This presentation gives a detailed overview of Oracle's Big Data Appliance, Hadoop, NoSQL Database and Big Data Connectors.

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Big Data
Jean-Pierre Dijcks
Team Lead – Big Data Product Management

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Agenda
 Big Data Implementation Patterns
 Big Data Products
 Q&A

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Big Data Implementations

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Big Data Usage Pattern
ETL and Batch Processing Workloads on Hadoop
Integrate
SQL
SQL
NoSQL
• Scalable
• Flexible
• Cost
Effective
DW & BI
Analytics
Web

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Ad-hoc
Big Data Usage Pattern
Scale-out Information Discovery
• Online
• Scalable
• Flexible
• Cost
Effective
Data Factory
Continuous On-Demand

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Big Data Usage Pattern
Expand Data Warehouse with Granular Data Store
MartsData Warehouse
Σ Σ
Business
Intelligence
Archiving
• Online
• Scalable
• Flexible
• Cost
Effective
Data Factory

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Big Data Usage Pattern
Instant Responses to Streaming Data based on Historical Analysis
Data Warehouse
Business
Intelligence
• Online
• Scalable
• Flexible
• Cost
Effective
Data Factory
Event Decisions
NoSQL

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Oracle Big Data Solution
Stream Acquire – Organize – Analyze
In-Database
Analytics
Data
Warehouse
Oracle
Advanced
Analytics
Oracle
Database
Oracle BI
Enterprise Edition
Oracle Real-Time
Decisions
Endeca Information
Discovery
Decide
Oracle Event
Processing
Apache
Flume
Applications
Oracle
NoSQL
Database
Cloudera
Hadoop
Oracle R
Distribution
Oracle Big Data
Connectors
Oracle Data
Integrator
• Complete
• Integrated
• Scalable

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Big Data Products

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Big Data Appliance X3-2
Sun Oracle X3-2L Servers with per server:
• 2 * 8 Core Intel Xeon E5 Processors
• 64 GB Memory
• 36TB Disk space
Totals per Full Rack:
• 288 Processor Cores
• 1152 GB of Memory
• 648TB Available Disk space

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Big Data Appliance Software Stack
Integrated Software:
 Oracle Linux 5.8 with UEK
 Cloudera CDH 4.2 & Cloudera Manager 4.5
 Big Data Appliance Enterprise Manager Plug-In
 Oracle R Distribution
All integrated software is supported as part of Premier Support for
Systems and Premier Support for Operating Systems
Optional Software:
 Oracle NoSQL Database 2.x
 Oracle Big Data Connectors 2.x

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BDA in Infrastructure as a Service
 Procurement option for H/W
 Low monthly fee spread out
over 3 to 5 years
 Ownership of the system
stays with Oracle
 Applies to all Engineered
Systems
 BDA Full Racks only
Month

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Big Data Appliance Product Family
 Starter Rack is a fully cabled and
configured for growth with 6 servers
 In-Rack Expansion delivers 6 server
modular expansion block
 Full Rack delivers optimal blend of
capacity and expansion options
 Grow by adding rack – up to 18 racks
without additional switches

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Big Data Appliance X3-2 Starter Rack
 6 Nodes fully cabled in Starter Rack
• 96 Intel® Xeon® E5 Processors
• 384 GB total memory
• 216TB total raw storage capacity
 6 Nodes In-Rack Expansion added in-rack
• 96 Intel® Xeon® E5 Processors
• 384 GB total memory
• 216TB total raw storage capacity
Start and grow in increments of six servers

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Why Oracle Big Data Appliance?
 Beats DIY Clusters on:
– Initial Cost and Time to Value
– Performance and Scalability
 Pre-configured with leading Hadoop Distribution
– Proven at large scale
– Contributors across all components for better support
 Better Integration with your Oracle ecosystem with:
– High-performance connectivity to Exadata
– Unified analytics API (SQL, R, MapReduce etc.)
– Single Enterprise Manager Framework

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Divide Full Rack BDA in
multiple clusters
Provide more flexible
configurations for
customers
Automatic reconfiguration
when expanding the
cluster
Flexible Configurations
6 Node Cluster
12 Node Cluster
Example Configuration

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Engineered for Quicker Time to Value at Lower Cost
http://www.oracle.com/us/corporate/analystreports/industries/esg-big-data-wp-1914112.pdf
ESG believes that a "buy" versus "do-it-yourself"
approach will yield roughly one-third faster time-
to-market benefit improvement...
0
5
10
15
20
25
30
Oracle Big Data Appliance Build it yourself
Time to Market (Weeks)
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
Oracle Big Data Appliance Build it yourself
Cost: Initial Infrastructure/Tasks
[…] nearly 40% cost savings versus IT
architecting, designing, procuring, configuring, an
d implementing its own big data infrastructure.

