Confluent Streaming Events 2019

1. How Confluent Cloud helps for
sustainable food processing
Sabrina Eßer
Process Engineer
BAADER Group

2. About me

3. About Us
As a family-owned business – we think for generations. That is why we design solutions that will help people today, tomorrow, and beyond –
products that represent a sustainable value.
Innovation is the driving force behind our continuing growth. We draw on the creativity and resourcefulness of our people at BAADER to stay
ahead of other market players.
In close collaboration and partnership with our customers we are taking further major steps toward greater efficiency, traceability, transparency,
profitability, and sustainability. By sharing knowledge and data, together we can succeed in optimizing the food value chain in the long term.
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4. Worldwide
Global Presence
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With own offices, representatives and
service stations all over the world and
a reputation of high quality equipment for
the food industry, BAADER constitute
one of the strongest and most innovative
business partners in the global food
processing market.
Fish location
Poultry location

5. Digitalization
Digital Principles
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1. Everything that can be standardized will be standardized.
2. Everything that can be automated will be automated.
3. Everything that can be networked will be networked.
4. We love data!

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7. Digital Opportunities
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OPPORTUNITIES OF FUTURE VALUE ADD
CURRENT VALUE CREATION
OPPORTUNITIES OF FUTURE VALUE
ADD
Machine Information
Energy consumption, ..
Product Data: Size, Weight, Quality
Business Process Data: Supplier, Order, Customer, Shipment
INFORMATIONSKETTE
OPPORTUNITIES
OF FUTURE VALUE ADD
OPPORTUNITIES
OF FUTURE VALUE ADD
INFORMATION CHAIN

8. Data-based
Digital Business Models
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Examples:
● Freemium model: Spotify, Linkedin, Xing
● Subscription Model: Netflix, Amazon
● Free-Model: Google, Facebook
● Marketplace-Model: Uber, eBay
● Access-over-Ownership: AirBnB
● Pyramids model : Microsoft, Dropbox
● Ecosystem model: Apple, Google
● On-Demand model: Amazon Prime, Uber
Economist, 2019

9. Digitizing a machinery builder
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Challenges
● Huge diverse installation base (versions,
customizations)
● Machines are running very long > 15 years
● Different competitors in one factory
● Quality of data sources very different
● No clear standards in the past (e.g.,
temperature: celsius, celsius * 10, kelvin)
● Focus on machines not on software
● Digitizing machines is major homework
Opportunities
● Valuable process behavior shared across
businesses
● Having a ground-truth for data-driven
company
● Understand the precise relation between
business and process steps of the food
value network
● Engineering new machines with
digital-twins significantly improves the
innovation-cycle

10. Proof-of-Concepts
Data-pipelines with help of Confluent Cloud
● Setup: An independent small team with focus on digitalization
● Goal: Monitoring of a poultry factory to debug breakdowns (what is possible with just data)
● Given:
○ Some machines already providing data via OPC-UA (M2M) and custom protocols
○ Usually, those IT-systems aren’t prepared for digitalization
Requirements:
● How to get data from an factory without impacting the factory:
○ local IoT-Gateway gather data locally over MQTT
● Transfer as much data as possible to a central storage continuously:
○ Apache Kafka by Confluent Cloud
● Apply: Real-time transformation on the IoT-data while continuously uploading data to get insights:
○ KSQL und Kafka Connect by Confluent Platform
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11. Proof-of-Concepts (cont’)
Data-pipelines with help of Confluent Cloud
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Streaming Platform: Apache Kafka
● Right at the beginning we started with Confluent Cloud Enterprise for long-term storage
● Just keep focused on the PoC
Applied:
● KSQL Transformation to standardize machine data
● Kafka-Connect to store parts of the data in MongoDB (R data analysis)
Outcome:
● Significant optimization has been found by just looking at the data.
● Continuously checking of the hypothesis by just writing a KSQL query

12. A digital food value chain
Impact of the network of food value chain is huge but mostly hidden
Goal: A digital platform for collecting and sharing data along the food value chain.
Rough requirements
- Data needs to be ingested from any data source possible.
- Data needs to be shared in a permissioned way to any data sink
■ Talk later by Stefan: Routing IoT Data with KSQL and Kafka Connect at 5.20 pm.
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13. Breaking Business Silos
Partners need to be connected
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Farm Transport Fabrik Letzte Meile

14. Breaking Business Silos
Partners need to be connected
14 Farm Transport Fabrik Letzte Meile
Apache Kafka on Confluent Cloud
Standardization, Transformation, Aggregation

15. Breaking Business Silos
Partners need to be connected
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Farm Transport Fabrik Letzte Meile

16. Digital Platform for Food-Value-Chain
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IoT on Edge
Factory
IoT-GW by
BAADER.connect
Farm
IoT-GW by
BAADER.connect
Digital Platform
MQTT
overTLS
Event Storage & Processor baked by Apache Kafka
Farm events
Factory events
JIDL: Joint IoT Data Log
BAADER.modules
● Real-time
Dashboards
● Reporting
● Alerting
BAADER.connect
centralized handled

17. BAADER.modules
BAADER.connect
Modules
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JIDL: Joint IoT Data Log
Event Storage & Processor
Farm events
Factory events Real-timemonitoring
Alerting
Reporting
JIDL events
BAADER.connect
BAADER.connectcomponents
Managed by a
partner
Enterprise hosting
AWS, GCP, Azure,
Siemens MindSphere
JIDL-permissions
BAADER.connect

18. Bring it to live
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“Are you able to handle this huge amount of data from my
machine?”
“Can you store this data for more than 14 days?”
“Is it possible to get access to the data?”
Most asked questions
from our machinery
builders before providing
us sensor data.

19. Apache Kafka by Confluent Cloud
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● Partner for long-term partnership requires trust
● Confluent as partner exports in handling our data
● Confluent Cloud helps to connect different partners that belong each other
● Risk reduction
○ From PoC to production we haven’t changed anything in Kafka
○ Confluent Cloud: system is just available and reliable with world class 24/7/365 support
● Streaming Processing is generally is more natural to our daily live even in context of
IoT data

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Thank You!
Sabrina Eßer
sabrina.esser@baader.com
https://www.value-chain-network.com/01