, Granular data security, Securing data
  17. 17. Customer Case Study 17
  18. 18. Leading Construction Manufacturer - Telematics & Predictive Maintenance Dealer Maintenance Parts Inventory OSI PI Hadoop Cluster Tableau: Dealer / Customer Dashboard
  19. 19. Business Benefits  Improved asset performance and proactive maintenance.  Reduced warranty costs due to proactive maintenance of parts preventing parts failure.  Optimized pricing for services and parts among global service providers.  New Business Model opportunities based on real-time analysis of detailed sensor data.
  20. 20. Data Virtualization Benefits Implement a Single Logical Data Lake Using Data Virtualization Improves the enterprise func- tionality of data lakes by combining one or more physical data lakes with other enterprise data Provides a way to access data from separate systems through an abstraction layer that makes it appear as if the data were in a single data lake Improves an organization’s ability to govern and extract more value from its data lakes by extending them as logical data lakes 20
  21. 21. Key Takeaways  Identify if and how IoT data will benefit your organization  Identify your potential IoT Data sources  Employ Data Virtualization to combine IoT data with other data to enhance the use and value of data assets  Employ a Logical Data Lake/Logical Data Warehouse architecture to eliminate the cost of storing information in multiple places, to govern IoT data access, and to prevent IoT data from becoming siloed. 21
  22. 22. Q&A
  23. 23. Thank you! © Copyright Denodo Technologies. All rights reserved Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm, without prior the written authorization from Denodo Technologies. O C T O B E R 1 8 , 2 0 1 6 S A N F R A N C I S C O B A Y A R E A , C A #DenodoDataFest