The document provides an agenda and overview for a briefing on using MongoDB and JasperSoft for enterprise reporting. It discusses MongoDB's data model of flexible documents, query model with rich queries and analytics functions. It then outlines use cases of JasperSoft and MongoDB together for data hubbing from various sources, real-time analytics dashboards, and examples of customers using the integrated solution.
6. 6
MongoDB Use Cases
Big Data Product & Asset
Catalogs
Security &
Fraud
Internet of
Things
Database-as-a-
Service
Mobile
Apps
Customer Data
Management
Data
Hub
Social &
Collaboration
Content
Management
Intelligence Agencies
Top Investment and
Retail Banks
Top US Retailer
Top Global Shipping
Company
Top Industrial Equipment
Manufacturer
Top Media Company
Top Investment and
Retail Banks
10. 10
Document Model Benefits
• Agility and flexibility
– Data model supports business change
– Rapidly iterate to meet new requirements
• Intuitive, natural data representation
– Eliminates ORM layer
– Developers are more productive
• Reduces the need for joins, disk seeks
– Programming is more simple
– Performance delivered at scale
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Do More With Your Data
MongoDB
Rich Queries
• Find Paul’s cars
• Find everybody in London with a car
built between 1970 and 1980
Geospatial
• Find all of the car owners within 5km of
Trafalgar Sq.
Text Search
• Find all the cars described as having
leather seats
Aggregation
• Calculate the average value of Paul’s
car collection
Map Reduce
• What is the ownership pattern of colors
by geography over time? (is purple
trending up in China?)
{
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’,
location:
[45.123,47.232],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
}
}
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Why Jaspersoft/MongoDB ?
• Decision Making – Visualise your MongoDB data
– Meaningful interpretation of what lies beneath
– Bring the MongoDB value proposition to non-technical
Decision Makers
• Direct to MongoDB
– Integration for every use case
– Native Connectors & ETL
• Utilise the power of MongoDB Aggregation
Framework
– Push down to MongoDB aggregation
– Don’t need to add another layer of complexity
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1. Data hub, replicating and consolidating data
from operational and EDW sources, allowing for
cross-function, complete 360-degree view
reporting and visualization
2. Data store enabling RT analytics and
dashboards to be generated against live,
operational data
MongoDB for Online Big Data
20. 20
Unified data services
… Benefit
• Each application can
still save its own
data
• Data is already
aggregated for cross-
silo reporting
• One cluster and data
access layer to
manage
Equities Systems
FI Systems
Derivatives
Systems
…
Reporting
……
21. 21
Jaspersoft: The Bridge to UDS
…
Equities Systems
FI Systems
Derivatives
Systems
…
EmbeddedinApplications
……
ETL
ETL
Benefit
• Phased approach
• Data blending
• Real-Time reporting
• Embedded
• Access native
functionality
• Each application can
still save its own
data
• Data is already
aggregated for cross-
silo reporting
• manage
RDBMS
New Apps
22. 22
Customer Examples
• Ericsson – success story
complete
• Clockwork Solutions –
success story complete
• Kansys – success story in
edit
• Ogilvy & Mather
• Scala
• Triumph Learning
• Masternaut
• Sagezza
• Nexgen
• CGR
• Turkey Ministries
23. 23
For More Information
Resource Location
MongoDB Downloads mongodb.com/download
Free Online Training education.mongodb.com
Webinars and Events mongodb.com/events
White Papers mongodb.com/white-papers
Case Studies mongodb.com/customers
Presentations mongodb.com/presentations
Documentation docs.mongodb.org
Additional Info info@mongodb.com
Resource Location
Customer Data Management (e.g., Customer Relationship Management, Biometrics, User Profile Management)
Product and Asset Catalogs (e.g., eCommerce, Inventory Management)
Social and Collaboration Apps: (e.g., Social Networks and Feeds, Document and Project Collaboration Tools)
Mobile Apps (e.g., for Smartphones and Tablets)
Content Management (e.g, Web CMS, Document Management, Digital Asset and Metadata Management)
Internet of Things / Machine to Machine (e.g., mHealth, Connected Home, Smart Meters)
Security and Fraud Apps (e.g., Fraud Detection, Cyberthreat Analysis)
DbaaS (Cloud Database-as-a-Service)
Data Hub (Aggregating Data from Multiple Sources for Operational or Analytical Purposes)
Big Data (e.g., Genomics, Clickstream Analysis, Customer Sentiment Analysis)
MongoDB aims to blend the scalability and performance of K/V stores with the rich query functionality of the RDBMS
With a document model, MongoDB can provide the flexible schema demanded by modern applications, supporting structured, semi structured, unstructured and polymorphic data. Through embedding data using sub-documents and arrays, they eliminate the need for expensive JOINs, enabling simple scaling across multiple nodes.
At the same time, support for powerful query operators, the aggregation framework and rich secondary indexes, users do not trade away the ability to run complex queries against data. MongoDB can do this in real time, across multi-structured data sets
Here we have greatly reduced the relational data model for this application to two tables. In reality no database has two tables. It is much more common to have hundreds or thousands of tables. And as a developer where do you begin when you have a complex data model?? If you’re building an app you’re really thinking about just a hand full of common things, like products, and these can be represented in a document much more easily that a complex relational model where the data is broken up in a way that doesn’t really reflect the way you think about the data or write an application.
Rich queries, text search, geospatial, aggregation, mapreduce are types of things you can build based on the richness of the query model.
In terms of reporting, A number of Business Intelligence (BI) vendors have developed connectors to integrate MongoDB as a data source with their suites, alongside traditional relational dbs. This integration provides reporting, visualizations, dash-boarding of MongoDB data
Mix in Real Time with All Use Case coverage – Mike B