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IoT Analytics @ splunk

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© 2 0 1 9 S P L U N K I N C . IoT Analytics @ Splunk Funktionalitäten kurz und knackig Ronald Perzul
During the course of this presentation, we may make forward‐looking statements regarding future events or plans of the company. We caution you that such statements reflect our current expectations and estimates based on factors currently known to us and that actual events or results may differ materially. The forward-looking statements made in the this presentation are being made as of the time and date of its live presentation. If reviewed after its live presentation, it may not contain current or accurate information. We do not assume any obligation to update any forward‐looking statements made herein. In addition, any information about our roadmap outlines our general product direction and is subject to change at any time without notice. It is for informational purposes only, and shall not be incorporated into any contract or other commitment. Splunk undertakes no obligation either to develop the features or functionalities described or to include any such feature or functionality in a future release. Splunk, Splunk>, Turn Data Into Doing, The Engine for Machine Data, Splunk Cloud, Splunk Light and SPL are trademarks and registered trademarks of Splunk Inc. in the United States and other countries. All other brand names, product names, or trademarks belong to their respective owners. © 2019 Splunk Inc. All rights reserved. Forward- Looking Statements © 2 0 1 9 S P L U N K I N C .
© 2 0 1 9 S P L U N K I N C . Who am I? OFFICIAL TITLE: Staff Consulting Sales Engineer FAVORITE QUOTE: "Learning never exhausts the mind." — Leonardo Da Vinci FAVORITE MOVIE: Blade Runner PREVIOUS ROLES: lead consultant | trusted advisor | solution architect | project manager EMPLOYERS: SAP | IBM | Splunk INTERESTS: Traveling, board and videos games, my family WHAT I DO: Advise customers and prospects on Splunk solutions in the area of IoT and IT Operations NAME: Ronald Perzul
© 2019 SPLUNK INC. Splunk for Manufacturing Intro
© 2 0 1 9 S P L U N K I N C . Splunk turns manufacturing data into Business Outcomes Access to Expanded Data Universe Powered by AI and ML CloudOn-Premise Any Structure, Any Source, Any Time Scale SecurityIT Business Users IoT EngineeringMfg Apps Supply Chain Splunk >enterprise Splunk >cloud
© 2 0 1 9 S P L U N K I N C . Predictive Maintenance Advanced Monitoring & Investigation Integrate and manage data across complex data sources, and applications to drive equipment observability Forecast and predict downtime of critical assets with machine learning to predict failure Predict downtime of equipment, make recommendations, and act on recommendations Downtime Investigation Prescriptive Maintenance Perform simple monitoring, alerting, and diagnostics across disparate data sources Splunk for Manufacturing From Investigation to Automation
© 2 0 1 9 S P L U N K I N C . Architecture Example Splunk Purdue Model - Simple FieldBus Control OT Enterprise and Corporate Analytics Level 0 Level 1 Level 2/3 Level 4 Heavy Forwarder UF / HF Optional Use Case: Capture logs/sensor data in real-time and correlate data with quality metrics stored in MES, ERP, etc. or other systems to further enrich this data. IoT Analytics Strong ecosystem to ensure data connectivity and acquisition as well as implementation.
