1.
Traveloka’s Journey to
No Ops Streaming
Analytics
Rendy Bambang Jr., Data Eng Lead - Traveloka
Gaurav Anand, Solutions Engineer - Google

2.
● Business Intelligence
● Analytics
● Personalization
● Fraud Detection
● Ads optimization
● Cross selling
● AB Test
● etc.
How we use the data

3.
6 offices
Incl. Singapore
1,000+
Global employees
400+
Engineers
Our technology core has enabled
us to scale Traveloka into
6 countries
across ASEAN rapidly in
less than 2 years.

4.
#EnablingMobility

5.
In the beginning...

6.
Consumer of Data
Initial Data Architecture
Streaming
Batch
Traveloka
App
Kafka
ETL
In Memory Real
Time DW
Data Warehouse
S3 Data
Lake
Batch
Ingest
Android,
iOS
NoSQL Realtime DB
Traveloka
Services
Hive, Presto
Query
DOMO
Analytics UI

7.
Key Numbers
● Volume kafka: billions of messages/day
● In-Memory DB: hundreds of GB in-memory data
● NoSQL DB: 50+ nodes, 20+ TB storage, 50+ use cases
● S3: hundreds of TB
● Spark: 20+ nodes, 200+ core
● Redshift DW: 20+ Nodes, tens of TB
● Team: 8 Developers + 3 SysOps/DevOps

8.
Consumer of Data
Problems with Initial Data Architecture
Streaming
Batch
Traveloka
App
Kafka
ETL
In Memory Real
Time DW
Data Warehouse
S3 Data
Lake
Batch
Ingest
Android,
iOS
NoSQL Realtime DB
Traveloka
Services
Hive, Presto
Query
DOMO
Analytics UI

9.
Problems with Initial Data Architecture
Debugging Kafka Issues - Dedicated
On-call
Data Warehouse throughput issues
for high frequency load, coupling
storage & compute
Team well being, paged on holiday,
even honeymoon for infra issue!
Scaling Issues with NoSQL DB and
In-Memory DB
Scaling Issues with Custom-built
Java Consumers

10.
How do we..?

11.
Ideal Solution
Fully-managed infrastructure to
free engineers to solve business
problems
Autoscaling of Storage and
Compute
Low end-to-end latency with
guaranteed SLA
Resilience, end-to-end system
availability

12.
Solution Components
● Google Cloud PubSub (Events Data Ingestion)
● Google Cloud Dataflow (Stream Processing)
● Google Bigquery (Analytics)
● Cross-Cloud Environment (AWS-GCP)
● AWS DynamoDB (Operational datastore)
Note: Although Cloud Datastore was our prefered operational DB, but its non availability in SG region
necessitated use of Dynamodb.

13.
How did we..?

14.
Analytics Architecture: Reimagined
Consumer of Data
Streaming
Batch
Traveloka
App
Kafka
ETL
Data
Warehouse
S3 Data
Lake
Batch
Ingest
Android,
iOS
DOMO
Analytics UI
NoSQL DB
Traveloka
Services
Ingest
Cloud
Pub/Sub
Storage
Cloud
Storage
Pipelines
Cloud
Dataflow
Analytics
BigQuery
Monitoring
Logging
Hive, Presto
Query

15.
Developed two Common Dataflow Engine
● Self-Service Streaming analytics to BigQuery

16.
Developed two Common Dataflow Engine
● Stream processing to DynamoDB, common features for dev:
○ Combine by key
○ Optimistic Concurrency
○ Local-file based integration test

17.
Key Facts/Numbers
● End to End Pipeline Latency: seconds
● Volume: hundreds of GB/day
● Team: 2 Developers, 0 Ops
● Agility: POC + Pilot in 1 month
● Migrate 50+ different stream processing use case in 1 month
● Bigquery Integration with BI tools: thousands of dashboard, hundreds of
users

18.
Awesome Autoscale
Pubsub & Dataflow could
absorb spiky load just fine!
Our case: promo
PubSub Publish Count DataFlow vcpus Count

19.
Why Cloud Dataflow
(Beam): Tuning Pipeline
vs. Managing Servers

20.
The Lambda Architecture

21.
Unified Model with Apache Beam
Batch
or
Streaming

22.
http://www.vldb.org/pvldb/vol8/p1792-Akidau.pdf

23.
Unified Model with Apache Beam

24.
Optimize
Schedule

25.
Why Google Cloud..?

26.
Traveloka Data Team Philosophy
● Managed Service
● NoOps
● Self-Service
Focus more on solving complex business problems rather than focusing on
infrastructure

27.
What required us to change?
● Ever increasing scale
● Ever increasing operations burden
● New business needs: Streaming Analytics

28.
What’s Next..?

29.
Next Generation Architecture
Cloud Pub/Sub
Cloud Dataflow
BigQuery Cloud Storage
Kubernetes Cluster Collector
Managed services
Simplify!
BI &
Analytics UI

30.
Conclusion
Our engineering team of
2 produces and
maintains like a team of
8 because of products
like PubSub, Dataflow &
Bigquery
“
”

31.
Q&A

32.
Thank You.