In this webinar, Carl W. Olofson, Research Vice President, Application Development and Deployment for IDC, and Dale Kim, Director of Industry Solutions for MapR, will provide an insightful outlook for Hadoop in 2015, and will outline why enterprises should consider using Hadoop as a "Decision Data Platform" and how it can function as a single platform for both online transaction processing (OLTP) and real-time analytics.

1. © 2014 MapR Techno©lo g2i0e1s4 MapR Technologies 1
Hadoop in 2015: Keys to Achieving
Operational Excellence for the
Real-Time Enterprise

2. © 2014 MapR Technologies 2
Today’s Speakers
• Carl Olofson
IDC Research Vice President
Database Management and Data Integration Software Research
• Dale Kim
MapR Director of Industry Solutions

3. Keys to Achieving Operational Excellence
with Hadoop
Carl Olofson
Research Vice President
IDC
Sponsored by MapR

4. Agenda
 Hadoop Adoption Drivers
 Common Uses of Apache Hadoop Today
 Desired: the Real Time Enterprise
• Levels of Decision Management
• The Ideal: A Single Decision Data Platform
• Requirements for the Decision Data Platform
 Enabling the Decision Data Platform: MapR
Enterprise Database Edition
 Conclusions/Recommendations
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5. Challenge: the Accelerating Pace of
Business
 The Internet Impact
• The Amazon Effect:
Instant Transactions
• Online Banking:
Reduced “Float” Time
• Online Trading:
Instant Decisions
• Globalization: No
Batch Windows
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6. Challenge: Big Data
 Big Data Technologies
• New Low Cost Ingestion and
Analysis
 Hadoop / MapReduce
 Graph Database
• Enhanced Old Friends
 Scalable Data Warehouse
 Large Scale IMDB
 Big Data Opportunities
• Social Media Analysis
 Customer Sentiment
 New Market Opportunities
• Machine Data (Sensors)
 Improve Efficiency, Service
 Better Utility Pricing
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7. Unified Information Management Architecture
Structured
Unstructured
Rich Media
ERP
CRM
PLM
SCM
HR
Clickstream
Spreadsheets
XML
Forms
Industry
standards
(UCCNet, HL7
SWIFT, FIX)
RSS
Documents
Email
Annotations
Image
Video
Audio
Executives
Managers
Financial
Analysts
Business
Analysts
Quantitative
Analysts
LOB Staff
Suppliers
Partners
Customers
Government
IT Staff
Web
Unified
Access
and
Analysis
Semi-structured
Unified
Mgmt
for retention,
security,
master data
management
Enriched
Metadata
(Semantic
+
Structured)
7
Single
Decision
Data
Platform

8. The Real Time Enterprise
 Dynamically Responds to Business Requirements
• Acts on events as they happen.
• Can change data and operations to fit shifting business
needs.
• Ingests large amounts of data, including from Big Data
sources, and makes that data actionable.
 Such Speed and Flexibility Require
• Agile process definitions and application execution.
• Ability to store and retrieve data at the speed of business.
• Ability to support real-time decisioning.
8

9. Real-Time Decisioning
 Strategic
• Deep analytic models and data
warehouse reporting inform executive
decision making
• Data analysts comb tons of data,
executive management makes the
decisions
 Operational
• Project or product-focused data is
collected and analyzed
• End-users run queries against data
marts and adjust their project or product
plans
 Tactical
• Immediately available current data and
streaming data are presented.
• Line and field staff, or automated
computer algorithms, make decisions.
9

10. Decision Management Framework
Tactical decisions
must apply the policy
or rule in a specific
case, which lends itself
to automation
Operational decisions
focus on a specific
project or process and
result in the formation of
a type of policy or rule
that drives tactical
decisions.
Strategic decision set
the long-term directions
for the organization, a
product, a service, or an
initiative and result in
guidelines within which
operational decisions are
made
Scope and Degree of Risk
Strategic
Decisions
Operational
Decisions
Tactical
Decisions
Degree of Automation
Level of Collaboration
Number of Decisions

11. Hadoop and the Real Time Enterprise
Real Time BI
Composite Data
Framework
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CEP
Engine
Streaming
Data
Business Action
Enterprise Analytics
IMDB
High Speed Transactions and
Automated Decisioning
Integrated
Business
Process
Apps
Data
Warehouse
Hadoop