© 2019 SPLUNK INC. Anforderungen: • Anbindung an MQTT Broker • Support von unstrukturierten Daten und Metriken • Klassische Funktionalitäten wie Reports, Dashboards, Alarmierung • Eingebaute ML Funktion oder Erweiterung durch Frameworks • AR/VR/Mobile Erweiterungen • Self-Service Funktionalitäten: Ad-Hoc Analysen und Korrelationen durch Anlagen- und Prozessingenieure Benefits: • Fokus der IT auf Betrieb und Datenbereitstellung • Nutzung vorhanderer Infrastruktur für IoT Use Cases • Wiederverwenden von vorhandenem Know-How • Schnelle Implementierung und geringe Aufwände Technologiekonzern (Bestandskunde) Use Case: IoTAnalytics Plattform Anforderungen und Benefits Industrie
© 2019 SPLUNK INC. Fast, Easy, Intuitive Analytics
© 2 0 1 9 S P L U N K I N C . Metrics Set of numbers describing a particular process or activity Measured over intervals of time – i.e., time series data Common metrics sources: • IoT Sensors (temperature readings) • System metrics (CPU, disk memory) • Infrastructure metrics (AWS CloudWatch) • Web tracking scripts (Google Analytics) Events Immutable record of discrete events that happen over time Come in three forms: plain text, structured, binary Common event sources: • System and server logs (syslog, journald) • HEC • APIs (Twitter, Wunderground) • Application, platform and server logs (log4j, log4net, Apache, MySQL, AWS) HEC • External apps (EAM, Work Order Mgmt., etc.) Metrics and Events Metrics vs. Events Timestamp Metric Name Value Dimensions 1481050800 brearing.temperature 42.12345 hq:us-west-1 Sample Metric [29/Aug/2017 08:47:05:316503] "POST /cart.do?uid=84e8d742-a31d69&action=remove&&product_id=BS- 2&JSESSIONID=SD6SAL4FF1ADFF9 HTTP 1.1" 200 2569 "http://www.buttercupenterprises.com/product.screen? product_id=BS-2" "Mozilla/5.0 (Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2957.0 Safari/537.36" 98 Sample Log Equivalent to 1 metric value Splunk Enterprise 8.0
• Create better performing streaming metrics alerts in a few quick steps directly from the results of visual analysis • Analyze Events, Alerts and Metrics together • Visually compare variations in your metrics with statistical metrics • Create more types of dashboard panels and reports Splunk Enterprise Analytics Workspace Self Service Splunk Enterprise 8.0
© 2 0 1 9 S P L U N K I N C . Demo – Self-Service Diagnostics Use Case: Self-service diagnostics and troubleshooting, monitor and correlate all operational data. Recommended reading: https://www.splunk.com/en_us/blog/iot/self- service-analytics-for-the-shop-floor-part-i- splunk-core-concepts.html https://www.splunk.com/en_us/blog/iot/self- service-analytics-for-the-shop-floor-part-2-a- practical-example-using-mqtt.html
© 2 0 1 9 S P L U N K I N C . Demo – Visualization Framework Use Case: Monitor and visualize complex manufacturing processes or use for replacement of existing Andon Boards to display key production KPI’s.
© 2 0 1 9 S P L U N K I N C . Demo – IT/OT Monitoring with ITSI Use Case: Monitor the availability and interoperability of business services, between systems and interfaces involved, from IT to OT.
© 2019 SPLUNK INC. Machine Learning Toolkit Splunkbase App Splunk Machine Learning Toolkit 5.0 • Navigate modeling with a new, modern showcase layout • Tackle your problems with smarter assistants like the Smart Outlier Detection Assistant for anomaly detection • Applicable to many use cases with the Smart Forecasting Assistant with Multivariate Forecasts and Special Days Effects Use Case: Predictive Maintenance and Predictive Quality. Use Splunk’s Machine Learning toolkit to understand all correlations as they are happening in real-time.
© 2019 SPLUNK INC. Connected Experiences
© 2019 SPLUNK INC. Splunk Connected Experiences Delivering contextual insights seamlessly for better, faster decisions Stay connected with on- the-go visibility Empower non-technical users to access data Provide contextual insights that inspire action
Splunk®AR on iOS ● Splunk® Workflow Automation Consume data in AR and act on it through workflow playbooks ● Media Notes Add video notes, audio notes, image notes, and pdf notes to workspaces ● Chart Support Support extended to all basic Splunk® chart types Connected Experiences Use Case: execute automated actions based on manual machine inspection results.
© 2 0 1 9 S P L U N K I N C . Demo – Augmented Reality Use Case: Provide access to operating manuals or videos to fix issues directly at the asset or just understand machine behavior without access to a laptop or workstation.
