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MongoDB Inc. Proprietary and Confidential
7 million downloads 1,000 active subscribers 150,000
- nline
A Big Data Arsenal for the 21 st Century Matt Asay VP, Marketing - - PowerPoint PPT Presentation
A Big Data Arsenal for the 21 st Century Matt Asay VP, Marketing - - PowerPoint PPT Presentation
A Big Data Arsenal for the 21 st Century Matt Asay VP, Marketing & Business Development, MongoDB MongoDB Inc. Proprietary and Confidential 7 million downloads 150,000 online education registrations 1,000 active subscribers 20,000
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What We Don’t Do
“The relational database market is a $9 billion a year market. I want to shrink it to $3 billion and take a third of the market.”
- Marten Mickos
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What We Do
Enable a Generation of Innovative, Modern Applications Previously Impossible Or Too Difficult to Achieve
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The Big Data Unknown
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Top Big Data Challenges?
Translation? Most struggle to know what Big Data is, how to manage it and who can manage it
Source: Gartner
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- More than 90% of today’s data was created in the
last 2 years
- Moore’s Law for data: Doubles at regular intervals
Big Data Is Sort of a Matter of Volume
2250 4500 6750 9000 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
1 4 10 24 55 120 250 500 1,000 2,150 4,400 9,000
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Big(ger) Is the New Normal
2014 2013 2011 2010 2009 2008
2007
2006
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Understanding Big Data – It’s Not Very “Big”
from Big Data Executive Summary – 50+ top executives from Government and F500 firms
64% - Ingest diverse, new data in real-time 15% - More than 100TB
- f data
20% - Less than 100TB (average of all? <20TB)
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Modern, Big Data Is Messy
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Data Now Looks Like This
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And This
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And This
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Doesn’t Fit Neatly into a “Spreadsheet”
- 90% of the world’s data
was created in the last two years
- 80% of enterprise data
is unstructured
- Unstructured data
growing 2X faster than structured
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Back in 1970…Cars Were Great!
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So Were Computers!
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Lots of Great Innovations Since 1970
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New Tools for New Data
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Innovation As Iteration
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“I have not failed. I've just found 10,000 ways that won't work.”
― Thomas A. Edison
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Must Be Open Source
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Must Not Require Big Upfront Payment
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Must Not Penalize Success
“Clients can also opt to run zEC12 without a raised datacenter floor -- a first for high-end IBM mainframes.” IBM Press Release 28 Aug, 2012
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Must Not Impede Iteration
New Table New Table New Column Name Pet Phone Email New Column
3 months later…
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Must Be Based on Industry Standards
DB-Engines.com Database Ranking
Ranking Database Type Score Changes 1 Oracle Relational 1491.8
- 8.43
2 MySQL Relational 1290.21 1.83 3 Microsoft SQL Relational 1205.28
- 8.99
4 PostgreSQL Relational 235.06 4.61 5 MongoDB Document 199.99 4.81 6 DB2 Relational 187.32
- 1.14
7 Microsoft Access Relational 146.48
- 6.4
8 SQLite Relational 92.98
- 0.03
9 Sybase Relational 81.55
- 6.33
10 Cassandra Wide Column 78.09
- 2.23
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Must Be Easy to Find Skills
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Must Be Easy to Learn/Use
“Organizations already have people who know their own data better than mystical data scientists….Learning Hadoop [or MongoDB] is easier than learning the company’s business.” (Gartner, 2012)
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When To Use Hadoop, Modern Databases
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Enterprise Big Data Stack
EDW Hadoop
Management & Monitoring Security & Auditing
RDBMS CRM, ERP, Collaboration, Mobile, BI OS & Virtualization, Compute, Storage, Network RDBMS
Applications Infrastructure Data Management Online Data Offline Data
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Consideration – Online vs. Offline
- Long-running
- High-Latency
- Availability is lower priority
- Real-time
- Low-latency
- High availability
Online Offline
vs.
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Hadoop Is Good for…
Risk Modeling Churn Analysis Recommendation Engine Ad Targeting Transaction Analysis Trade Surveillance Network Failure Prediction Search Quality Data Lake
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MongoDB/NoSQL Is Good for…
360° View of the Customer Mobile & Social Apps Fraud Detection User Data Management Content Management & Delivery Reference Data Product Catalogs Machine to Machine Apps Data Hub
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How To Use The Two Together?
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Finding Waldo
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Predictive Analytics
Government
- Predictive analytics system
for crime, health issues
- Diverse, unstructured (incl.
geospatial) data from 30+ agencies
- Correlate data in real-time
- Long-form trend analysis
- MongoDB data dumped into
Hadoop, analyzed, re-inserted into MongoDB for better real- time response
Algorithms
MongoDB + Hadoop
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Machine Learning
Ad-Serving
- Catalogs and products
- User profiles
- Clicks
- Views
- Transactions
- User segmentation
- Recommendation engine
- Prediction engine
Algorithms
MongoDB Connector for Hadoop
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- Modern data is messy
- Your data infrastructure must support iteration
- Modern data infrastructure market is crowded
– But clear winners are distinguishing themselves – Bet on general purpose over niche, popular over
- bscure, open source over proprietary
- Use MongoDB + Hadoop together
Remember…
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