Pentaho and MongoDB Partner to Solve Government Big Data Challenges

Pentaho & MongoDB Partner to Solve
Government Big Data Challenges
December 2013
Bob Gourley
Publisher, CTOvision.com

Will LaForest
Director of Federal, MongoDB

Dave Henry
SVP Enterprise Solutions, Pentaho
1

© 2013, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-7555

Big Data Management
Best Practices for Federal Big
Data Projects
Bob Gourley
Publisher, CTOvision.com

2


Brief Purpose
Research & Reports

A focus on a
new discipline
of “Big Data
Management”

Contribute your
thoughts at
CTOvision.com

3

Intro to top 5
“Best
Practices”
of Federal
Data activities

Invitation to
collaborate
and refine
approaches

A perpetual
draft - your
input is
requested


Update Sources
 Big Data Government Newsletter - reader survey
 2,600 readers
 2% response rate, across Federal agencies

 Review of openly published research by Wikibon, TDWI, IDC, Gartner,
Forrester and of course our own CTOvision
 Review of best practices and use cases from the best vendors in
Enterprise Big Data
 Engagement of the community at events like Strata and Hadoop World

Planning Assumption
The ability to collect, parse, analyze machine data in real time,
whether on premise or in the cloud, will continue to grow

4


Big Data Management

 Agencies are thinking through the right changes to concepts and technologies
 Old approaches still important, but cannot solve emerging problems
 Big Data Management is an evolved discipline which builds on existing data
management approaches to leverage new concepts, technologies and best
practices to optimize mission support
5


Solutions That Require Big Data Management
•
•
•
•
•
•
•
•
•
•

Open Source Information: analysis and integration
Situational Awareness across disparate data sets
Two use cases: “Connect the Dots” and “Needle in Haystack”
Cyber Security: rapid real time analysis of all relevant data
Asset catalog across extensive/dynamic enterprises
Rapid return of geospatial data
Location based push of data
Real time return of relevant search
Real time suggestion of topics
Bioinformatics:
• Human Genome
• Patient location, treatment, outcomes
• Law Enforcement: Predictive Policing
• Data Hub: Unified storage, governance, security, functionality
6


Best Practices in Big Data Management
VISION

STRATEGY

Start with a mission-focused vision. This will vary by organization. Support
to mission will drive everything else. Consider that analytics and Big Data
go together.
Should prioritize and tackle challenges like: Changes to governance
processes, right mix of skills for workforce, learning new technology,
prioritizing which workload types will be handled by which part of the
architecture.

KNOW

DESIGN

Document and continuously improve. Architect to manage data in its
original form. Include right mix of traditional and new in your design. Don’t
assume any one platform will be a solution. Architect to insulate
applications and users from a variety of disparate big data platforms.

EXECUTE

7

Know existing infrastructure and process with focus on: Understanding of
legal/policy dynamics relevant to your agency, understanding of new
capabilities available, current and required throughputs/capacities, types of
workloads supported by each components in the architecture, available
tech choices.

Avoid custom coding wherever possible. Don’t let new Big Data Platforms
become proprietary silos. ETL remains important. Ensure training for all
based on job function. Don’t neglect your own training. Serve the analyst.


Next Steps
 Continue your market surveys, stay aware of what new
technologies can do for you.
 Revisit your vision. As you do, ponder this: How can you leverage
data to support your mission?
 Continue to study use-cases and exchange best practices. Dialog
with others in and out of your sector. Great lessons are coming
from other industries.
 Continue to engage with the broader community. Sign-up for our
Government Big Data Weekly.
 Share your lessons learned.

