In the last 3 years kappa architecture has evolved too much. From the Classic kafka+spark and now many options and new players like apache beam/google dataflow. Will show how a real use case has evolved and how important is think big and different.

1. Juantomás García - Open Sistemas
Kappa Architecture 2.0
DataScience Lab, Codemotion

2. Buenas Tardes
Codemotion
(Dobroho ranku Odesa)
first

3. Juantomás García
• Data Solutions Manager @ OpenSistemas
• GDE (Google Developer Expert) for cloud
Others
• Co-Author of the first Spanish free software book “La Pastilla
Roja”
• President of Hispalinux (Spanish Linux User Group)
• Organizer of the Machine Learning Spain and GDG Cloud
Madrid.
Who I am

4. What’s Kappa Architecture?
July 2, 2014 Jay Kreps coined the term Kappa
Architecture in an article for O’reilly Radar
“Maybe we could call this the Kappa Achitecture, though it may
be too simple of an idea to merit a Greek letter”

5. Jay has been involved in lots
of projects:
✓ Author of the essay: The
Log: What every software
engineer should know about
real-time data's unifying
abstraction (12/16/2013)
✓ Author of the book I love
Logs
Who is Jay Kreps?

6. •Involved with projects as:
✓ Apache Kafka
✓ Apache Samza
✓ Voldemort
✓ Azkaban
✓ Ex-Linkedin
✓ Now co-founder and CEO of Confluent
Who is Jay Kreps?

7. Usual Data Flow

8. Usual Data Flow

9. Usual Data Flow

10. Kappa Architecture Way

11. Tools we use

12. Tools we use

13. Tools we use

14. ✓ If you have an schema spark SQL, is
perfect.
✓ Spark streaming works very fine with spark
and almost each streaming sources.
✓ Structured queries will be a huge advance.
✓ We love Scala, the spirit of Spark.
Some Favorite Spark Features

15. We love code like this:
Some Favorite Spark Features

16. • One of our clients wanted to monitor all the
car's information via OBD II
• OBD II is a car interface with the car
electronics.
• Our client developed an app for reading all
the car information throw ODB II with
bluetooth
A Real Use Case

17. A Real Use Case

18. • We needed to scale the rest interfaces.
There were too many requests.
• MySQL don’t scale
• Client wanted to do realtime expensive
queries.
First Problems

19. Some metrics

20. Architecture v 2.0

21. Architecture v 3.0

22. We can have queries like:
“What are the drivers that are not client
of the X gas brand, has a few gas and
are near of gas station of the brand X and
if true, send a notification with a discount
coupon and a link with the route."
Now we’re more flexible!!

23. • Kappa architecture is not a silver bullet but helps
with a lot of solutions.
• Kafka + spark streaming are our favorite tools
• There are a lots of improvements:
Takeaways
✓ OLAP like Apache Druid
✓ Graph databases like neo4j
✓ Kafka streams and
compacts logs
✓ Apache Beams
✓ Scio Scala bindings

24. Takeaways: Apache Beam

25. Takeaways: Scio Scala Binding

26. Think Big

27. Think Big
• Forget Legacy Architectures
• Forget Old Tools
• Use Light Technologies / Serverless
• Use pieces of Lego
• Mix different technologies from diverse sources

28. Spark Use Cases
Not to do list
•Avoid install & config a server even a
VM.
•Avoid installs tools instead use
containers and/or cloud services.
•In general: think if there is a simpler
way to do it and needs less effort

29. Spark Use Cases
Architecture & Tools
•To use Cloud Services is not a brainer
decision.
•Git + Containers + Kubernetes
•Use the best language* for each
module.
•Use Notebooks: Jupyter, Zeppelin,
DSX
(*) Even java might be an option - unprovable

30. Google Cloud Version

31. Kappa Architecture
Questions?
•email: juantomas@opensistemas.com
•twitter: @juantomas
This talk have a free questions lifetime warranty: If you have any questions or concerns
about this talk, feel free to contact me anytime.
Selfie Time: If you liked the talk just smile while I take
the selfie ;-)
We’re Hiring!!

32. Kappa Architecture
велике спасибі