In 2016, digital advertising will overtake TV in overall media spend, surpassing $66 billion in annual revenue. The companies that will come out on top in this new digital media landscape will be those that can cut through the noise and reach the right consumer with the right message at the right time. At Altitude Digital, we have to decide which video ad to serve to a particular user in a matter of milliseconds—and be able to report on it in real time to our clients. In this session, I’ll discuss how we do this accurately and effectively, by leveraging a data pipeline we built using Hadoop, Kafka, Cascading, Oozie, Druid, and a custom ETL process. By the end of this session, you’ll understand the benefits of layering these technologies, and how you can apply the same process in order to transform your businesses.

1. The Data Pipeline Behind
Billions of Daily Transactions
Manny Puentes, CTO
@epuentes

2. Who Am I?
Manny Puentes
CTO at Altitude Digital
@epuentes
Linked.in/in/MannyPuentes
MannyPuentes.com
Leading strategy & vision behind the
largest Independent Programmatic
Platform for Publishers

3. 3
Let me introduce you to “Big Data”

5. • Big Data is at the heart of our
business.
• In seconds, we have to decide
the best video ad to play at the
right time for the right
person…billions of times per
day.
• It’s a hard problem. And we’ve
built a powerful data platform
behind it.
Big Data at Altitude Digital: The Business

6. 70 nodes in our MapR production cluster
5 PB raw storage (1.7 PB useable with 3x replication)
6.7 TB RAM
1040 CPU cores
415.24 TB CDN bandwidth per month
23.6 Billion CDN requests per month
60 Billion video events per day
Big Data at Altitude Digital: By the Numbers

7. • Present our clients with real-
time insights and analytics.
• Updated every 15 minutes
• Slicer functionality lets clients
pivot on unique data points
• Up to 30 days of data can be
returned instantly.
Giving Customers Insights

8. Using Big Data to Solve Big Industry Issues
Fraud. We detect and
throw out any non-
human traffic before it
hits the exchange.
Viewability. We
predict it; don’t show
an ad unless there’s
a high probability of
being seen.
The right big data platform & pipeline
lets us solve for these issues
programmatically… and give clients
real-time analytics and insights.

9. Big Data at Altitude Digital: The Data Pipeline
Video Player Events Server
Analytics Platform
Earhart -
ETL

10. MapR
• Reduced Infrastructure and
Resource Costs
• Half the infrastructure needed to
process large amounts of data
• Developers don’t have to spend
time solving big data problems
• Built-in NFS reduces reliance on
Java programmers and rely on
standard IT resources
• Open Source Ecosystem
Integration
• Support – Insurance Policy

11. MapR – Hardware
• 70 hosts, each with:
• 16 CPU cores at 2.6 GHz
(Intel Xeon E5-2640V3)
• 96 GB RAM
• 12x 6TB disks
• Cumulative specs:
• 1,120 CPU cores
• 6,720 GB RAM
• 5 PB raw storage

12. Kafka
• Centralized messaging for all
components within our platform
• Fast and Scalable
• Durable
• Messages are persistent on disk
and replicated within the cluster
• Distributed by Design
• Cluster-centric design makes it
fault-tolerant

13. Kafka - Hardware
• 5 hosts per data center, each
with:
• 8 CPU cores at 2.6 GHz (Intel
Xeon E5-2640V3)
• 64 GB RAM
• 12x 6TB disks
• Cumulative specs per data
center:
• 40 CPU cores
• 320 GB RAM
• 360 TB raw storage

14. Custom ETL – Earhart
• Earhart based on founding
principals of Camus, built to our
use case
• Needed scalable and fault-tolerant
revenue processing
• Ability to re-process hourly
partitions
• Runs in 15-minute batch intervals
• Human-readable offset
management
• Avro natively built into processing

15. Cascading
• Robust, reliable, data-oriented
• Build & test locally—deploy at
scale in production
• Eliminate compute fabric lock-in
• Runs on and can be ported between
MapReduce, Apache Tez, Apache Flink
• Reduced development &
operational complexity
• Data processing code/algorithms
• Cascading Pattern for machine
learning

16. Driven
• Accelerates Cascading
application development and
management
• Plugin collects internal run-time
and execution metadata directly
from any Cascading application
and visualizes data application
• Reduced cluster utilization costs
• Improved performance
• Immediate application failure
diagnosis

17. Druid
• Time-series database
• Sub-second queries
• Ideal for user-facing analytics,
and can handle thousands of
concurrent users
• Real-time streams
• Lock-free ingestion
• Query 10,000+ events per
second per node
• Scalable & cost-effective
• Deploy anywhere

18. Druid - Hardware
• 15 hosts, each with:
• 20 CPU cores at 2.3 GHz
(Intel Xeon E5-2650V3)
• 196 GB RAM
• 1.2 TB SSD
• Cumulative specs:
• 300 CPU cores
• 2940 GB RAM
• 18 TB SSD storage

19. Big Data at Altitude Digital: The Data Pipeline
Video Player Events Server
Analytics Platform
Earhart -
ETL

20. Altitude ARENA Demo

21. Questions?
Connect:
@epuentes
Linked.in/in/MannyPuentes
MannyPuentes.com