SlideShare a Scribd company logo
Implementing EDW
using MongoDB
22 March 2016
1
Purvesh Patel
Cardtronics
•  About Cardtronics
•  Enterprise Data Warehouse
•  Lessons Learned, Best Practices developed or followed, Next steps
2
Implementing EDW using MongoDB
Who and Where We Are
The World’s Leading ATM Owner and Operator
168,274 U.S. and
Puerto Rico locations
15,735 U.K.
locations
1,406 Mexico
locations
3,255 Canada
locations
1,121 Germany
locations
Global ATM Operations
24 Carnival Cruise
Ships & the Caribbean
Nearly
190,000
ATMs
Key Partnerships
At Cardtronics, we provide cash and financial services in the most
convenient locations, connecting consumers, retailers and
financial institutions where cash meets commerce.
3
What We Do
•  Purchasing
•  Installation &
placement
•  All makes &
models
•  Exterior
signage,
toppers, etc.
•  Transaction
processing
•  Network
settlement
and dispute
resolution
•  Multiple
language
support
•  99% ATM
Uptime
•  Monitoring &
incident
management
•  First and
second line
maintenance
•  24/7 service
hotline
•  Cash
provisioning
•  Cash
forecasting
•  Cash
management
•  Marketing
campaign
management
•  One-to-one
marketing
•  On-screen
advertising
•  Image
deposit
Cardtronics provides a virtual á la carte menu of ATM
services solutions. From processing transactions via our
proprietary EFT platform, to fully-turnkey ATM programs,
Cardtronics has the right solution for any business.
4
How Our ATM Solutions Drive Customer Traffic
•  Make the ATM a stage for purchase decisions
–  Mobile products deliver incentives to visit the store/use the ATM
–  Surcharge-free products eliminate the largest barrier to ATM use
–  ATM Marketing converts the ATM visit to a sale or other event
SurchargedOut-of-Store Surcharge-Free	Products
&
Allpoint	
Mobile	/	Web
FeeAlert
Bank	
Marketing
ATM	Marketing
Direct-to-
Consumer
Network
Branding
5
•  About Cardtronics
•  Enterprise Data Warehouse
•  Lessons Learned, Best Practices developed or followed, Next steps
6
Implementing EDW using MongoDB
Problems We Were Trying To Solve
7
§  Technology Challenges
§  Enterprise Application Scaling
§  Real-Time + Analytics Workload
§  Performance, Scalability
§  High Availability, Upgrades
§  Advanced Security
•  Business Challenges
§  Just as many formats (ATM Transactions, POS Transactions, Gateway
Transactions)
§  Can we get analysis in a useful timeframe?
§  When we get new data, how fast can we do something with it?
Why MongoDB
8
•  Just as many formats
§  Dynamic Schemas
•  Real-Time + Analytics Workload
§  Full, Flexible Index Support and Rich Queries
§  Aggregation Framework and MapReduce
•  Performance, Scalability
§  Sharding for Horizontal Scalability
§  Increase capacity as you go
§  Cloud Architectures
•  High Availability, Upgrades
§  Automated replication and failover
§  Multi-data center support
§  Improved operational simplicity (e.g., HW swaps)
Total Cost Of Ownership
9
MongoDB T***data/E**data
Capital Expense
Commodity Server,
Internal Storage (no SAN)
SAN, Propriety Hardware
Operating Expense $$ $$$$
Licensing Cost Free $$$$$
Time To Market 6-12 Months 18-24 Months
Ability to Prototype Yes No
Ability to Scale
Quickly
Yes No/May be
Support
24/7 Included with Enterprise
License
????
