Talked about how engineer's life at Traveloka and what technology stacks used in Data Team at Traveloka.

1. Life @ Traveloka
Imre Nagi
Data Engineer

2. Introduction
Imre Nagi (Imre)
● S1 Electrical Engineering, ITB (2010)
● Software Engineering Intern, CERN (2013)
● S2 Software Engineering, CMU (2016)
● Software Engineering Intern, eBay (2017)
● Data Engineer, Traveloka (2018)
● Docker Community Leader

3. This is what I present
• What Traveloka Is
• Working @ Traveloka
• Tech @Traveloka Data
Outline

4. Part 1: What Traveloka Is
Yes, we are selling flight ticket,
but we are also more than this

5. Who we are
What Traveloka Is

6. Products (March 2018)
What Traveloka Is

7. Our Story
What Traveloka Is

8. What we believe
What Traveloka Is

9. Part 2: Working @ Traveloka
Work Hard, Play Harder

10. Flexible Working Hour
Working @ Traveloka

11. Free Breakfast & Lunch
Working @ Traveloka

12. Diversity
Working @ Traveloka

13. Working From Home
Working @ Traveloka

14. Brown Bag
Working @ Traveloka

15. Part 3: Tech @ Traveloka
Data
We provide data, insight and value to our data user

16. Tech @ Traveloka
Code Management & Version
Control

17. Tech @ Traveloka
Scrum @ Traveloka Data

18. Tech @ Traveloka
Current Architecture

19. Tech @ Traveloka
Goal

20. Tech @ Traveloka
Data Science Tools

21. Expected benefits:
▪ Simplify our footprint on
tools’ variations
▪ Serverless, less things to
worry about
▪ Less operations overhead
▪ Scale easily
▪ Cheaper
WHY USING GOOGLE?
21
Main challenges:
▪ Different animals
▪ Compatibility with our
current workflow
▪ Might redo a lot of things,
especially reports and
enrichments
▪ Huge migrations

22. Tech @ Traveloka
Deployment Workflow
Code + PR
+
Automated
Test + CR
Manual Trigger
Deploy for Prod
(canary release,
stable release)
Automatic
Deploy to
Stg

23. Tech @ Traveloka
Jenkins as CI/CD
tools

24. Tech @ Traveloka
Data Use Case
Realperson: are you real?
Automate Know Your Customer
(KYC) process by leverage
off-the-shelf ML APIs such as
Google Cloud Vision and Azure
Face API.
ml-common: expose in-house ML model as web-service (in
collaboration with NVS team).
Flask-based web-service, with support for ML models from:
- caffe2 , Scikit Learn, Tensorflow Serving, Keras (w/ Tensorflow
Backend)
Hotel Images:
Automate hotel images
selection which improves the
hotel booking and transactions

25. TRAVELOKA first
then THE WORLD