Data is growing at a quantum scale and one of challenges you face is to enable your users to analyze all this data, extract timely insights from it, and visualize it. In this session, you learn about business intelligence solutions available on AWS. We discuss best practices for deploying a scalable and self-serve BI platform capable of churning through large datasets. Fanatics, the nation’s largest online seller of licensed sports apparel, talks about their experience building a globally distributed BI platform on AWS, that delivers massive volumes of reports, dashboards, and charts on a daily basis to an ever growing user base. Fanatics shares the architecture of their data platform, built using Amazon Redshift, Amazon S3, and open source frameworks like Presto and Spark. They talk in detail about their BI platform including Tableau, Microstrategy, and other tools on AWS to make it easy for their analysts to perform ad-hoc analysis and get real-time updates, alerts, and visualizations. You also learn about the experimentation-based approach that Fanatics adopted to fully engage their business intelligence community and make optimal use of their BI platform resources on AWS.

1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Rahul Bhartia, Amazon Web Service, Principal Solutions Architect
Amit Jain, Fanatics, Sr. Manager - BI Platform and Reporting
December 1, 2016
Deploying Scalable, Self-Service Business
Intelligence on AWS
featuring

2. What to Expect from the Session
• Learn about various Business Intelligence (BI) solutions on AWS
• Hear from Fanatics about their scalable & elastic BI stack on AWS
• What this session is not about
• Various BI solutions and their features

3. BI on AWS
Amazon QuickSight AWS Big Data Competency partners

4. Get Started Quickly
• Amazon QuickSight – Get started today!!!
• Managed on AWS
• Tableau Online, Microstrategy Cloud, ChartIO, WingArc1st
• AWS Marketplace
• Tableau Server, Tibco Jaspersoft, Microstrategy, Looker

5. BI workloads on AWS Cloud
Self-service Scalable

6. BI workloads on AWS Cloud
Self-service Scalable

7. Making BI self-service on AWS
Get started today!
https://quicksight.aws/
Managed offerings
vs.
self-managed
Amazon QuickSight AWS Big Data Competency partners

8. Managing yourself
• Custom integration
• AWS CloudFormation
• Automate Tableau - https://github.com/tableau/server-install-
script-samples
• AWS Marketplace 1-click
• Tibco Jaspersoft
• Looker

9. Self-service Scalable
BI workloads on AWS Cloud

10. Scale – Infrastructure
• Scale-out
• Cluster or Distributed
• Scale-up
• Leverage bigger or more specific instances
• Scale-with
• Scale the underlying data-store

11. Tableau Online – Scaling on AWS
Tableau online on AWS

12. Scale – Data
Create an In-Memory
aggregation - Extracts or
Cubes or Cache
Leverage the underlying
cluster - In-database, Live
connection, Live Connect
Amazon QuickSight AWS Big Data Competency partners

13. Also, remember to
1. Leverage the right AWS Services
1. Amazon RDS
2. Amazon Redshift
3. Amazon EMR
4. Amazon S3
2. Leverage integrations with AWS Services
1. Amazon QuickSight – Direct ingestion from Amazon S3
2. Microstrategy –VLDB Properties for Amazon Redshift
3. Looker & ChartIO – Amazon EMR (Spark-SQL/Presto)

14. Largest retailer of
officially
licensed sports
merchandise

15. All Major US Leagues
If you are a sports fan, you’ve likely had a Fanatics
Experience

16. 26,000,000
Minutes of customer contact
250,000,000
Visitors across Fanatics’
platform of sites
31,000,000
Units shipped annually
6,000
Peak season employees
(1,700 non-peak)
Major Scale, Advantage
$1B in sales
through eCommerce
and sport venues

17. Business Centric Technology Centric
Financials
Inventory
Customer
Support
Marketing
Experimentation
S
I
T
E
S
E
R
V
I
C
E
S
Engineering
Hardware
Site Performance
Click Stream
Personalization
2016 - Data and Analytics everywhere

18. 18
Infrastructure
Analytical
Content
Developers
Scaling our BI environment

19. Current Fanatics Data Architecture
SSIS Stone Branch Spark
Data Integration
Qubole PIGAttunity
Data Platform
400 TB
Data Warehouse
FanHouse EDW (Redshift)
100 TB
Relational Data
Legacy Storage
Football (SQL Server)
500 TB
Unstructured Data
Pattern Detection
Deep Storage
HADOOP CLUSTERS
Analyze & Report Discover & Explore
MS Excel Tableau
Data Access
SOA/DAL SQL Custom AppsSSRS MicroStrategy
Business Centric Technology Centric
R

