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MongoDB AnalyticsDiverse Use Cases

– Diverse Architectures

Jaspersoft

Ben ConnorsWorldwide Head of Alliances

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Example MongoDB use cases and analytics MongoDB Analytics Architectures

Advantages Challenges

Q & A

Agenda

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Connecting to MongoDB

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Different access techniques to match diverse requirements

Use each technique individually or combine the ones you need

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Example MongoDB Use Cases

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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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Diverse Users

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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

Jaspersoft BI High-Level Architecture

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Native Direct Access

Nat

ive

Native connectors enable reporting without batch

processing or moving data

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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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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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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

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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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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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Move & Combine Data Using ETL

ETL

ETL Jobs connect Big Data, relational and other sources into a

single data warehouse

ETL

ETL

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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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Demo

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Summary

Multiple Use Cases = Multiple Technical Approaches Native Semantic layer Federated ETL

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