Leading brands such as Pepsi and Macy’s use Celtra’s technology platform for brand advertising. To inform better product design and resolve issues faster, Celtra relies on Databricks to gather insights from large-scale, diverse, and complex raw event data. Learn how Celtra uses Databricks to simplify their Spark deployment, achieve faster project turnaround time, and empower people to make data-driven decisions. In this webinar, you will learn how Databricks helps Celtra to: - Utilize Apache Spark to power their production analytics pipeline. - Build a “Just-in-Time” data warehouse to analyze diverse data sources such as Elastic Load Balancer access logs, raw tracking events, operational data, and reportable metrics. - Go beyond simple counting and group events into sequences (i.e., sessionization) and perform more complex analysis such as funnel analytics.

2. Grega Kešpret, Director of Engineering, Analytics – Celtra
Denny Lee, Technology Evangelist – Databricks
December 9th, 2015
How Celtra Optimizes its
Advertising Platform
with Databricks

3. About Me: Grega Kešpret
Grega Kešpret is the Director of Engineering for Analytics. He works at
Celtra since 2012, where he helped build analytics pipeline and
optimization systems. Grega also leads the team of engineers and data
scientists at San Francisco and Ljubljana, working on their analytics
platform. Prior to Celtra, Grega worked at IBM, helping enterprise
customers adopt WebSphere Application Server and before that did a 8-
month internship at SANYO (Panasonic) in Japan, working on battery
systems. His current technical interests include databases, distributed
systems, functional programming and machine learning.

4. About Me: Denny Lee
Denny is a Technology Evangelist with Databricks; he is a hands-on data
sciences engineer with more than 15 years of experience developing
internet-scale infrastructure, data platforms, and distributed systems for
both on-premises and cloud. His key focuses surround solving complex
large scale data problems – providing not only architectural direction but
the hands-on implementation of these systems. He has extensive
experience in building greenfield teams as well as turn around / change
catalyst. Prior to joining Databricks, Denny worked as a Senior Director of
Data Sciences Engineering at Concur and was part of the incubation team
that built Hadoop on Windows and Azure (currently known as HDInsight).

6. • About Celtra & AdCreator
• Event Data
• AdTech Problems (Sessionization, Funnel Analytics)
• Databricks Use Cases
• Data Analysis Evolutionary Path
• Main pain points from the past
• Demo
• Q&A
Agenda

7. Motivation
The analytics platform at Celtra has experienced tremendous growth
over the past few years in terms of size, complexity, number of users,
and variety of use cases.
Business Data Volume Data Complexity

8. Leading creative technology
company for data-driven brand
display and video advertising
across mobile, tablets and desktop
Powering the creative side of
advertising campaigns
SaaS platform to allow clients easily
create, manage, and traffic data-
driven dynamic ads, optimize them
on the go, and track their
performance with insightful
analytics
Celtra AdCreator
About Celtra & AdCreator

9. Trusted by +5000 brands
and agencies.

10. Unparalleled Distribution Options
Reach across endless list of large publishers and media owners, DSP’s and ad networks.
Certified and partnered with the top 25 global Ad Networks and
over 50 premium publishers
AdCreator is the most widely used creative, analytics and
optimization technology for display and video advertising

11. • Bread and butter of our analytics data
• Facts about what happened
• creative X was served on placement Y at time Z
• user A interacted with creative X at time Q
• Automatically tracked
• Behavioral data from users
• ~2 billion events per day
• 1 TB new data per day (uncompressed)
• Very sparse
• Complex relationships between events
Event data

12. • Combining discrete events into sessions
• In first version, we used to compute simple counts of events
• But, no “holistic” view over the whole session:
• Very hard to troubleshoot/debug
• Cannot check for/enforce causality (if X happened, Y must also have
happened)
• Duplication leading to skewed rates (e.g. one user interacting)
• Later events make you understand earlier arriving events better (e.g.
session duration, attribution, identity, etc.)
Sessionization

13. Why Spark for Event Analytics
One of the first companies to use Spark in production (v0.5)
Sessionization
Nice API
Expressive computation layer
Speed of innovation
Seamless integration with S3
Aggregated
Analytics data
Event data
+
Operational
data
Trackers

14. Funnel Analytics
Celtra Analytics Funnel View

15. • Multi-step process
• Enabled by sessionization
• Originally developed in the context of e-commerce sites
• Ad was requested, then served, then shown, then interacted with, then the
user expanded the ad, then watched a video, …
• Not just whether X happened; but whether A, B and X happened
Funnel Analytics
XA
B

16. Production Use Case

17. But that’s just
the beginning

18. Consider the following sample questions:
• When do users engage with creatives?
• Do different groups of users behave differently?
• Why is a certain rate low or high?
• What is the adoption of recently rolled out feature?
• Does it correlate with engagement?
• What features are important when detect environment we run in?
In a traditional data warehouse, you’d figure out all the needed reports/questions in
advance, design the schema and that’s it.
Questions & Answers

19. Explanation
Insight
Question
Insight generation lifecycle

20. 1. Ad-hoc queries for campaigns
• Databricks allows us to easily run our increasingly complex ad-hoc queries
2. Exploratory data analysis
• Derive value from data as soon as possible
3. Troubleshooting
• The bar for reliability and correctness is very high
• Why something broke? Why is certain rate low/high?
• Quickly identify the root cause of production failures and minimize system downtime
Use cases