Technology Open Source Proprietary
EDW Architecture
All in One Data
Transformation
Process – ETL
(Extract, Transform,
and Load)
All	in	One
Office	4.3
ATM
Transactions
POS	Data
Non-Financial
Transactions
FeeAlert	FI
Transactions	
&	Enrollment
LocatorSearch
Click	Traffic
Enterprise Data Warehouse
iDesign	Offers	
&	Favorite	
Transaction
TBD	-	Future
ETL
Service-oriented Architecture
ATMPass CORE
ATM
Operations -
Ops Analytics
ATM
Operations -
Reporting
Internal
Staff
HyperionCAMP
ATM
Transaction
Analysis
MapApp
Branding
Interface
Allpoint &
Branding
Pricing
Interface
iDesign
Offers
FI BIN
Reports
POS
Interface
FeeAlert
LocatorSear
ch click
traffic
TBD -
Future
IN PIPELINE
UNDER
CONSTRUCTION
Gateway	
Files
COMPLETED
Postilion	Servers
Office
Office
Office
Office
European
RealTime
North	America
RealTime
US	Platform
RealTime
US	Pilot
RealTime
Device
Manager Device	
Manager
10
MongoDB Cluster Architecture
Mongod
Mongos
Mongod
Mongos
Mongod
Mongos
Mongod
Mongos
Mongod
Config
Mongod
Mongos
Mongod
Mongos
Config
Mongod
Mongos
Mongod
Config
AWS EAST-A
AWS EAST-B
i2.2xlarge
61 GB Ram
2xSSD: RAID-0
Journal on root
{w:2}
i2.2xlarge
61 GB Ram
2xSSD: RAID-0
Journal on root
{w:2}
i2.2xlarge
61 GB Ram
2xSSD: RAID-0
Journal on root
{w:2}
i2.2xlarge
61 GB Ram
2xSSD: RAID-0
Journal on root
{w:2}
i2.2xlarge
61 GB Ram
2xSSD: RAID-0
Journal on root
{w:2}
i2.2xlarge
61 GB Ram
2xSSD: RAID-0
Journal on root
{w:2}
m2.4xlarge
68 GB Ram
2xEBS: 840GB
m2.4xlarge
68 GB Ram
2xEBS: 840GB
Backup & snapshotting layer
m2.4xlarge
68 GB Ram
2xEBS: 840GB
11
MongoDB Technology Stack
12
MongoDB Release 3.0.10
Operating System Amazon Machine Image (AMI)
Storage Engine WiredTiger
Sharding Range Based Sharding
Storage
EBS Storage with provisioned
IOPS/Ephemeral Storage
Instances i2 Series VMs, m2.4xlarge
Backup & Snapshot EBS storage, S3 Storage
ETL Jobs ASP.NET, MongoDB C# Driver
Hadoop MongoDB Hadoop Connector
Hadoop Cluster
MasterNode
/NameNode
Master
Node/
Name Node
Data Node
Data Node
Data Node
Data Node
Data Node
Data Node
AWS EAST-A
AWS EAST-B
m2.4xlarge
68 GB Ram
EBS: 2TB
m4.xlarge
8 GB Ram
EBS: 50GB
m2.4xlarge
68 GB Ram
EBS: 2TB
m2.4xlarge
68 GB Ram
EBS: 2TB
m2.4xlarge
68 GB Ram
EBS: 2TB
m2.4xlarge
68 GB Ram
EBS: 2TB
m2.4xlarge
68 GB Ram
EBS: 2TB
m4.xlarge
8 GB Ram
EBS: 50GB
13
•  About Cardtronics
•  Enterprise Data Warehouse
•  Lessons Learned, Best Practices developed or followed, Next steps
14
Implementing EDW using MongoDB
Lessons Learned, Best Practices Developed
15
•  Disk
§  Disk I/O is a key performance consideration for a MongoDB system
•  Sharding
§  Select a good shard key
•  Bulk Inserts
§  Apply best practices for bulk inserts
•  Upgrade Strategy
§  Take advantage of the latest features
§  Stability updates or bug fixes
•  Roles and Responsibilities
•  Aggregation Framework vs Map Reduce
Next Step
16
•  MongoDB/Hadoop Deployment across cloud provider (AWS à Azure)
•  MongoDB Connector for BI
•  MongoDB Ops Manager
•  Services:
•  Fraud Detection System
Discussion
17
•  Questions?