8


Provide Your Thoughts, Input, Questions
E-mail: bob@ctovision.com
Blog: http://ctovision.com
Twitter: http://www.twitter.com/bobgourley
Facebook, LinkedIn, etc: See the blog

9


The Modern Operational
Database for Government

Will LaForest
Director of Federal, MongoDB

10


The Evolution of Databases

1990

2000

2010

Operational &
Real-time

Online

NoSQL

RDBMS
RDBMS
RDBMS
Datawarehouse

OLAP/BI

OLAP/BI
Hadoop

Offline

11


Relational Database Challenges
Variety

Agile Development

• Unstructured data

• Iterative

• Semi-structured
data

• Short development
cycles

• Polymorphic data

• New workloads

Volume & Velocity

New Architectures

• Petabytes of data

• Horizontal scaling

• Trillions of records

• Commodity
servers

• Millions of queries per
second

12


• Cloud computing

MongoDB
The Modern Operational Database

General
Purpose

13

Document
Oriented


OpenSource

Fully Featured
Rich Queries

• Find Paul’s cars
• Find everybody in London with a car
built between 1970 and 1980

Geospatial

• Calculate the average value of Paul’s
car collection

• Secondary
Native Indexes • Compound
• Geospatial

14

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, … }
}

• Find all the cars described as having
leather seats

Aggregation

{

• Find all of the car owners within 5km of
Trafalgar Sq.

Text Search

MongoDB

• Full Text
• Hash
• Covering


}

MongoDB and Enterprise IT Stack

CRM, ERP, Collaboration, Mobile, BI

Data Management
Online Data

Offline Data

RDBMS
RDBMS

Hadoop

EDW

Infrastructure
OS & Virtualization, Compute, Storage, Network

15


Security & Auditing

Management & Monitoring

Applications

Document Data Model
MongoDB

Relational
{

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, … }
]
}

17


Dynamic Schema
MongoDB does not need any defined data schema.
Every document could have different data

{name: “will”,
eyes: “blue”,
birthplace: “NY”,
aliases: [“bill”, “la
ciacco”],
gender: ”???”,
boss: ”ben”}

18

{name: “jeff”,
eyes: “blue”,
height: 72,
boss: “ben”}

{name: “ben”,
hat: ”yes”}


{name: “brendan”,
aliases: [“el diablo”]}

{name: “matt”,
pizza: “DiGiorno”,
height: 74,
boss: 555.555.1212}

Volume, Velocity, and New Architectures

Automatic Sharding

• Increase or decrease capacity as you go
• Automatic balancing
• Optimized for commodity servers and cloud
infrastructure

20


High Availability

• Automated replication and failover
• 0 down time with hardware failure and upgrades
• Multi-data center support
• Improved operational simplicity (e.g., HW swaps)
• Data durability and consistency
21


MongoDB Performance*

Top 5 Marketing
Firm

Government
Agency

Top 5 Investment
Bank

10+ fields, arrays,
nested documents

20+ fields, arrays,
nested documents

Queries Key-based
1 – 100 docs/query
80/20 read/write

Compound queries
Range queries
MapReduce
20/80 read/write

Compound queries
Range queries
50/50 read/write

Servers ~250

~50

~40

Ops/sec 1,200,000

500,000

30,000

Data Key/value

* These figures are provided as examples. Your application governs your performance.

22


Operational and Analytical Workloads
• Application interacts with primaries
• Analytical workloads on secondaries
• Workloads are isolated from one
another
• Working set appropriate for each
application

24


Global Data Distribution

Real-time

Real-time

Real-time

Real-time

Real-time
Real-time
Real-time

25


Read Global / Write Local
Primary:LON

Secondary:NYC

Primary:NYC

Secondary:SYD

Secondary:LON
Secondary:SYD

Primary:SYD
Secondary:LON
Secondary:NYC

26


Solving Big Data
Challenges in the
Federal Government
Dave Diegtel
Head of Federal Sales, Pentaho

27


Why Pentaho for Federal Government
•

•

Business Model and Subscription: Pentaho’s Subscription Model and
Server-based pricing allows for lower upfront investment and risk compared to
legacy BI vendors who traditionally cost an average of 4X for similar size
deployments.

•

Government Certifications: Pentaho has made significant investments in
Government Certifications and Compliance such as 508 and Security.

•

Open API’s and extensible architecture enable ease of integration and
reduce potential for vendor lock-in.

