MongoDB IoT City Tour LONDON: Hadoop and the future of data management. By, Mark Lewis, Cloudera

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Mark Lewis, Senior MArketing Director EMEA, Cloudera. Hadoop and the Future of Data Management. As Hadoop takes the data management market by storm, organisations are evolving the role it plays in the modern data centre. Explore how this disruptive technology is quickly transforming an industry and how you can leverage it today, in combination with MongoDB, to drive meaningful change in your business.

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Hadoop and the Future of Data Management
Mark J. Lewis
Senior Director Marketing
Europe, Middle East & Africa
Twitter: @markjlewis

9
Information Driven
9
All of these organizations
are

10
Information Driven
10
All of these organizations
needed to change to become

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Expanding Data Requires A New Approach
1980s
Bring Data to Compute
Compute
Data
Data
©2014 Cloudera, Inc. 11 All rights reserved.
Now
Bring Compute to Data
Relative size & complexity
Data
Information-centric
businesses use all data:
Multi-structured,
internal & external data
of all types
Compute
Compute
Compute
Process-centric
businesses use:
• Structured data mainly
• Internal data only
• “Important” data only
Compute
Compute
Data
Data

4
3
2
1
12
The Old Way: Bringing Data to Compute
Complex Architecture
• Many special-purpose
systems
• Moving data around
• No complete views
Cost of Analytics
• Existing systems strained
• No agility
• “BI backlog”
Time to Data
• Up-front modeling
• Transforms slow
• Transforms lose data
Missing Data
• Leaving data behind
• Risk and compliance
• High cost of storage
EDWS MARTS SERVERS DOCUMENTS STORAGE SEARCH ARCHIVE
ERP, CRM, RDBMS, MACHINES FILES, IMAGES, VIDEOS, LOGS, CLICKSTREAMS EXTERNAL DATA SOURCES
©2014 Cloudera, Inc. 12 All rights reserved.

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From Hadoop to an Enterprise Data Hub
Open Source
Scalable
Flexible
Cost-Effective
✔
Managed ✖
✔
✔
✔
Open
Architecture ✖
Secure and
Governed ✖
CLOUDERA’S ENTERPRISE DATA HUB
©2014 Cloudera, Inc. 13 All rights reserved.
3RD PARTY
APPS
STORAGE FOR ANY TYPE OF DATA
UNIFIED, ELASTIC, RESILIENT, BATCH
PROCESSING
MAPREDUCE
ANALYTIC
SQL
IMPALA
SEARCH
ENGINE
SOLR
MACHINE
LEARNING
SPARK
STREAM
PROCESSING
SPARK STREAMING
WORKLOAD MANAGEMENT YARN
FILESYSTEM
HDFS
ONLINE NOSQL
HBASE
MANAGEMENT
CLOUDERA NAVIGATOR
DATA
MANAGEMENT
CLOUDERA MANAGER
SYSTEM
, SECURE
SENTRY

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The New Way: Bringing Compute to Data
2
SERVERS MARTS EDWS DOCUMENTS STORAGE SEARCH ARCHIVE
ERP, CRM, RDBMS, MACHINES FILES, IMAGES, VIDEOS, LOGS, CLICKSTREAMS ESTERNAL DATA SOURCES
©2014 Cloudera, Inc. All rights reserved.
Diverse Analytic Platform
• Bring applications to data
• Combine different workloads on
common data (i.e. SQL + Search)
• True analytic agility
4
1
3 4
15
Active Compliance Archive
• Full fidelity original data
• Indefinite time, any source
• Lowest cost storage
1
Persistent Staging
• One source of data for all analytics
• Persist state of transformed data
• Significantly faster & cheaper
2
Self-Service Exploratory BI
• Simple search + BI tools
• “Schema on read” agility
• Reduce BI user backlog requests
3

16
Your Journey to Achieve Full Potential
Operational Efficiency Information Advantage
Exploration Data Science
ETL
Acceleration
Cheap
Storage
EDW
Optimization
IT Business
©2014 Cloudera, Inc. All Rights Reserved.
Consolidation
360° View
Advance from Strategy to ROI with Best Practices and Peak Performance

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80% of Those Surveyed Are Planning,
or Have Already Begun Big Data Projects
Are Augmenting or Replaced
Existing Infrastructure (46% of all Respondents):
0% 20% 40% 60%
ETL process (Extract Transform Load)
Analytic databases
Storage
EDW (Enterprise Data Warehouse)
Mainframes
©2014 Cloudera, Inc. All rights reserved.
Key Insight:
Multiple
Overlapping
Use Cases
Require
Converged
Analytics
Source: King Research survey, September 2013, 3,922 Respondents

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Thank you!
Mark Lewis
Twitter @markjlewis
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Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe. This talk covers: Common components of an IoT solution The challenges involved with managing time-series data in IoT applications Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance. How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.

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Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.

