This talk presents StreamPipes (https://www.streampipes.org), an open source self-service data analytics solution leveraging existing big data technologies such as Apache Flink to provide non-technical users with an easy and intuitive way to connect, analyze and exploit a variety of different streaming data sources for their use. Newly arising IoT-driven use cases in domains such as manufacturing, smart city or autonomous driving often demand for continuous integration and processing of sensor data in order to derive time-sensitive actions. One example is the optimization of maintenance processes based on the current condition of machines (condition-based maintenance). While this is technically already well supported by the existing big data tool landscape, building such applications still require a crucial set of expertise ranging from general domain expertise, programming skills to deep knowledge on distributed and scalable systems. Such skills are usually not present in hardware-focused manufacturing companies. To mitigate these shortcomings, StreamPipes allows non-technical users to leverage a graphical editor to model and deploy analytical tasks as pipelines in a drag and drop manner. Pipelines are built based on a toolbox of reusable data adapters, processors and sinks. Toolbox elements encapsulate dedicated algorithms (e.g., filter, aggregation, machine learning classifiers) implemented in big data processing engines such as Apache Flink communicating over an internal distributed messaging system (e.g. Apache Kafka). In this talk, we present technologies and tools enabling flexible modeling of real-time processing pipelines by domain experts. We motivate our talk by showing real-world examples we gathered from a number of industry projects during the past years in Industrial IoT domains such as manufacturing and supply chain management. For instance, we show how StreamPipes eases the accessibility of big data tools for non-technical users based on examples such as supervising a fleet of autonomous electric delivery vehicles as well as data analytics in one of the largest test areas for autonomous driving in Germany.

1. Flink for Everyone: Self-Service Data
Analytics with StreamPipes
Patrick Wiener, Philipp Zehnder
Flink Forward Europe 2019, Berlin, 2019-10-08

2. www.streampipes.org | @streampipes | github.com/streampipes
2
"A self-service IoT toolbox to enable non-technical users
to connect, analyze and explore IoT data streams"
What's StreamPipes?

3. www.streampipes.org | @streampipes | github.com/streampipes
3
What's StreamPipes?
Big Data / Edge
InfrastructureExecuteReusable
algorithm toolbox
Install
Model pipelines

4. www.streampipes.org | @streampipes | github.com/streampipes
About us
4
Dominik Riemer
Senior Research Scientist
Philipp Zehnder
Research Scientist
Patrick Wiener
Research Scientist
FZI Research Center for Information Technology, Karlsruhe, Germany
Stream Processing, Data Management, Machine Learning
Non-profit research center for applied ICT research (250 employees)
Started StreamPipes in 2014, first OSS release 2018

5. www.streampipes.org | @streampipes | github.com/streampipes
Agenda
The need for self-service IoT data analytics1
StreamPipes: Technical Overview
Demo
2
Lessons Learned w/ Flink & Getting Started3

6. The need for self-service IoT data analytics
1

7. www.streampipes.org | @streampipes | github.com/streampipes
Conveyor Belts
Pressure
Oil temperature
Dust particles
Production plans
Environmental Data
Gear box drive
Energy consumption
Telematics
Industrial Internet of Things
Data streams everywhere

8. Continuous Monitoring Situational Awareness
Continuous Data
Harmonization
Flexible data integration
from heterogeneous
sources and monitoring
of current system states
Detect time-critical
situations, e.g., by
means of rules or ML
approaches
Continuous pre-
processing and
transformation of input
streams for third party
systems
Industrial Internet of Things
Typical application scenarios

9. www.streampipes.org | @streampipes | github.com/streampipes
StreamPipes
Open Source framework to easily manage IoT data
Data Access
Data analytics &
harmonization
Data exploration &
exploitation
Generic adapters
Specific adapters
Metadata
Data streams & sets
Pre-processing
Filter/Aggregation
Pattern Detection
ML
Situation detection
Harmonized data sets
Visualizations
Third-party systems
9

10. Technical Overview
2

11. www.streampipes.org | @streampipes | github.com/streampipes
High-level architecture
Analytics Microservices
Data Integration
Data Sources
Adapter Library
Pipeline Editor
Streaming Engine
11

12. www.streampipes.org | @streampipes | github.com/streampipes
High-level architecture
Analytics Microservices
Data Integration
Data Sources
Adapter Library
Pipeline Editor
Streaming Engine
12

13. Data Access
StreamPipes Connect: Easily connect IoT sources

14. www.streampipes.org | @streampipes | github.com/streampipes
Data Access
Machine-interpretable metadata
100
011
010
001
010
010
100
101
000
111
data stream
{
"tstamp": 1453478160,
"machineId": "ID5",
"temperature": 73.5,
"flowRate": 4.2
}
Semantic
metadata
Data type, runtime name,
semantic type
Frequency, latency,
measurement unit
Format, Protocol
Schema
Quality
Grounding
14

15. www.streampipes.org | @streampipes | github.com/streampipes
Data Access
Machine-interpretable metadata
Example
temperature
schema.org/temperature
schema.org/degreeCelsius
xsd:float
[0,80]
100
011
010
001
010
010
100
101
000
111
data stream
{
"tstamp": 1453478160,
"machineId": "ID5",
"temperature": 73.5,
"flowRate": 4.2
}
Semantic
metadata
15

