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SplunkLIve! Warsaw IoT Session
- 1. Copyright © 2014 Splunk Inc. IndustrialData & Internet of Things Philipp Drieger Sales Engineer
- 2. 2 Splunk a World of Interconnected Assets Internet of Things Transportation | Energy | Utilities | Building Management Oil and Gas | Manufacturing Wearables, Home Appliances, Consumer Electronics, Gaming Systems, Personal Security, Set-Top Boxes, Vending Machines, Mobile Point of Sale, ATMs, Personal Vehicles Sensors, Pumps, GPS, Valves, Vats, Conveyors, Pipelines, Drills, Transformers, RTUs, PLCs, HMIs, Lighting, HVAC, Traffic Management, Turbines, Windmills, Generators, Fuel Cells, UPS Retail | Home | Consumer Telemedicine | Connected Cars Industrial Data
- 3. Why Splunk for Industrial Data? 3 Secure data collection across different formats, protocols and connectivity options Real-time dashboards and reporting Search, ad hoc correlations and powerful analytics across OT and IT data Scalable time- series storage of sensor, diagnostic and transactional data
- 4. 4 Splunk for 360 degree data view Data Analysts Technical Users Business Users Security Analysts
- 5. 5 Typical Workflow for Analyzing Sensor Data COLLECT ENRICH ANALYZE lookup data data analytics feedback loop sensor data middle ware
- 6. 6 3 Ways to Analyze Sensor Data with Splunk APPS leverage Splunk Apps to quickly onboard data and gain insights SCRIPT create scripts or code with SDKs for advanced and customized solutions SPL use out of the box SPL search commands to analyze your data https://splunkbase.splunk.com/
- 7. 7 Splunk Platform for IoT Data Connection and Collection Data Analytics Partner Ecosystem Developer Platform REST • Web Framework • SDKs: Java, .NET, JS • Modular Inputs • Virtual Indexes • Rest • SDKs • Universal Forwarder VIMI WFT
- 8. Copyright © 2014 Splunk Inc. IoT Success Stories
- 9. Data-Driven Refreshment Aggregate machine data from freestyle machines Insights into customer interactions and decisions Reduced Downtime and Increased Consumer Satisfaction Vending machine performance and diagnostics
- 10. + Content browsed, purchased and watched. All tracked by time and MAC address Customer behavior analytics Customer profile and MAC address / device assignments UnderstandingCustomer Behavior
- 11. 11 Remote Freight Train Monitoring Energy Efficiency Calculations Driving Strategy Recommendations Over $1 Billion Saved
- 12. Splunk wins 24h Hackathon at Deutsche Bahn
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- 21. Thank You – Q&A
Editor's Notes
- Welcome to SplunkLive [City]. Thank you for taking the time to attend today’s event.
- Complexity – Myriad connectivity options, different protocols, different data formats, data quality Lack of contextual information– Sensor data is not enough, need to mash this data with other enterprise data sources – asset management for example Integration challenges/ data correlation – Need to correlate sensor data with data from heterogeneous data sources Data security – Lack ability to securely access this data Lack of scalable platforms – Inability to analyze data in real time
- Splunk can offer a 360 degree view on your industrial data. Different stakeholders in your company can be provided with a specific view on your corporate data. Splunk enables all users to get insights quickly and gain value from industrial data. Most importantly, all stakeholders look at the data as a single point of truth. They all view the same data from different angles and can easily communicate about their findings and have shared insights. Regarding industrial data in Splunk, first of all technical users can monitor production machines and parts of their facility. They can better understand how their machines work and can troubleshoot problems to find errors or faults, discover anomalies and check system health of their environment. > Machine operator. > Maintenance technician. Business users can view enriched sensor data to check how production systems are aligned with business processes. Matthias will show later how energy data can be correlated with process data to gain valuable insights and improve business. Business users get real time insights from the production environment and monitor productivity and efficiency. For example an energy manager (calc TCO per month, aggegated data from 15min samples). Data Analysts can work with the data in Splunk to optimize production and business processes. Analysts can mine industrial data with frameworks like the R Project which easily integrates with a Splunk app. Matthias and his team also used the Splunk app for R or Python for advanced analytics and prediction in various projects. Last but not least, industrial data can also be valuable for security analysts. They can detect changes in machine configurations or see access behavior on industrial assets. By combining with IT and security data these analysts can correlate them with industrial data. This allows them to draw a bigger picture and protect industrial facilities from attacks and insider threats.