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MongoDB IoT City Tour LONDON: Hadoop and the future of data management. By, Mark Lewis, Cloudera

1. 1 Hadoop and the Future of Data Management Mark J. Lewis Senior Director Marketing Europe, Middle East & Africa Twitter: @markjlewis

2. 22

3. 33

4. 44

5. 55

6. 66

7. 77

8. 88

9. 9 Information Driven 9 All of these organizations are

10. 10 Information Driven 10 All of these organizations needed to change to become

11. 11 Expanding Data Requires A New Approach 1980s Bring Data to Compute Compute Data Data ©2014 Cloudera, Inc. 11 All rights reserved. Now Bring Compute to Data Relative size & complexity Data Information-centric businesses use all data: Multi-structured, internal & external data of all types Compute Compute Compute Process-centric businesses use: • Structured data mainly • Internal data only • “Important” data only Compute Compute Data Data

12. 4 3 2 1 12 The Old Way: Bringing Data to Compute Complex Architecture • Many special-purpose systems • Moving data around • No complete views Cost of Analytics • Existing systems strained • No agility • “BI backlog” Time to Data • Up-front modeling • Transforms slow • Transforms lose data Missing Data • Leaving data behind • Risk and compliance • High cost of storage EDWS MARTS SERVERS DOCUMENTS STORAGE SEARCH ARCHIVE ERP, CRM, RDBMS, MACHINES FILES, IMAGES, VIDEOS, LOGS, CLICKSTREAMS EXTERNAL DATA SOURCES ©2014 Cloudera, Inc. 12 All rights reserved.

13. 13 From Hadoop to an Enterprise Data Hub Open Source Scalable Flexible Cost-Effective ✔ Managed ✖ ✔ ✔ ✔ Open Architecture ✖ Secure and Governed ✖ CLOUDERA’S ENTERPRISE DATA HUB ©2014 Cloudera, Inc. 13 All rights reserved. 3RD PARTY APPS STORAGE FOR ANY TYPE OF DATA UNIFIED, ELASTIC, RESILIENT, BATCH PROCESSING MAPREDUCE ANALYTIC SQL IMPALA SEARCH ENGINE SOLR MACHINE LEARNING SPARK STREAM PROCESSING SPARK STREAMING WORKLOAD MANAGEMENT YARN FILESYSTEM HDFS ONLINE NOSQL HBASE MANAGEMENT CLOUDERA NAVIGATOR DATA MANAGEMENT CLOUDERA MANAGER SYSTEM , SECURE SENTRY

14. 1144

15. 15 The New Way: Bringing Compute to Data 2 SERVERS MARTS EDWS DOCUMENTS STORAGE SEARCH ARCHIVE ERP, CRM, RDBMS, MACHINES FILES, IMAGES, VIDEOS, LOGS, CLICKSTREAMS ESTERNAL DATA SOURCES ©2014 Cloudera, Inc. All rights reserved. Diverse Analytic Platform • Bring applications to data • Combine different workloads on common data (i.e. SQL + Search) • True analytic agility 4 1 3 4 15 Active Compliance Archive • Full fidelity original data • Indefinite time, any source • Lowest cost storage 1 Persistent Staging • One source of data for all analytics • Persist state of transformed data • Significantly faster & cheaper 2 Self-Service Exploratory BI • Simple search + BI tools • “Schema on read” agility • Reduce BI user backlog requests 3

16. 16 Your Journey to Achieve Full Potential Operational Efficiency Information Advantage Exploration Data Science ETL Acceleration Cheap Storage EDW Optimization IT Business ©2014 Cloudera, Inc. All Rights Reserved. Consolidation 360° View Advance from Strategy to ROI with Best Practices and Peak Performance

17. 17 80% of Those Surveyed Are Planning, or Have Already Begun Big Data Projects Are Augmenting or Replaced Existing Infrastructure (46% of all Respondents): 0% 20% 40% 60% ETL process (Extract Transform Load) Analytic databases Storage EDW (Enterprise Data Warehouse) Mainframes ©2014 Cloudera, Inc. All rights reserved. Key Insight: Multiple Overlapping Use Cases Require Converged Analytics Source: King Research survey, September 2013, 3,922 Respondents

18. 18 Thank you! Mark Lewis Twitter @markjlewis 18

Editor's Notes

Welcome, and thank you for joining us today to learn about Hadoop and the Future of Data Management. Cloudera is the leading provider of software and services built on Apache Hadoop. Our mission is to help our customers Ask Bigger Questions, leveraging all of their data. What do I mean by that? Let’s take a look at some examples of Cloudera customers going beyond traditional analytics.
Monsanto & John Deere Optimizing crop yield Massive plant / genetics database
Disney ILM Cost of storage Feasibility No analytics on storage
Large Investment / Commercial Bank $100 MM Teradata expansion 300,000 ETL flows Now in use by five business units: Card services analytics; EDW; Fraud; GIS; Quantitative Risk
Retail / Commodity Trading – Skybox Ports and parking lots Skybox uses satellite images to tell traders how busy specific ports are for different types of commodity. It can also tell retailers how busy their shopping areas are by counting the numbers of vehicle in their own and competitors’ parking lots.
Government and security Combining online and internal database data to identify suspicious behaviors. Detect network intrusions from foreign and domestic attackers.
Healthcare Patient 360 Structured and unstructured patient data, cohort data, research data.
Insurance – Allstate 80 years of data across all BUs and geographies Internal and external, traffic patterns, weather, telematic data, EDW data Universal data archive Tune pricing models and products to the individual
There’s a common thread here. All of these companies are information-driven. Of course, this isn’t a new concept.
Left: What other infrastructure are you looking to augment or replace with Big Data solutions (Evaluating / planning)? Right: What other infrastructure have you augmented or replaced with Big Data solutions (doing Big Data project)?
Oh, and one more thing… Kimball webinar. Come by our booth.

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