This presentation will cover the design principles and techniques used to build data pipelines taking into consideration the following aspects: architecture evolution, capacity, data quality, performance, flexibility and alignment with business objectives. The discussions will be based on the context of managing a pipeline with multi-petabyte data sets; a code-base composed of Java map/reduce jobs with HBase integration; Hive scripts and Kafka/Storm inputs. We?ll talk about how to make sure that data pipelines have the following features: 1) Assurance that the input data is ready at each step. 2) Workflows are easy to maintain. 3) Data quality and validation comes included in the architecture. Part of presentation will be dedicated to show how to organize the warehouse using layers of data sets. A suggested starting point for these layers are: 1) Raw Input (Logs, Messages, etc.), 2) Logical Input (Scrubbed data), 3) Foundational Warehouse Data (Most relevant joins), 4) Departmental/Project Data Sets and 5) Report Data Sets. (Used by Traditional Report engines) The final part will discuss the design of a rule-based system to perform validation and trending reporting.

1. Rocket Fuel
Big Data and Artificial Intelligence for Digital Advertising
Abhijit Pol
Marilson Campos
Designing Data Pipelines
July, 2013

2. What We Do?
Data
Partners*
Optimize
Bid
Request
Rocket Fuel
Winning Ad
Ad Request
Ad Served to
User
Page
Request
Bid & Ad
Web Browser
Rocket Fuel Platform
Real-time Bidder
Automated Decisions
Response
Prediction
Model
Publishers
User
Engagement
Recorded
User Engages with
Ad
Refresh
learning
Campaign &
User Data
Warehouse
Qualify
Audience
Some Exchange Partners
Ad
Excha
nge
Ads &
Budget

3. How Big Is This Problem Each Day?
Trades on NASDAQ
Facebook Page Views
Searches on Google
Bid Requests Considered by Rocket Fuel

4. How Big Is This Problem Each Day?
Trades on NASDAQ
Facebook Page Views
Searches on Google
Bid Requests Considered by Rocket Fuel
~5 billion
10 million
30 billion
~20 billion

5. BIG DATA + AI

6. Advertising That Learns

7. Outline
•Architecture Evolution
•Hurdles and Challenges Faced
•Data Pipelines Best Practices

8. Architecture for Growth
•20 GB/month to 2 PB/month in 3 years
•New and complex requirements
•More consumers
•Rapid growth

9. How We Started?

10. Architecture 2.0

11. Current Architecture

12. Outline
•Architecture Evolution
•Hurdles and Challenges Faced
•Data Pipelines Best Practices

13. Hurdles and Challenges Faced
•Exponential data growth and user
queries
•Network issues
•Bots
•Bad user queries

14. Outline
•Architecture Evolution
•Hurdles and Challenges Faced
•Data Pipelines Best Practices

15. Data Pipeline Design Best Practices
Job Design
Consistency
Job Features
Avoid Re-work Golden Input
Shadow Cluster
Data Collection
Dashboard

16. Job Design / Consistency
•Idempotent
•Execution by different users
•Account for Execution Time

17. Job Execution Timeline

18. Job Features / Re-Work
•Smaller Jobs
•Record completion of steps

19. Recording completion times
Start
Is mark
already
there?
Step of workflow, job or script
Yes
No
Execute work
for the step.
Create the mark
End
Collect other
data (Optional)

20. Golden Input / Shadow Cluster
•Integration tests on realistic data sets.
•Safe environment to innovate.

21. Data Collection - Delivery time view
J
Data product
Workflow Workflow
Job
Job
Job Job
Job Job
Job
Job
JobJob
Job
Hive/Pig SSH Script
J J… J
J
Hive
J J J
Pig
…

22. Data collection : Data profiles view
Data product
Data set
Data set
= Data Set
= Transformation
Record Size & Type
Job
Counts
Join success ratios Data Set
Consistency

23. Data Collection Hierarchy
wk_external_events
wk_build_profile
user_profile
extract_fields
consolidate_metrics
load_into_data_centers
extract_features
compact_user_profile
Workflow/Job/Script StepData Product

24. Golden Input / Shadow Cluster
•Integration tests on realistic data sets.
•Safe environment to innovate.

25. Dashboard
• Delivery Time
• Data Profile Ratios
• Counters
• Alarms

26. Thank you
www.rocketfuel.com