1. Jaspersoft
MongoDB Analytics
Diverse Use Cases –
Diverse Architectures
Ben Connors
Worldwide Head of Alliances
© Copyright 2000-2014 TIBCO Software Inc.

2. Agenda
 Example MongoDB use cases and analytics
 MongoDB Analytics Architectures
 Advantages
 Challenges
 Q & A
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3. 3
Connecting to MongoDB
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Different access techniques to match diverse
requirements
Use each technique individually or combine the ones
you need

4. 4
Example MongoDB Use Cases
 Historical Pattern Analysis
 Trends in sentiment analysis
 Seasonality of searches
 Operational Reporting
 Clickstream analysis
 Sensor data
 Content management analysis
 Data Exploration
 Research
 Causal analysis
 Multi-Source Analysis
 Revenue patterns correlating to external factors
 Political event reaction
 Pure science
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5. Diverse Users
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6. Fast Insights
“Success in the Big
Data era is about
more than size. It’s
about getting insight
from these huge data
sets more quickly”
Doug Henschen
InformationWeek
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7. Jaspersoft BI High-Level Architecture
7
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8. Native Direct Access
Native
Native connectors enable
reporting without batch
processing or moving data
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9. Native Direct Access Pros/Cons
Pros Cons
Leverage full underlying functionality Limited tool choices
Query Latency (depends on type) Memory limits, fault tolerance
No bulk movement of data Security
E.g. Operational Reporting
• Clickstream analysis
• Sensor data
• Geospatial analytics
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10. Semantic Layer for Simplified Access
Enable self-service BI by defining a
semantic layer on top of your
MongoDB and other sources
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11. Semantic Layer Pros/Cons
Pros Cons
Simplified access for users Performance overhead
Self-service, ad hoc No access to underlying functionality
No bulk movement of data
Low latency queries
E.g. Data Exploration
• Research
• Causal analysis
©2014 TIBCO Corporation . 11

12. Data Federation for Rapid Blending
Combine relational sources with
MongoDB without copying or moving
data using the a Data Virtualization
engine
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13. Data Federation Pros/Cons
Pros Cons
Simplified access for users Performance overhead
Self-service No access to underlying functionality
No bulk movement of data Network overhead at query time
Low latency queries
Quickly (& cheaply) combine
MongoDB + operational data for better
insights
E.g. Multi-Source Analysis
• Revenue patterns correlating
to external factors
• Political event reaction
• Pure science
• Operational + CRM data
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14. Move & Combine Data Using ETL
ETL Jobs connect Big
Data, relational and
other sources into a
single data warehouse
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15. ETL Pros/Cons
Pros Cons
Full RDBMS functionality Added expense for software
Low latency queries Very high latency ETL process
Self-service No access to underlying functionality
No network overhead at query time Bulk movement of data
Maintain use of SQL tools Added operational complexity
Insert data cleansing process
Combine multiple data sources
E.g. Historical Pattern Analysis
• Trends in sentiment analysis
• Seasonality of searches
• Combine disparate data patterns
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16. Demo
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17. Summary
 Multiple Use Cases = Multiple Technical Approaches
 Native
 Semantic layer
 Federated
 ETL
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18. Try Out Jaspersoft
Easy
 Download 30-day Evaluation at Jaspersoft.com
 Connect to your own data
 Go to Jaspersoft.com/getting-started
Easier
 Try the Hosted version – NO INSTALLATION!
 Play with sample reports & data
 Go to Jaspersoft.com/jaspersoft-live-trial
©2014 Jaspersoft Corporation. 18