20. Evolution timeline on AWS
Microstrategy
‘’06 ’08 ‘18’End ’14 ‘15 Nov ‘16 ‘17
Access DB &
MS Excel Reports
(3 MB)
SQL Server
SSIS &
SSRS
(500 GB)
Redshift
S3
Spark
Presto
Storm/Kafka/Scala
Real Time
Reporting
R Integration
Machine
Learning
Tableau
Hadoop

21. Why we chose AWS
Scalability & Agility
Elasticity and Cost
Automation & Self-service
Availability & Disaster Recovery

22. Why we chose
Microstrategy Tableau
Enterprise Business Intelligence Data Discovery and Prototyping

23. Our Journey with Microstrategy
02-2015
TECH ASSESSMENT
10 LICENSES
{T2.XLARGE} (WIN /
ACCESS MD)
03-2015
IN PRODUCTION
DISTRIBUTION SERVICES
REPORTS {T2.XLARGE , RDS)
06-2015
WEB USERS ALPHA
{M4.4XLARGE
(WIN),RDS)
07-2015
WEB USERS PRODUCTION
{R3.4X LARGE (LINUX),
M4.4X LARGE (WIN),
RDS)
09-2016
500 WEB USERS
7 CUBES (AVG 100
MILL ROWS)
3-10 SEC CUBE
RESPONSE
(AWS X1 INSTANCE)
2017 Goal : 1500+ Users
All adhoc users on Microstrategy
DELIVER FAST, GATHER FEEDBACK, IMPROVE
Just took 1 month to be in Production

24. Current Microstrategy Architecture
500 WEB USERS
AWS X1
INSTANCE*
1 TB OF RAM, 8
TB OF SSD
CAN BE
CLUSTERED

25. Our Journey with Tableau
02-2015
TECH ASSESSMENT
TABLEAU ONLINE
(2 DESKTOP , 5
USERS)
03-2015
IN PRODUCTION
TABLEAU ONLINE
(3 DESKTOP, 30 USERS)
06-2015
OWN TABLEAU
ENVIRONMENT ON AWS
(5 DESKTOP USERS, 75
WEB USERS)
02-2016
HARDWARE UPGRADE ON
AWS
(12 DESKTOP USERS , 125
WEB USERS)

26. Why we chose AWS
Scalability & Agility
Elasticity and Cost
Automation & Self-service
Availability & Disaster Recovery

27. Tableau Capacity Management
Regular Capacity
(Single Server)
Peak Capacity
(Distributed Workers)

28. Cost Control (CloudWatch and Tags)
MLB WORLD
SERIES FINALS
DR
TESTING
TABLEAU 10
UPGRADE TEST
AWS X1
INSTANCE

29. Why we chose AWS
Scalability & Agility
Elasticity and Cost
Automation & Self-service
Availability & Disaster Recovery

30. Self-service for the users
Web Based Command Manager and tabadmin
• Web-service based event triggering
& control mechanism
• Triggers both Microstrategy and
Tableau events
• No need for client installation
• Has offset (or delay) mechanism
• Saves Significant resources and
complexity for ETL and Database
http://bitechapi.fanatics.corp:8080/FanBiAutomation/WBCM?triggername=testAmitemail&cmd=tableau&cmddelay=0&projname =

31. Microstrategy Systems Manager for Cluster Capacity
• Launch a New Governed AWS
Instance
• Automatically Start I-server
• Add to existing Microstrategy
Cluster

32. Why we chose AWS
Scalability & Agility
Elasticity and Cost
Automation & Self-service
Availability & Disaster Recovery

33. Run Hot/Cold Stand By Machines
• Disaster Recovery
• Redundant deployment in different Availability Zones
• Cold Stand By with a higher RPO/RTO
• Availability
• During Critical Business events/seasons
• Hot Stand By with instant failover capability

34. Best Practices for BI on AWS
• Automate
• Use CloudFormation Templates
• AMIs (and Maintain them)
• Distribute the workload
• Managed shared storage (EFS)
• Flexible infrastructure
• Microstrategy and Tableau
• Monitor your cost and budget
• CloudWatch Metrics and Tags

35. 36
3-10 second Data Exploration time for Business Users
Real time Reporting (consume elastic search web services)
Distribute 100s of PDF Reports Daily from the same
Metadata and Infrastructure, Run 1000s of Jobs per hour
Site Data services based on the same Metadata
Some Business Use Cases Solved

36. 37
Custom BI Portal and Real time analytics using AWS

37. 38
Hybrid Ownership Model –Hardware on AWS
/ Owned Software
Always buy “User Based Licenses” – Never
CPU Core
The BI Platform should be scalable and should have
enough Automation APIs to mimic cloud
functionality
Experiment with small number of user licenses to
prototype (start with 1 or 2 user license)
Try not to get locked in : If your vendor is
only subscription based then you are locked in
Cloud BI Vs On Premises BI (Get the best of both)

38. Thank you!

39. Remember to complete
your evaluations!

40. Related Sessions