•

28

Company and Product Maturity: Pentaho has been around for over 9 years,
with 1,000’s of paid customers, and 5.0 Version release. Pentaho is proven
and less risky.

Existing Government Customers and Cleared Personnel


A Comprehensive Big
Data Platform

Dave Henry
Senior VP Enterprise Solutions, Pentaho

29


Pentaho 5.0 Architected for the Future
Simplified analytics experience for all users

Billing Customer

Social
Media

Analytics

Existing & New
Data Infrastructure
& Processes

Web

Location Network

ANY Data
•
•
•
•

30

Relational
Operational
Big Data
Data sources not yet
anticipated…

ANY Environment
•
•
•
•
•

Data warehouses
Data marts
Stack vendors
Cloud
Embedded


ANY Analytics
•
•
•
•
•

Reports
Dashboards
Visualizations
Discovery
Predictive

The New Reality
Simplified analysis for all users

Simplified
Analytics
Experience

Blended
Big Data

Enterprise
Big Data
Integration

31


Pentaho & MongoDB Enable Key Use Cases
Customer 360 and Device Data Analytics enable comprehensive
insight

…
• MongoDB delivers Scalable,
Low-Latency Enterprise Data
Store

Mission
Scope

• Visual ETL development with
Pentaho Data Integration
(PDI)
• Reporting, Dashboards,
Visualization and Discovery
with Pentaho Analytics

32


Pentaho Data
Integration
Pentaho Analytics
• Reporting
• Dashboards
• Visualization
• Discovery
Pentaho Data
Integration

Enterprise Customer Data Store
Powerful data integration for MongoDB

Customer
Master

PDI ETL
POS Data

Web Event
Data

$push to data arrays

33


mongoDB
cluster

Data Integration
Exploits MongoDB’s native APIs and query language

34


Operational Reports
Multi-page, highly formatted reports – real-time, scheduled or burst
to email

35


Operational Dashboards
Highly tailored, pixel-perfect dashboards on MongoDB

36


Analyzer
Explore and visualize data

37


James Dixon
Founder and CTO, Pentaho

38


As CTO at Pentaho, James Dixon is
responsible for Pentaho's
architecture and technology
roadmap. James has over 15 years
of professional experience in
software architecture, development
and systems consulting. Prior to
Pentaho, James held key technical
roles at AppSource Corporation
(acquired by Arbor Software which
later merged into Hyperion
Solutions) and Keyola (acquired by
Lawson Software). Earlier in his
career, James was a technology
consultant working with large and
small firms to deliver the benefits of
innovative technology in real-world
environments.

Why Pentaho?
• Pentaho is the best platform to connect, integrate, and analyze both
traditional sources and MongoDB
• Pentaho embraces and extends the MongoDB environment with rich
visualization and exploration of data
• Pentaho’s Subscription-based business model lowers upfront investments,
enabling faster ROI
• Pentaho has dozens of Federal Government Customers and made
significant investments in government certifications and cleared personnel
• Pentaho and MongoDB are established partners – Pentaho carefully
engineers its products to use the latest MongoDB APIs to provide the best
possible performance

39


Next Steps and Q&A
• Needs Assessment with Pentaho and MongoDB
• Dave Diegtel - ddiegtel@pentaho.com
• Will LaForrest - will@mongodb.com
• Try Pentaho (30 Free Trial) -- pentaho.com/download
• Learn More about Big Data and Government Solutions
• Pentaho
• Big Data Website: pentahobigdata.com/
• Government Solutions: pentaho.com/solutions/government

• MongoDB:
• Government Solutions: mongodb.com/industries/government
• Big Data: Examples and Guidelines for the Enterprise Decision Maker
mongodb.com/lp/whitepaper/big-data-nosql
• MongoDB Top 5 Considerations When Evaluating NoSQL Databases
mongodb.com/lp/whitepaper/nosql-considerations

• Sign-up for the Big Data Government Newsletter at CTOvision.com &
take reader survey
40


Thank You

41


Pentaho and MongoDB Partner to Solve Government Big Data Challenges

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