This document summarizes a presentation given by David Darden and Don Smith of Big Fish Games about their efforts to integrate the BigInsights and PureData System for Analytics platforms. They discussed augmenting their data warehouse by using these platforms for landing zones, exploration of "awkward" datasets, and offloading some processing. They demonstrated several options for moving data between the platforms using tools like Sqoop, Fluid Query, and Big SQL. They identified documentation, performance, and usability as ongoing challenges and next steps to improve their users' experience with the systems.

1. © 2015 IBM Corporation
DDW-2150 : Integrating BigInsights
and PureData System for Analytics
With Query Federation and Data
Movement
David Darden – Big Fish Games
Don Smith – Big Fish Games
October 28th, 2015

2. Agenda
• Introduction
• Data Warehouse Augmentation
• What We’ve Learned
• Options & Demos
• Pain Points & Next Steps
• Q & A
1

3. Who Are We?
• Big Fish Business Intelligence Engineering Team
• World's largest producer and distributor of casual games
• Big Fish has distributed more than 2.5 billion games to
customers in more than 150 countries
• Small, agile, business focused
• Owners of the Enterprise Data Warehouse
2
David Darden
david.darden@bigfishgames.com
Don Smith
don.smith@bigfishgames.com

4. What Are Our High Level Goals?
• Provide the right data to the right people at the right time
• Deliver business value fast
• Give people the tools they need to do their job
• Minimize reliance on engineering
3
Business intelligence (BI) is the set of techniques and tools
for the transformation of raw data
into meaningful and useful information
for business analysis

5. Data Warehouse
Augmentation
4

6. • Landing Zone / Pre-Processing / Data Lake
• Exploration
• Dealing with awkward data sets
• Offloading
Big
Data
Volume
Variety
Velocity
Veracity
What is Data Warehouse Augmentation?
5

7. What Does Our Architecture Look Like?
6

8. Where Were We Last Year?
• Data integration using a variety of tools
• Right platform for the right job
• Data was in silos if they weren’t manually integrated
7
Landing Zone Offloading
Exploration Awkward data
sets

9. What Have We Learned?
• Blending data across the different platforms unlocks huge value
• Most users favor familiar tools over performance
• Use cases are highly disparate
• Users have a hard time on their own (lots of options!)
8

10. What Are the Options?
9

11. Where Are We Now?
• Query federation (bi-directional) is a major use case
• Data movement (bi-directional) is a major use case
• Documentation & examples are key
• Direction for the best tool for the job
• Keeping up with a changing landscape
10

12. Demos
11

13. Integration Options
• We want our users to get the data to where they need it, with
minimal effort/complexity
• We want methods for efficiently moving data for ETL tasks
• 3 approaches
 Big Insights Command Line
 Netezza SQL
 Big SQL
12
Platform Use Case Ease of Use Performance
Big
Insights/PDA
Import or export data • Easy
• Medium
• Hard
• High
• Medium
• Low

14. Sqoop
 $BIGINSIGHTS_HOME/sqoop/bin/sqoop import --direct --
connect jdbc:netezza://netezza-
hadoopdata.sea.bigfishgames.com:5480/edw_prod_eds --query
'select * from my_table where my_key % 2 = 0 and
$CONDITIONS' --username donsmith -P --target-dir
/adhoc/don.smith/fluidtest/sqoop_nz_to_bin_01 --split-by my_key
--num-mappers 24
13
Platform Use Case Ease of Use Performance
Big
Insights
Import or export data Medium Medium-High,
~38-100MB/s

15. Fluid Query High Speed Data Movement
Demo!
14
Platform Use Case Ease of Use Performance
Big
Insights
Import or export data Hard High,
~100MB/s

16. HDFS Read/Write
15
Platform Use Case Ease of Use Performance
Netezza Read or Write data from/to
HDFS
Medium Low,
~5MB/s

17. Fluid Query Data Connector
Demo!
16
Platform Use Case Ease of Use Performance
Netezza Query Big SQL table or Hive
table
Easy Low,
~5MB

18. Big SQL Load Statement
Demo!
17
Platform Use Case Ease of Use Performance
Big SQL Import data to Big SQL table Easy Medium,
~10MB/s

19. Big SQL Query Federation
18
Platform Use Case Ease of Use Performance
Big SQL Query remote data (on PDA or
other sources)
Medium Shocking

20. Big SQL Query Federation
19
Platform Use Case Ease of Use Performance
Big SQL Query remote data (on PDA or
other sources)
Medium Shocking

21. Integration Options
20
Platform Option Ease of Use Performance
Big Insights Sqoop Medium Medium-High,
~38-100MB/s
Big Insights FluidQuery HDM Hard High,
~100MB/s
Netezza HDFS Read/Write Medium Low,
~5MB/s
Netezza FluidQuery Data Connector Easy Low,
~5MB
Big SQL Big SQL Load Easy Medium,
~10MB/s
Big SQL Big SQL Query Federation Medium Shocking

22. Documentation & Examples
21

23. Pain Points & Next
Steps
22

24. Pain Points
• Documentation
• Lots of experimentation
• Manual steps
• Highly variable performance
23

25. Next Steps
• Continue iterating with users (make it easier)
• Improve patterns & guidance for self-serve
• Continue upgrading (new features)
• Continue learning & tuning
• Improve monitoring and alerting
• Increase size of data lake
• Increase EDW offloading
24

26. Questions?
25

27. We Value Your Feedback!
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Access your surveys at insight2015survey.com to quickly
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conference kiosk.
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