21. 4. Compliance jobs
• Regularly scheduled jobs that make various checks for compliance purposes (e.g. non-human
traffic)
5. Supporting product decisions with data
• Some of this is not “Big Data”, but visualizations, precomputing different views, etc.
• Example: pricing model analysis
• Connecting to Databricks with Tableau
6. Predictive analytics
• Example: dynamically detect environment in which
our tags run (in-app, mobile web)
Use cases

22. • Need flexibility, not provided by precomputed aggregates (uniques, order
statistics, etc.)
• Answers to questions that existing data model does not support (Demo)
• Short development cycles and faster experimentation
• Complex ecosystem + Wide creative capabilities = Diverse data
• Data focused on the engagements of consumers with our clients’ ads
• Constantly exploring new ways to leverage this information to improve our offering
• Visualizations
• Important aspect of big data mining
• In 9 out of 10 notebooks, there is at least one visualization
Exploratory data analysis

23. Complex ecosystem / environment
• Support all major mobile
platforms (IOS, Android,
Windows Phone) and desktop
• Work on mobile web & in-app
• Device fragmentation,
browser specifics

24. • Errors will happen
Error in log parsing
Wrong tags being trafficked
Custom (wrong) code being executed
Internal service call failures
• When stuff doesn’t work, we need to be able to
dig into the data easily and quickly
• To understand the problem, we need to check:
ELB logs, event logs, operational data, creative structure, machine logs
Troubleshooting
Resulting in
Bad user experience
Wrong insights (wrong metrics)

25. Data analysis evolutionary path
Solution progression
Bash
Logcat
Spark
Spark-Shell
Databricks
• Downloading event logs from S3
on a single machine, “grepping”,
doing simple counts in bash. Using
R or Python for analysis.
• But: Slow download (single core,
one machine)
• Solution: Written Logcat – Scala,
multi-threaded downloading from
S3, scale horizontally
Version 1 (with Bash)

26. Bash
Logcat
Spark
Spark-Shell
Databricks
Data analysis evolutionary path
Solution progression
• Can download large amount of
logs
• But: no shuffle, i.e. no
grouping/joining events
• Solution: use Spark
Version 2 (with Logcat)

27. Bash
Logcat
Spark
Spark-Shell
Databricks
Data analysis evolutionary path
Solution progression
• Can download large amounts,
do shuffle and perform data
analysis
• But: package code into jar,
submit jar to the cluster, not
really interactive
• Solution: spark-shell to the
rescue
Version 3 (with Spark)

28. Bash
Logcat
Spark
Spark-Shell
Databricks
Data analysis evolutionary path
Solution progression
• Can download large amounts,
do shuffle and perform data
analysis interactively
• But: need to provision clusters,
cannot do any statistical
processing, cannot visualize
results without moving data
(again)
• Solution: some ideas, but then
saw Databricks demo at Spark
Summit 2014
Version 4 (with spark-shell)

29. Bash
Logcat
Spark
Spark-Shell
Databricks
Data analysis evolutionary path
Solution progression
• Can download large amounts,
do shuffle and perform data
analysis interactively, visualize
results and perform statistical
processing
• We can focus on drilling down
into the raw events, quickly
verify hypotheses and visualize
results, all of which would be
very difficult if not impossible
one year ago.
Version 5 (Databricks)

30. • Complex setup and configuration required
• Requires effort and experience
• Analyses not reproducible and repeatable
• No collaboration
• Moving data between different stages in troubleshooting/analysis lifecycle
• For example: Scala for aggregations, R for statistics and visualization
• Heterogeneity of the various components (Spark in production, something
else for exploratory data analysis)
• Analytics team (3-5 people) bottleneck
Main pain points from the past

31. • Reduced the load on the analytics engineering team by expanding
access to the number of people able to work with the data directly by
a factor of four.
• Before: Engineers, Analytics team (5 people)
• Today: Engineers, Analysts, Ad ops, Support team, QA, Product (35 people, 20 active)
• Increased the amount of ad-hoc analysis done six-fold, leading to
better informed product design and quicker issue detection and
resolution.
• Increased collaboration and improved reproducibility and
repeatability of analyses.
Scaling Big Data Analysis Projects Six-Fold

32. Demo
Event data
+ + +
+
Operational
data
Databricks TableauBusiness
intelligence
Job/analysis
results
Access logsAggregated
analytics data
SumoLogic
BambooHR CircleCI
+
CloudWatch DynamoDB MongoDB Snowflake

33. Q&A

34. Appendix

35. • Notebook sharing
• public/ and private/ folders within each user’s home directory
• Database default, tables called u_<username>_*
• problems with per-user databases (only works with Hive tables)
• Always prefer Spot instances for ad-hoc clusters
• Name the cluster <username>, <username>2, <username>3, etc.
• Prefer DBFS mounts over s3a:// URLs to get the benefits of Tachyon
Tips for using Databricks

36. • If you want to preserve state, save it to a table (by e.g. CREATE
TABLE
AS
SELECT
...
FROM
...) or to Parquet files on S3
• Another option is to downscale the cluster to 1 node (all variables,
temporary tables etc. will be retained).
• Prefer using Jobs for longer/larger tasks
• Avoid high cardinality joins in Tableau, instead join the data in Spark/
Databricks
Tips for using Databricks