1. The document discusses using Lambda architecture principles to perform fast data analytics on streaming and batch data from IoT sources using Spark Streaming and MLlib. 2. A proposed smart parking use case would recommend the best parking garage by combining streaming GPS data from cars with batch updates on garage capacity, scoring each garage using machine learning models. 3. The Lambda architecture is implemented using Kafka to ingest streaming GPS updates and batch capacity updates, Spark Streaming and Spark SQL to prepare, transform, and join the data, and MLlib to score and rank the garages in real-time.

1. Lambda Architecture in the IoT
Fast Data Analytics with Spark Streaming and MLlib
Bas Geerdink
ING

2. Bas Geerdink
• Chapter Lead in Analytics area at ING
• Master degree in Artificial Intelligence and
Informatics
• Spark Certified Developer
• @bgeerdink
• https://www.linkedin.com/in/geerdink
Who am I?

3. The Internet of …

4. • Data
– Streaming data from more sources
• Use cases
– Combining data streams
• Technology
– Fast processing and scalability
• Challenges
– Security: encrypt the sensors/network/server
What’s new in the IoT?

5. What do we want in the IoT?
• FAST data: process stream of events (sensory data)
• BIG data: process files/tables in batches (static data)
• ONE analytics engine
• ONE querying/visualization tool
• Scalable environment
• Reactive software

6. Lambda Architecture
Source: Nathan Marz (2013)

7. Gamma/Kappa/Omega/…
Architecture
• Lambda Criticism:
– Too complex
– Two code bases
– Two data stores
• Alternatives:
– Treat a stream as a mini-batch
– Treat a batch as a stream of records
– Combine batch and stream within one system

8. Big Data Reference Architecture
Source: Geerdink (2013)

9. Use case: Smart Parking
Recommend the best car park when driving to Amsterdam
 Show the car park with the highest score, determined by
• Batch (every x minutes), data from car parks
• Stream (every x seconds), GPS data of cars

12. Stream Process
• Get car events (GPS data)
• Filter events (business rules)
• Store events
• For each car park in the neighborhood:
– Get feature set (location, capacity, usage, …)
– Combine event with previous events (running sum)
– Update feature set
– Predict score (machine learning) and update database

13. Batch Process
• Get car park data
• Clean up (remove old car data)
• For each car park in the data set:
– Get recent set of car data (running sum)
– Update feature set
– Predict score (machine learning) and update database

14. Lambda Architecture
GPS
updates
Message
Broker
Analytics
Engine
File
System
Capacity
updates
Data Science
Environment
Machine learning models
Business Rules
Environment
Scores
Batch
Stream
1.
Get
features
2.
Apply
rules
3.
Predict
score
Business rules
Features
Selection
API
Front-end
Data Scientist
User

15. Lambda Architecture
GPS
updates
Message
Broker
Analytics
Engine
File
System
Capacity
updates
Data Science
Environment
Machine learning models
Business Rules
Environment
Scores
Batch
Stream
1.
Get
features
2.
Apply
rules
3.
Predict
score
Business rules
Features
Selection
API
Front-end
Data Scientist
User

16. Lambda Architecture
GPS
updates
Message
Broker
Analytics
Engine
File
System
Capacity
updates
Data Science
Environment
Machine learning models
Business Rules
Environment
Scores
Batch
Stream
1.
Get
features
2.
Apply
rules
3.
Predict
score
Business rules
Features
Selection
API
Front-end
Data Scientist
User

17. Event Processing with Kafka

18. Lambda Architecture
GPS
updates
Message
Broker
Analytics
Engine
File
System
Capacity
updates
Data Science
Environment
Machine learning models
Business Rules
Environment
Scores
Batch
Stream
1.
Get
features
2.
Apply
rules
3.
Predict
score
Business rules
Features
Selection
API
Front-end
Data Scientist
User

19. Stream: Data Preparation

20. Lambda Architecture
GPS
updates
Message
Broker
Analytics
Engine
File
System
Capacity
updates
Data Science
Environment
Machine learning models
Business Rules
Environment
Scores
Batch
Stream
1.
Get
features
2.
Apply
rules
3.
Predict
score
Business rules
Features
Selection
API
Front-end
Data Scientist
User

21. Stream: Create Recommendation

22. Lambda Architecture
GPS
updates
Message
Broker
Analytics
Engine
File
System
Capacity
updates
Data Science
Environment
Machine learning models
Business Rules
Environment
Scores
Batch
Stream
1.
Get
features
2.
Apply
rules
3.
Predict
score
Business rules
Features
Selection
API
Front-end
Data Scientist
User

23. Batch Processing

24. Lambda Architecture
GPS
updates
Message
Broker
Analytics
Engine
File
System
Capacity
updates
Data Science
Environment
Machine learning models
Business Rules
Environment
Scores
Batch
Stream
1.
Get
features
2.
Apply
rules
3.
Predict
score
Business rules
Features
Selection
API
Front-end
Data Scientist
User

25. Data Science with Zeppelin

26. Summary
• The IoT requires new architecture principles: in-memory,
distributed, reactive and scalable are the new normal
• Lambda/Kappa/Omega reference architectures are
designed for combining batch and streaming data flows
• Open source tooling such as Kafka, Cassandra, and
Spark adhere to these principles and design patterns

27. THANK YOU!
ING is hiring 
#SparkSummit