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Webinar: Evolution of Data Management for the IoT

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The Internet of Things is here - slowly, unevenly and in vertical markets, creating new data silos. Evolving data management will be required to realize the full potential of the IoT. There are specific needs for properly addressing IoT data, which legacy ETL tools and database management systems simply don't handle well.

This presentation is from a webinar with Joseph A. di Paolantonio, industry expert working at the convergence of IoT with data management and analytics, where he discusses five recommendations on gaining advantage through the latest IoT data management technologies and business processes.

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Webinar: Evolution of Data Management for the IoT

  1. 1. Evolution of Data Management for the Internet of Things
  2. 2. Today’s Speakers Shayne Hodge Data Scientist SnapLogic Joseph A. di Paolantonio IoT Industry Expert DataArchon.com
  3. 3. Anything apps | APIs | things | data SnapLogic: Unified Platform for Data and Application Integration Anytime batch | streaming | real-time Anywhere on premises | cloud | hybrid
  4. 4. SnapLogic Architecture: Components Self-Service User Interface Cloud-Based Designer,Manager, Dashboard Snaplex Execution Grid; Elastic Scale-Out; Respects Data Gravity Snaps 400+ Pre-Built Connectors Cloudplex Groundplex Hadooplex
  5. 5. SnapLogic and the IoT • Rapidly integrate data from connected devices and other sources without coding • Streaming or near real-time • Make the physical world digital Integrate device or sensor data with systems such as:
  6. 6. Connect to IoT Faster: MQTT Snaps Pre-built, intelligent connectors for the MQTT protocol • Ingest real-time device messages in real time • Validate, transform, and route messages • Integrate message data with application or API data • Persist messages on Hadoop, Redshift, Big Query and more • Provide bi-directional communication with devices • Provide seamless cross-protocol support
  7. 7. Example Use Case • We want to measure something in the real world, process it on a bunch of computer systems, and then send some signal out to the real world again • So let’s make our sensing hardware look like a mouse-clicking user • Let’s make our output hardware look like a browser to render to • And then we can reuse all the work that’s gone into making web applications over the last 20+ years
  8. 8. Example Use Case: Pipelines
  9. 9. Bringing IoT Data together for Sensor Analytics Ecosystems The Evolution of Data Management for the Internet of Things Joseph A. di Paolantonio jadp@dataArchon.com @JAdP http://press.teleinteractive.net/jadp https://saeiot.com/media/blogs/saeiot/SAEmi ndmap.svg
  10. 10. What’s IoT All About?
  11. 11. Things You Use Everyday
  12. 12. Right?
  13. 13. Self Quatification Child Health Logistics Retail Industrial Vehicle Home EMR
  14. 14. Things are great, but the real value comes from DATA
  15. 15. Five Cs IoT Maturity Model ❖ Connect ❖ Communicate ❖ Collaborate ❖ Contextualize ❖ Cognition
  16. 16. The Value of IoT ❖ Customer Understanding and Retention ❖ Bottom Line Improvements ❖ Process Efficiencies
  17. 17. Standards and More Standards ❖ IEEE ❖ IEC ❖ OMG IIC ❖ AllSeen Alliance ❖ OIC ❖ Thread ❖ MQTT ❖ CoAP ❖ Smart Manufacturing Leadership Coalition ❖ SmartGrid Interoperability Panel ❖ Society of Automotive Engineers International ❖ Personal Connected Health Alliance ❖ OASIS ❖ OpenIoT ❖ Open Mobile Alliance ❖ OneM2M ❖ IoT European Research Cluster ❖ Internet Protocol Smart Objects Alliance ❖ International Telecommunications Union ❖ International Standards Organization ❖ Health Level 7 International ❖ Home Gateway Initiative ❖ European Telecommunications Standards Institute ❖ GSMA ❖ Digital Living Network Alliance ❖ Eclipse M2M Industry Working Group ❖ Eclipse IoT Group ❖ Broadband Forum ❖ Consumer Electronics Association ❖ Third Generation Partnership Project ❖ Alliance for Telecommunications Industry Solutions ❖ NOAA ❖ Open Geospatial Consortium ❖ IETF
  18. 18. Metadata for IoT ❖ About a device ❖ About a sensor ❖ About an actuator ❖ About sensor-actuator feedback loops ❖ About a specific use ❖ Tracking algorithm evolution ❖ Time-series metadata ❖ Location metadata ❖ Set-points vs. readings
  19. 19. Joining Location and Time-Series Data
  20. 20. Data Architecture for IoT
  21. 21. Mark Madsen Third Nature ❖ Building the Enterprise Data Lake ❖ https://campaigns.snaplogic.com/data-lake-webinar?referring_site=madsen ❖ https://www.snaplogic.com/resources/white-papers/how-to-build-an- enterprise-data-lake
  22. 22. Thank You
  23. 23. Accelerate your integration. Accelerate your business. +1 888-494-1570 sales@snaplogic.com @SnapLogic www.snaplogic.com

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