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Engineered for Performance
Compared with a DIY Cluster
0
5
10
Big Data
Appliance
DIY Hadoop
Cluster
Time(hours)
 Configured for exceptional
performance on delivery
 6x faster than custom 20-node
Hadoop cluster for large batch
transformation jobs
 Engineering done by Oracle and
Cloudera:
– OS and File System Tuning
– Java Virtual Machine Tuning
– Hadoop Configuration and Setup
6x

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Engineered by Oracle and Cloudera
Why Cloudera and Cloudera CDH?
 Proven Track Record with the largest Hadoop Installed Base
 Proven in large scale enterprise implementations
 Demonstrated Leadership in Hadoop Community
– Breath and Depth across the Hadoop ecosystem and products
– Fast evolution in critical features
 Managed Distribution
– Components certified to work together and on Oracle Big Data Appliance in
regular updates
– Industry Leading Management Framework for Hadoop integrated with
Oracle Enterprise Manager

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Engineered by Oracle and Cloudera
 Cloudera’s Hadoop Knowledge Engineered into the system:
– Master service lay-out designed for large clusters based on
experience with many large systems
– Optimized data block size for MapReduce workloads
– Optimized number of Map and Reduce slots fitting the system
capacity
– Optimized settings for a large number of Hadoop parameters
 Tested at Oracle and Cloudera on the same hardware/software
stack as our customers
Market Leading Hadoop Distribution Pre-configured

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Engineered by Oracle and Cloudera
 Multi-Homing for Hadoop
– To leverage BDA’s InfiniBand and 10GiGE network, Hadoop needed to be able to
support multiple networks and IP addresses
– Committed to Apache Hadoop by Cloudera
 Highly Available NameNode Solution
– Remove dependency on a HA Filer to enable HA without required additional
hardware
– Build a journaling based HA solution for NameNode with automatic fail-over
 System Administration
– Tight integration between Oracle Enterprise Manager (Hardware and High-Level
Software Monitoring) and Cloudera Manager (Hadoop Details)
Driving Enterprise Class Requirements for Hadoop

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Integrated Management Framework
Management Infrastructure combines EM and CM
Quick view of Hardware and Software status
in Oracle Enterprise Manager

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Big Data Connectors
Optimized integration of Hadoop with Oracle Database
and Oracle Exadata
• Oracle Loader for Hadoop
• Oracle SQL Connector for Hadoop Distributed File System
(HDFS)
• Oracle Data Integrator Application Adapter for Hadoop
• Oracle R Connector for Hadoop
• Does not require Big Data Appliance – can be licensed for Hadoop
running on non-Oracle hardware

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Analyze Data across your Oracle Systems
SQL Analytics on ALL data
SQL
Hadoop Oracle Database
IB
 Expand the data pool for
analytics leveraging Hadoop
 Stream Hadoop resident data
through Big Data Connectors
for SQL processing
 Use the full power of Oracle
SQL on all data
 Or use Oracle Loader for
Hadoop to integrate data in
Oracle Database

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Analyze Data across your Oracle Systems
R Analytics on ALL data
R
Hadoop Oracle Database
IB
 Expand the data pool for
analytics leveraging Hadoop
 Improve scalability and
performance for R without
changes to your programs
 Dynamically leverage Hadoop
through Big Data Connectors
to execute R analytics

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Oracle Data Integrator
Simplify Map Reduce
OLH
&
OSCH
Oracle
Data
Integrator
 Automatically generates
MapReduce code
 High performance loads into
Data Warehouse leveraging
both OLH and OSCH
 Manages the process across
platforms

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Oracle NoSQL Database
Scalable, Highly Available, Key-Value Database
Application
Storage Nodes
Datacenter B
Storage Nodes
Datacenter A
Application
NoSQL DB Driver
Application
NoSQL DB Driver
Application
 Simple Key-Value Data Model
 Horizontally Scalable
 Highly Available
 Simple administration
 ACID Transactions at scale
 Transparent load balancing
 Elastic Configuration
 Commercial grade software and
support

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Oracle NoSQL Database Use Cases
NoSQL DB Driver
Application
Oracle Event
Processor
Event
Stream
Web Scale Transaction Processing
• High velocity, High volume, High variety, Low information density data capture
• Uses Hadoop and/or Data Warehouse for analytics
• Applications: Web browsing, Web Retail, CDR processing, Sensor data capture
Last Mile Content Delivery
• Platform for real-time content delivery
• Content & market segmentation Acquired and Analyzed in Hadoop & RDBMS
• NoSQL provides low latency content lookup and delivery to end-customers
• OEP rules perform low latency lookups to Oracle NoSQL DB for additional data
Real Time Event Processing
• Real time events trigger rule execution in Oracle Event Processing
• OEP rules perform low latency lookups to Oracle NoSQL DB for additional data
• OEP actions are triggered
• Applications: Medical Monitoring, Factory Automation, Oil & Gas, Geo-location
Rule Action

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