Thank You © 2 0 1 9 S P L U N K I N C . Ronald Perzul rperzul@splunk.com

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IoT Analytics @ splunk

  • 1. © 2 0 1 9 S P L U N K I N C . IoT Analytics @ Splunk Funktionalitäten kurz und knackig Ronald Perzul
  • 2. During the course of this presentation, we may make forward‐looking statements regarding future events or plans of the company. We caution you that such statements reflect our current expectations and estimates based on factors currently known to us and that actual events or results may differ materially. The forward-looking statements made in the this presentation are being made as of the time and date of its live presentation. If reviewed after its live presentation, it may not contain current or accurate information. We do not assume any obligation to update any forward‐looking statements made herein. In addition, any information about our roadmap outlines our general product direction and is subject to change at any time without notice. It is for informational purposes only, and shall not be incorporated into any contract or other commitment. Splunk undertakes no obligation either to develop the features or functionalities described or to include any such feature or functionality in a future release. Splunk, Splunk>, Turn Data Into Doing, The Engine for Machine Data, Splunk Cloud, Splunk Light and SPL are trademarks and registered trademarks of Splunk Inc. in the United States and other countries. All other brand names, product names, or trademarks belong to their respective owners. © 2019 Splunk Inc. All rights reserved. Forward- Looking Statements © 2 0 1 9 S P L U N K I N C .
  • 3. © 2 0 1 9 S P L U N K I N C . Who am I? OFFICIAL TITLE: Staff Consulting Sales Engineer FAVORITE QUOTE: "Learning never exhausts the mind." — Leonardo Da Vinci FAVORITE MOVIE: Blade Runner PREVIOUS ROLES: lead consultant | trusted advisor | solution architect | project manager EMPLOYERS: SAP | IBM | Splunk INTERESTS: Traveling, board and videos games, my family WHAT I DO: Advise customers and prospects on Splunk solutions in the area of IoT and IT Operations NAME: Ronald Perzul
  • 4. © 2019 SPLUNK INC. Splunk for Manufacturing Intro
  • 5. © 2 0 1 9 S P L U N K I N C . Splunk turns manufacturing data into Business Outcomes Access to Expanded Data Universe Powered by AI and ML CloudOn-Premise Any Structure, Any Source, Any Time Scale SecurityIT Business Users IoT EngineeringMfg Apps Supply Chain Splunk >enterprise Splunk >cloud
  • 6. © 2 0 1 9 S P L U N K I N C . Predictive Maintenance Advanced Monitoring & Investigation Integrate and manage data across complex data sources, and applications to drive equipment observability Forecast and predict downtime of critical assets with machine learning to predict failure Predict downtime of equipment, make recommendations, and act on recommendations Downtime Investigation Prescriptive Maintenance Perform simple monitoring, alerting, and diagnostics across disparate data sources Splunk for Manufacturing From Investigation to Automation
  • 7. © 2 0 1 9 S P L U N K I N C . Architecture Example Splunk Purdue Model - Simple FieldBus Control OT Enterprise and Corporate Analytics Level 0 Level 1 Level 2/3 Level 4 Heavy Forwarder UF / HF Optional Use Case: Capture logs/sensor data in real-time and correlate data with quality metrics stored in MES, ERP, etc. or other systems to further enrich this data. IoT Analytics Strong ecosystem to ensure data connectivity and acquisition as well as implementation.