16. www.streampipes.org | @streampipes | github.com/streampipes
Data Access
StreamPipes Connect: Architecture
Connect Master
Connect Worker 1 Connect Worker 2 Connect Worker n
MySQL
RESTROS
OPC-UAPLC
MQTT
Messaging
Edge Worker Cloud Worker
…
register
capabilities
16

17. Demo
Introduction to StreamPipes
Connecting and visualizing flow rate measurements of a multi tank system

18. Demo
Introduction to StreamPipes
Flow
Sensor
Aggregate
data
VisualizeMQTT
StreamPipes Connect
Connecting and visualizing flow rate measurements of a multi tank system

19. www.streampipes.org | @streampipes | github.com/streampipes
High-level architecture
Analytics Microservices
Data Sources
Adapter Library
Pipeline Editor
Data Integration
19
Streaming Engine

20. Analytics microservices
Extensible toolbox

21. www.streampipes.org | @streampipes | github.com/streampipes
• Extensible toolbox for pre-
processing & analytics
• Semantics-based
consistency checking
• Exchangable run-time
wrappers
• Stateful/stateless
• Inclusion of ML-models
possible
Features
Analytics microservices
Extensible toolbox
21

22. www.streampipes.org | @streampipes | github.com/streampipes
Analytics microservices
Anatomy of a processing element
Aggregation
Controller
output eventsinput events
Runtime
22

23. www.streampipes.org | @streampipes | github.com/streampipes
Analytics microservices
How to implement a new processing element
Select Wrapper
Implement
runtime
Describe
controller
Build / Install
Maven
Archetype
StreamPipes
SDK
StreamPipes
SDK
SDK, Docker,
UI
Aggregation
Controller
Runtime
23

24. www.streampipes.org | @streampipes | github.com/streampipes
Analytics microservices
Runtime Wrapper
Standalone/Edge
Wrapper
Kafka Streams
Wrapper
Python Wrapper
Select
Wrapper
Implement
runtime
Describe
controller
Build /
Install
Aggregation
Controller
output eventsinput events
Runtime
24
Flink Wrapper

25. www.streampipes.org | @streampipes | github.com/streampipes
Analytics microservices
SDK: Runtime
Select
Wrapper
Implement
runtime
Describe
controller
Build /
Install
Aggregation
Controller
Runtime
25

26. www.streampipes.org | @streampipes | github.com/streampipes
Analytics microservices
Processing Element Description
User Configuration Output StrategyInput Requirements
Schema, Quality, Protocol,
Format
Text Input, Selections, Domain
Knowledge, …
Keep, Custom, Transform,
Append, …
Semantic Metadata
Select
Wrapper
Implement
runtime
Describe
controller
Build /
Install
Aggregation
Controller
Runtime
26

27. www.streampipes.org | @streampipes | github.com/streampipes
Analytics microservices
Development: Maven Archetypes & SDK
Select
Wrapper
Implement
runtime
Describe
controller
Build /
Install
Aggregation
Controller
Runtime
27
Input
User Config
Output

28. www.streampipes.org | @streampipes | github.com/streampipes
Flink Cluster
Aggregation Job
28
Select
Wrapper
Implement
runtime
Describe
controller
Build /
InstallAnalytics microservices
Flink Deployment
Pipeline Management
register start
Controller
output eventsinput events
Runtime
Aggregation
RemoteEnvironment
Upload jar
Submit execution graph
Kafka
Source
Kafka
Sink

29. Demo
Condition monitoring + StreamPipes
Rule-based monitoring of flow rate measurements in a multi tank system

30. Demo
Condition monitoring + StreamPipes
Rule-based monitoring of flow rate measurements in a multi tank system
Flow
Sensor
Aggregate
data
Detect
Leakage
Notify
MQTT
IoTDB
StreamPipes Connect
Calculate
Statistics

31. Lessons Learned & Getting Started
3

32. www.streampipes.org | @streampipes | github.com/streampipes
Potentially huge stream of sensor data needs scalability
Remote Environment eased the implementation of Flink Wrapper
Clean & intuitive Flink API enables fast processor development
Simple setup for development (mini cluster) and deployment
Easy to configure & monitor
Good integration with Apache Kafka
Flink + StreamPipes
Lessons learned
P
P
P
P
P
P

33. www.streampipes.org | @streampipes | github.com/streampipes
How to start
Setting up StreamPipes
Docker-based installation
streampipes.org/en/download
Download installer from Github1
./streampipes start2
Finish installation in browser3
33

34. www.streampipes.org | @streampipes | github.com/streampipes
34
What's next?
Data Access
Data analytics &
harmonization
Data exploration &
exploitation
Metadata recognition
PLC4X
Flink fault tolerance
Python wrapper
AutoML
Historical data
explorer
New features: Current work-in-progress
Infrastructure (Edge / Fog)

35. Let's connect!
…and if you like StreamPipes, star us on Github J
streampipes.org
docs.streampipes.org
github.com/streampipes/streampipes
twitter.com/streampipes
feedback@streampipes.org