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1 
Hadoop and the Future of Data Management 
Mark J. Lewis 
Senior Director Marketing 
Europe, Middle East & Africa 
Twitter: @markjlewis
22
33
44
55
66
77
88
9 
Information Driven 
9 
All of these organizations 
are
10 
Information Driven 
10 
All of these organizations 
needed to change to become
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
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 
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
1144
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 
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 
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 
Thank you! 
Mark Lewis 
Twitter @markjlewis 
18

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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

Notes de l'éditeur

  1. 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.
  2. Monsanto & John Deere Optimizing crop yield Massive plant / genetics database
  3. Disney ILM Cost of storage Feasibility No analytics on storage
  4. 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
  5. 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.
  6. Government and security Combining online and internal database data to identify suspicious behaviors. Detect network intrusions from foreign and domestic attackers.
  7. Healthcare Patient 360 Structured and unstructured patient data, cohort data, research data.
  8. 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
  9. There’s a common thread here. All of these companies are information-driven. Of course, this isn’t a new concept.
  10. 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)?
  11. Oh, and one more thing… Kimball webinar. Come by our booth.