12. The Decision Data Platform Today
Real Time BI
Composite Data
Framework
© IDC Visit us at IDC.com and follow us on Twitter: @IDC 12 12
CEP
Engine
Streaming
Data
Business Action
Enterprise Analytics
IMDB
High Speed Transactions and
Automated Decisioning
Integrated
Business
Process
Apps
Data
Warehouse
Hadoop

13. Hadoop as the Decision Data Platform
Real Time BI
Composite Data
Framework
© IDC Visit us at IDC.com and follow us on Twitter: @IDC 13 13
CEP
Engine
Streaming
Data
Business Action
Enterprise Analytics
IMDB
High Speed Transactions and
Automated Decisioning
Integrated
Business
Process
Apps
Data
Warehouse
Hadoop

14. Hadoop as the Decision Data Platform
 Constraints in Apache Hadoop
• Serial HDFS is too slow for low latency random retrieval
• Lack of simple, integrated, high speed load mechanism
• Reliable standard data access methods, including SQL (Hive is
lacks rigor and performance).
 Requirements for the Decision Data Platform
• Simplified administration
• High performance support for standard data access methods
• Acceleration of Hadoop data ingestion
• Comprehensive data security
• High speed random data retrieval capability
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15. MapR as the Decision Data Platform
 Simplified administration
• Blends YARN with “zero downtime” features and point-in-time snapshots in
a single management environment.
 High performance support for standard data access methods
• Data is accessible by NFS, through Apache Drill, and native HBase.
 Acceleration of Hadoop data ingestion
• Leverages Apache Spark for high speed ingestion.
 Comprehensive data security
• Applies Access Control Expressions (ACEs) to manage permissions,
Kerberos authentication, standard NSA Suite B cryptology.
 High speed random data retrieval capability
• The MapR core file system is indexed for random retrieval, and designed to
support both wide column format and true random data access.
© IDC Visit us at IDC.com and follow us on Twitter: @IDC 15

16. Conclusions/Recommendations
 Conclusions
• Business imperatives are leading to adoption of both Big Data and
Real Time Enterprise technology.
• Businesses would like to use the scalability and affordability of
Hadoop as the decision data platform.
• Apache is constrained in a number of key areas in this regard.
 Recommendations
• There are many architectural alternatives for the decision data
platform, but Hadoop offers many advantages, and should be
explored.
• Seek a Hadoop configuration that meets all the requirements
discussed for a decision data platform.
• Consider MapR Enterprise Database Edition as a possible option
for building such a platform.
© IDC Visit us at IDC.com and follow us on Twitter: @IDC 16

17. © 2014 MapR Techno©lo g2i0e1s4 MapR Technologies 17
Hadoop in 2015: Keys to Achieving
Operational Excellence for the
Real-Time Enterprise

18. Big Data is Overwhelming Traditional Systems
© 2014 MapR Technologies 18
• Mission-critical reliability
• Transaction guarantees
• Deep security
• Real-time performance
• Backup and recovery
• Interactive SQL
• Rich analytics
• Workload management
• Data governance
• Backup and recovery
ENTERPRISE
USERS
Enterprise
Data
Architecture
OPERATIONAL
SYSTEMS
ANALYTICAL
SYSTEMS
PRODUCTION
REQUIREMENTS
PRODUCTION
REQUIREMENTS
OUTSIDE SOURCES

19. Hadoop Relieves the Pressure from Enterprise Systems
Keys for Production Success
1 Reliability and DR
3 High performance
© 2014 MapR Technologies 19
OPERATIONAL
SYSTEMS
ANALYTICAL
SYSTEMS
ENTERPRISE
USERS
• Data staging
• Archive
• Data
transformation
• Data exploration
• Streaming,
interactions
2 Interoperability
4
Supports operations
and analytics
Enterprise
Data
Architecture

20. © 2014 MapR Technologies 20
Different Workloads in Same Environment
Operational (e.g., databases)
• Real-time, low latency (in
ms)
• Reads/writes/updates/delete
s on subset of rows of live
data
• Typically CPU bound
Analytical (e.g., Hadoop)
• Batch (in seconds, hours, days)
• Large reads and writes on
historical data sets
• Typically I/O bound
Two workloads with conflicting requirements and goals