  • 8. © 2019 SPLUNK INC. Anforderungen: • Anbindung an MQTT Broker • Support von unstrukturierten Daten und Metriken • Klassische Funktionalitäten wie Reports, Dashboards, Alarmierung • Eingebaute ML Funktion oder Erweiterung durch Frameworks • AR/VR/Mobile Erweiterungen • Self-Service Funktionalitäten: Ad-Hoc Analysen und Korrelationen durch Anlagen- und Prozessingenieure Benefits: • Fokus der IT auf Betrieb und Datenbereitstellung • Nutzung vorhanderer Infrastruktur für IoT Use Cases • Wiederverwenden von vorhandenem Know-How • Schnelle Implementierung und geringe Aufwände Technologiekonzern (Bestandskunde) Use Case: IoTAnalytics Plattform Anforderungen und Benefits Industrie
  • 9. © 2019 SPLUNK INC. Fast, Easy, Intuitive Analytics
  • 10. © 2 0 1 9 S P L U N K I N C . Metrics Set of numbers describing a particular process or activity Measured over intervals of time – i.e., time series data Common metrics sources: • IoT Sensors (temperature readings) • System metrics (CPU, disk memory) • Infrastructure metrics (AWS CloudWatch) • Web tracking scripts (Google Analytics) Events Immutable record of discrete events that happen over time Come in three forms: plain text, structured, binary Common event sources: • System and server logs (syslog, journald) • HEC • APIs (Twitter, Wunderground) • Application, platform and server logs (log4j, log4net, Apache, MySQL, AWS) HEC • External apps (EAM, Work Order Mgmt., etc.) Metrics and Events Metrics vs. Events Timestamp Metric Name Value Dimensions 1481050800 brearing.temperature 42.12345 hq:us-west-1 Sample Metric [29/Aug/2017 08:47:05:316503] "POST /cart.do?uid=84e8d742-a31d69&action=remove&&product_id=BS- 2&JSESSIONID=SD6SAL4FF1ADFF9 HTTP 1.1" 200 2569 "http://www.buttercupenterprises.com/product.screen? product_id=BS-2" "Mozilla/5.0 (Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2957.0 Safari/537.36" 98 Sample Log Equivalent to 1 metric value Splunk Enterprise 8.0
  • 11. • Create better performing streaming metrics alerts in a few quick steps directly from the results of visual analysis • Analyze Events, Alerts and Metrics together • Visually compare variations in your metrics with statistical metrics • Create more types of dashboard panels and reports Splunk Enterprise Analytics Workspace Self Service Splunk Enterprise 8.0
  • 12. © 2 0 1 9 S P L U N K I N C . Demo – Self-Service Diagnostics Use Case: Self-service diagnostics and troubleshooting, monitor and correlate all operational data. Recommended reading: https://www.splunk.com/en_us/blog/iot/self- service-analytics-for-the-shop-floor-part-i- splunk-core-concepts.html https://www.splunk.com/en_us/blog/iot/self- service-analytics-for-the-shop-floor-part-2-a- practical-example-using-mqtt.html
  • 13. © 2 0 1 9 S P L U N K I N C . Demo – Visualization Framework Use Case: Monitor and visualize complex manufacturing processes or use for replacement of existing Andon Boards to display key production KPI’s.
  • 14. © 2 0 1 9 S P L U N K I N C . Demo – IT/OT Monitoring with ITSI Use Case: Monitor the availability and interoperability of business services, between systems and interfaces involved, from IT to OT.
  • 15. © 2019 SPLUNK INC. Machine Learning Toolkit Splunkbase App Splunk Machine Learning Toolkit 5.0 • Navigate modeling with a new, modern showcase layout • Tackle your problems with smarter assistants like the Smart Outlier Detection Assistant for anomaly detection • Applicable to many use cases with the Smart Forecasting Assistant with Multivariate Forecasts and Special Days Effects Use Case: Predictive Maintenance and Predictive Quality. Use Splunk’s Machine Learning toolkit to understand all correlations as they are happening in real-time.
  • 16. © 2019 SPLUNK INC. Connected Experiences
  • 17. © 2019 SPLUNK INC. Splunk Connected Experiences Delivering contextual insights seamlessly for better, faster decisions Stay connected with on- the-go visibility Empower non-technical users to access data Provide contextual insights that inspire action
  • 18. Splunk®AR on iOS ● Splunk® Workflow Automation Consume data in AR and act on it through workflow playbooks ● Media Notes Add video notes, audio notes, image notes, and pdf notes to workspaces ● Chart Support Support extended to all basic Splunk® chart types Connected Experiences Use Case: execute automated actions based on manual machine inspection results.
  • 19. © 2 0 1 9 S P L U N K I N C . Demo – Augmented Reality Use Case: Provide access to operating manuals or videos to fix issues directly at the asset or just understand machine behavior without access to a laptop or workstation.
  • 20. Thank You © 2 0 1 9 S P L U N K I N C . Ronald Perzul rperzul@splunk.com