Lightning Talk: Real-Time Analytics from MongoDB
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Transcript of Lightning Talk: Real-Time Analytics from MongoDB
© Copyright 2000-2014 TIBCO Software Inc.
MongoDB AnalyticsDiverse Use Cases
– Diverse Architectures
Jaspersoft
Ben ConnorsWorldwide Head of Alliances
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©2014 TIBCO Corporation .
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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