21. © 2014 MapR Technologies 21
Handling Different Workloads
Characteristics
• Separate clusters
– Data movement
– Data duplication
– Functional duplication (e.g., data
governance)
• Different technologies
• Different people and skillsets
Issues
• Higher operational cost
• Greater risk for error
• Consistency/data protection
challenges
• Longer time-to-insight, missed
opportunities to respond
Workload-specific systems break under weight of big data

22. NEW APPLICATIONS SLAs TRUSTED INFORMATION LOWER TCO
© 2014 MapR Technologies 22
Architecture Matters for Success
FOUNDATION
Data protection
& security
High
performance
Multi-tenancy
Operational &
Analytical
Workloads
Open standards
for integration

23. © 2014 MapR Technologies 23
Mobile
application server
Web
application server
Multiple Workloads Today
Data exploration
(SQL)
Analytics DB Operations
Hadoop Batch DBMS
import/export
Customer 360
dashboard
Churn analysis
(predictive
analytics)

24. © 2014 MapR Technologies 24
Mobile
application server
Product/service
optimization and
personalization
Data exploration
(SQL)
Customer 360
dashboard
Churn analysis
(predictive
analytics)
•Single cluster
•High performance, low latency
• Large-scale analytics
•Enterprise-grade HA/DR
•Unified file and table administration
Real-time ad
targeting
Real-Time and DB Operations
Actionable
Analytics
Web
application server
Operational Analytics

25. © 2014 MapR Technologies 25
How MapR Addresses Multiple Workloads
Characteristics
• Single cluster for data
storage/processing
– No data duplication
– No data movement
– No functional duplication
– Less architectural complexity
• High performance, consistent
low-latency database
• Large-scale Hadoop analytics
“close to the live data”
• Enterprise-grade HA/DR
• Unified file and table admin
Benefits
• Lower operational cost
• Architectural simplicity, lower
risk for error
• Faster time-to-insight, faster
ability to respond
• Consistent responsiveness
• More efficient use of resources,
lower TCO
• Uptime you expect from an
enterprise system
• Lower administrative effort
“Operational Analytics”

26. Operations + Analytics = Real-time, Personalized Services
© 2014 MapR Technologies 26
Real-time Operational Applications
Fraud model
Recommendations
table
MapR Distribution including Hadoop
Fraud
investigator
Interactive
marketer
Online
transactions
Fraud
detection
Personalized
offers
Clickstream
analysis
Fraud
investigation tool
Analytics

27. Lambda Architecture (lambda-architecture.net)
© 2014 MapR Technologies 27
BATCH VIEWS
BATCH LAYER
SERVING LAYER
SPEED LAYER
MERGE
ALL DATA
(HDFS)
HADOOP
BATCH
RECOMPUTE
PROCESS
STREAM
REAL-TIME VIEWS
INCREMENT
VIEWS
STORM
Partial
aggregate
REAL-TIME
INCREMENT
Partial
aggregate
Partial
aggregate
MERGED
VIEW
(HBASE)
REAL-TIME DATA
NEW DATA
STREAM
PRECOMPUTE
VIEWS
(MAPREDUCE)

28. © 2014 MapR Technologies 28
Other Common Use Cases
• Enterprise Data Hub
– Multiple data sources, users groups
– Large-scale querying to complement data warehouses
• Streaming applications
– File system for canonical, immutable data
– Database for low latency, real-time event processing/querying/analytics
• Security analytics
– Machine learning models on historical event data
– Database for real-time incident detection and prevention

29. © 2014 MapR Technologies 29
MapR-DB – Fast, Integrated NoSQL
Operational
Analytics
Resilience/Business
Continuity
Performance/Scale
Hadoop-enabled architecture
• Operational/architectural
simplicity, analytics on
live data
• “Wide-column” data
model with
HBase/C/Thrift APIs (run
existing HBase apps)
• Hadoop-scale (petabytes)
Proven production readiness
• Enterprise-grade HA/DR
• Consistent snapshots
• Integrated security
Consistent, and at any scale
• High throughput (100M
data points per second
ingestion, 4-node cluster)
• Consistent low latency, no
compaction delays, even
at 95th and 99th
percentiles (< 10ms in
YCSB)

30. © 2014 MapR Technologies 30
Q & A
Engage with us!
@mapr maprtech
dalekim@mapr.com
MapR
maprtech
mapr-technologies