MongoDB World
Pre-conference training: June 27th
Conference: June 28th-29th
Hilton Midtown
1335 6th Avenue
New York, NY 10019
19
Code “Evenings” "JakeAngerman" gets 25% off
Super Early Bird Registration Ends March 25, 2016
June 28 - 29, 2016
New York, NY
www.mongodbworld.com

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MongoDB Evenings Houston: Implementing EDW Using MongoDB by Purvesh Patel, Chief Technology Officer, Cardtronics

  • 1. Implementing EDW using MongoDB 22 March 2016 1 Purvesh Patel Cardtronics
  • 2. •  About Cardtronics •  Enterprise Data Warehouse •  Lessons Learned, Best Practices developed or followed, Next steps 2 Implementing EDW using MongoDB
  • 3. Who and Where We Are The World’s Leading ATM Owner and Operator 168,274 U.S. and Puerto Rico locations 15,735 U.K. locations 1,406 Mexico locations 3,255 Canada locations 1,121 Germany locations Global ATM Operations 24 Carnival Cruise Ships & the Caribbean Nearly 190,000 ATMs Key Partnerships At Cardtronics, we provide cash and financial services in the most convenient locations, connecting consumers, retailers and financial institutions where cash meets commerce. 3
  • 4. What We Do •  Purchasing •  Installation & placement •  All makes & models •  Exterior signage, toppers, etc. •  Transaction processing •  Network settlement and dispute resolution •  Multiple language support •  99% ATM Uptime •  Monitoring & incident management •  First and second line maintenance •  24/7 service hotline •  Cash provisioning •  Cash forecasting •  Cash management •  Marketing campaign management •  One-to-one marketing •  On-screen advertising •  Image deposit Cardtronics provides a virtual á la carte menu of ATM services solutions. From processing transactions via our proprietary EFT platform, to fully-turnkey ATM programs, Cardtronics has the right solution for any business. 4
  • 5. How Our ATM Solutions Drive Customer Traffic •  Make the ATM a stage for purchase decisions –  Mobile products deliver incentives to visit the store/use the ATM –  Surcharge-free products eliminate the largest barrier to ATM use –  ATM Marketing converts the ATM visit to a sale or other event SurchargedOut-of-Store Surcharge-Free Products & Allpoint Mobile / Web FeeAlert Bank Marketing ATM Marketing Direct-to- Consumer Network Branding 5
  • 6. •  About Cardtronics •  Enterprise Data Warehouse •  Lessons Learned, Best Practices developed or followed, Next steps 6 Implementing EDW using MongoDB
  • 7. Problems We Were Trying To Solve 7 §  Technology Challenges §  Enterprise Application Scaling §  Real-Time + Analytics Workload §  Performance, Scalability §  High Availability, Upgrades §  Advanced Security •  Business Challenges §  Just as many formats (ATM Transactions, POS Transactions, Gateway Transactions) §  Can we get analysis in a useful timeframe? §  When we get new data, how fast can we do something with it?