- A typical workflow for analyzing sensor data with Splunk can be outlined in 3 Stages: Collect, Enrich, Analyze. Stage COLLECT: Sensor data collected from industrial machines can flow directly into Splunk or use additional middle ware components to be indexed. Collection of sensor data can be done with Splunk Forwarder, REST, SDKs and also with the new feature announced in 6.3 HTTP stream. Whenever sensor data is hard to directly ingest into Splunk there are a bunch of middle ware components (like Kepware, ThingWorkx, just to name a few) that allow to onboard sensor data in Splunk easily. For energy meters Matthias will explain later how the RobotronSwitching Server can help with energy sensor data. Stage ENRICH: Raw sensor data may be enriched with non time series data like assets, configurations, user or product information. Splunk provides multiple ways to enrich raw data with information from databases, lookup files and other external data sources accessible by scripts. This step can bring significant value to the process because different users can analyze sensor data with context. Imagine for example you enrich sensor data from an machine with your asset inventory. With this added information an analyst can easily locate the machine on the production site and send for example the technican directly to the right machine. Stage ANALYZE: Splunk provides many statistical commands already out of the box to do all basic analytics (name a few commands). For more advanced analytics Splunk provides apps and interfaces to connect to more sophisticated or custom tools. With the R App you can feed your data from Splunk to R and return the results of the analysis back to Splunk. The returned results can be used in two ways: you can further enrich your sensor data e.g. with tags or eventtypes to categorize or classify your events. Or you use the results of external analytics tools to visualize those in Splunk dashboards.
- There are 3 Common Ways to Analyze Your Sensor Data: Simply use powerful Splunk Search Processing Language Command. Those ship out of the box with Splunk Enterprise and I will present you a quick overview in the next slide. Also watch out for upcoming development of our Machine Learning Team that aims to integrate Machine Learning into Splunk! Leverage Splunk Apps to quickly onboard data into Splunk and analyze sensor data to gain insights. Just to name a few: the R App, Predict App and Prelert. You can find many more on Splunkbase. Script your own solution using Python oder SDKs for advanced customized solutions that integrate with your application landscape and demands. Python offers quite a few useful libraries that can be used to analyze sensor data like NumPy for N-dimensional array computations, pandas for common data analysis or SciKit Learn if you want to dive into machine learning via Python.
- How to show that Splunk it also investing into these areas, where we are putting resources and money Ecosystem slide can come right after it.. One column for data ingestion Data Storage Data Analytics Data Visualization
- Welcome to SplunkLive [City]. Thank you for taking the time to attend today’s event.
- Customer comsumption pattern Add more specifics Words need to tbe tighted up Improve graphics…
- Comcast Corporation (Nasdaq: CMCSA, CMCSK) (www.comcast.com) is one of the world’s leading media, entertainment and communications companies. Comcast has many different Splunk use cases. One of their use cases involves taking data from the set-top boxes to gain real time insights in to customer interaction with content served up by the set top box. Each set top box has a media access control (MAC) address that is unique and is associated with a specific customer. The set top box is capturing all customer interaction with device including which content the customer searched for, what the date of search was, what search results were displayed (this information is recorded a unique identifiers called IDA numbers) and what content was purchased. However, the set top box does not have any information on the customer including their profile. That information is stored in the billing system. Comcast is using Splunk to correlate data across set top boxes and billing systems to gain real-time business insights. Using the correlation criteria of MAC address, content displayed in search and time of purchase, Comcast is gaining a broad range of business insights into their customers. For example, these insights are helping Comcast understand revenues driven by search. By overlaying this information with geo location data, they are able to improve content mix and drive higher monetization. These insights are also helping Comcast improve content promotion based on region. Comcast is using the Splunk and Hadoop integration to visualize Comcast setbox log information. The setbox data comes to Hadoop, get pre processed and moved to Splunk for visualization. Hadoop Input = High volume of data from many systems along a complex workflow, Developers expressing artistic prerogative on log formats, Many different data sources and formats Splunk Output = Drive operational intelligence, Improve user experience, Troubleshooting, root cause analysis, Track and measure success, Reports, alarms
- New York Air Brake’s Train Dynamic Systems Division is using Splunk to manage inter-train forces, the “slinky factor” inherent in large freight trains with 6 inches of flex between cars. With Splunk, they are able to produce insight and reports allowing the owners of the locomotives they manage to better train the engineers, and better manage the acceleration and braking of the trains throughout thousand mile journeys. Managing this data with Splunk, they can produce 1% fuel savings for customers. For their largest customers this can mean a billion dollars in savings a year.
- How can you leverage Splunk?
- How can you leverage Splunk?