  • 8. Why MongoDB 8 •  Just as many formats §  Dynamic Schemas •  Real-Time + Analytics Workload §  Full, Flexible Index Support and Rich Queries §  Aggregation Framework and MapReduce •  Performance, Scalability §  Sharding for Horizontal Scalability §  Increase capacity as you go §  Cloud Architectures •  High Availability, Upgrades §  Automated replication and failover §  Multi-data center support §  Improved operational simplicity (e.g., HW swaps)
  • 9. Total Cost Of Ownership 9 MongoDB T***data/E**data Capital Expense Commodity Server, Internal Storage (no SAN) SAN, Propriety Hardware Operating Expense $$ $$$$ Licensing Cost Free $$$$$ Time To Market 6-12 Months 18-24 Months Ability to Prototype Yes No Ability to Scale Quickly Yes No/May be Support 24/7 Included with Enterprise License ???? Technology Open Source Proprietary
  • 10. EDW Architecture All in One Data Transformation Process – ETL (Extract, Transform, and Load) All in One Office 4.3 ATM Transactions POS Data Non-Financial Transactions FeeAlert FI Transactions & Enrollment LocatorSearch Click Traffic Enterprise Data Warehouse iDesign Offers & Favorite Transaction TBD - Future ETL Service-oriented Architecture ATMPass CORE ATM Operations - Ops Analytics ATM Operations - Reporting Internal Staff HyperionCAMP ATM Transaction Analysis MapApp Branding Interface Allpoint & Branding Pricing Interface iDesign Offers FI BIN Reports POS Interface FeeAlert LocatorSear ch click traffic TBD - Future IN PIPELINE UNDER CONSTRUCTION Gateway Files COMPLETED Postilion Servers Office Office Office Office European RealTime North America RealTime US Platform RealTime US Pilot RealTime Device Manager Device Manager 10
  • 11. MongoDB Cluster Architecture Mongod Mongos Mongod Mongos Mongod Mongos Mongod Mongos Mongod Config Mongod Mongos Mongod Mongos Config Mongod Mongos Mongod Config AWS EAST-A AWS EAST-B i2.2xlarge 61 GB Ram 2xSSD: RAID-0 Journal on root {w:2} i2.2xlarge 61 GB Ram 2xSSD: RAID-0 Journal on root {w:2} i2.2xlarge 61 GB Ram 2xSSD: RAID-0 Journal on root {w:2} i2.2xlarge 61 GB Ram 2xSSD: RAID-0 Journal on root {w:2} i2.2xlarge 61 GB Ram 2xSSD: RAID-0 Journal on root {w:2} i2.2xlarge 61 GB Ram 2xSSD: RAID-0 Journal on root {w:2} m2.4xlarge 68 GB Ram 2xEBS: 840GB m2.4xlarge 68 GB Ram 2xEBS: 840GB Backup & snapshotting layer m2.4xlarge 68 GB Ram 2xEBS: 840GB 11
  • 12. MongoDB Technology Stack 12 MongoDB Release 3.0.10 Operating System Amazon Machine Image (AMI) Storage Engine WiredTiger Sharding Range Based Sharding Storage EBS Storage with provisioned IOPS/Ephemeral Storage Instances i2 Series VMs, m2.4xlarge Backup & Snapshot EBS storage, S3 Storage ETL Jobs ASP.NET, MongoDB C# Driver Hadoop MongoDB Hadoop Connector
  • 13. Hadoop Cluster MasterNode /NameNode Master Node/ Name Node Data Node Data Node Data Node Data Node Data Node Data Node AWS EAST-A AWS EAST-B m2.4xlarge 68 GB Ram EBS: 2TB m4.xlarge 8 GB Ram EBS: 50GB m2.4xlarge 68 GB Ram EBS: 2TB m2.4xlarge 68 GB Ram EBS: 2TB m2.4xlarge 68 GB Ram EBS: 2TB m2.4xlarge 68 GB Ram EBS: 2TB m2.4xlarge 68 GB Ram EBS: 2TB m4.xlarge 8 GB Ram EBS: 50GB 13
  • 14. •  About Cardtronics •  Enterprise Data Warehouse •  Lessons Learned, Best Practices developed or followed, Next steps 14 Implementing EDW using MongoDB
  • 15. Lessons Learned, Best Practices Developed 15 •  Disk §  Disk I/O is a key performance consideration for a MongoDB system •  Sharding §  Select a good shard key •  Bulk Inserts §  Apply best practices for bulk inserts •  Upgrade Strategy §  Take advantage of the latest features §  Stability updates or bug fixes •  Roles and Responsibilities •  Aggregation Framework vs Map Reduce
  • 16. Next Step 16 •  MongoDB/Hadoop Deployment across cloud provider (AWS à Azure) •  MongoDB Connector for BI •  MongoDB Ops Manager •  Services: •  Fraud Detection System
  • 18. MongoDB World Pre-conference training: June 27th Conference: June 28th-29th Hilton Midtown 1335 6th Avenue New York, NY 10019
  • 19. 19
  • 20. Code “Evenings” "JakeAngerman" gets 25% off Super Early Bird Registration Ends March 25, 2016 June 28 - 29, 2016 New York, NY www.mongodbworld.com