Hortonworks Oracle Big Data Integration

© Hortonworks Inc. 2011 – 2014. All Rights Reserved
We do Hadoop together.

Modern Data Architecture for
Data Transformation and Acquisition
with Oracle® and Apache™
Hadoop®

Quick Housekeeping
Q&A box is available for your questions
Webinar will be recorded for future viewing
Thank you for joining!

Your Presenters
• Jeff Pollock
– Vice President, Product Management, Oracle
– Previously responsible for IBM InfoSphere Information
Integration & Governance products
– Author of “Semantic Web for Dummies” and "Adaptive
Information”
• Tim Hall
– Vice President, Product Management, Hortonworks
– Previously responsible for Oracle’s outbound product
management covering the Business Process
Management Suite, SOA Suite

Today’s Topics
• Drivers for the Modern Data Architecture
• New Analytic Applications for New Types of Data
• Hadoop as the solution for Data Lake
• Hortonworks and Oracle Data Integration teaming up
• Oracle patterns for successful Data Reservoirs
• Oracle Data Integration Strengths in Hadoop
• Oracle Data Governance for Hadoop
• Q&A

Poll: Where are you in your Hadoop journey?
1. Researching our options
2. Currently evaluating some software
3. Deep in a trial
4. In production with a Hadoop cluster
5. What’s Hadoop?

A Data Architecture Under Pressure From New DataAPPLICATIONSDATASYSTEM
REPOSITORIES
SOURCES
Existing Sources
(CRM, ERP, Clickstream, Logs)
RDBMS EDW MPP
Business
Analytics
Custom
Applications
Packaged
Applications
Source: IDC
2.8 ZB in 2012
85% from New Data Types
15x Machine Data by 2020
40 ZB by 2020
OLTP, ERP, CRM Systems
Unstructured documents, emails
Clickstream
Server logs
Sentiment, Web Data
Sensor. Machine Data
Geolocation

Hadoop Within An Emerging Modern Data Architecture
OPERATIONS TOOLS
Provision,
Manage &
Monitor
DEV & DATA TOOLS
Build &
Test
DATASYSTEM
REPOSITORIES
SOURCES
RDBMS EDW MPP
OLTP, ERP,
CRM
Systems
Documents,
Emails
Web Logs,
Click
Streams
Social
Networks
Machine
Generated
Sensor
Data
Geolocation
Data
Governance
&Integration
Security
Operations
Data Access
Data Management
APPLICATIONS
Business
Analytics
Custom
Applications
Packaged
Applications

Clickstream
Capture and analyze
website visitors’ data
trails and optimize
your website
Sensors
Discover patterns in
data streaming
automatically from
remote sensors and
machines
Server Logs
Research logs to
diagnose process
failures and prevent
security breaches
New types of dataHadoop Value:
Sentiment
Understand how
your customers feel
about your brand
and products –
right now
Geographic
Analyze location-
based data to
manage operations
where they occur
Unstructured
Understand patterns
in files across
millions of web
pages, emails, and
documents

New Analytic Applications For New Types Of Data
$
• Supplier Consolidation
• Supply Chain and Logistics
• Assembly Line Quality Assurance
• Proactive Maintenance
• Crowdsourced Quality Assurance
• New Account Risk Screens
• Fraud Prevention
• Trading Risk
• Maximize Deposit Spread
• Insurance Underwriting
• Accelerate Loan Processing
• Call Detail Records (CDRs)
• Infrastructure Investment
• Next Product to Buy (NPTB)
• Real-time Bandwidth
Allocation
• New Product Development
• 360° View of the Customer
• Analyze Brand Sentiment
• Localized, Personalized
Promotions
• Website Optimization
• Optimal Store Layout
Financial
Services
Retail Telecom Manufacturing
Healthcare
Utilities,
Oil & Gas
Public
Sector
• Genomic data for medical trials
• Monitor patient vitals
• Reduce re-admittance rates
• Store medical research data
• Recruit cohorts for
pharmaceutical trials
• Smart meter stream
analysis
• Slow oil well decline curves
• Optimize lease bidding
• Compliance reporting
• Proactive equipment repair
• Seismic image processing
• Analyze public sentiment
• Protect critical networks
• Prevent fraud and waste
• Crowdsource reporting for
repairs to infrastructure
• Fulfill open records requests

… And Incrementally Delivers A ‘Data Lake’
Data Lake
• An architectural shift in the
data center that uses
Hadoop to deliver deeper
insight across a large,
broad, diverse set of data at
efficient scale
SCALE
SCOPE
A Modern Data Architecture/Data Lake
New Analytic Apps
New types of data
LOB-driven
RDBMS
MPP
EDW
Governance
&Integration
Security
Operations
Data Access
Data Management

The Modern Data Architecture
Oracle Data Integration
• Eliminates need for
separate ETL engine –
and associated H/W,
admin, overhead
• Non-invasive realtime
data staging into Hadoop
• Streamlines development
by providing capability to
separate Logical from
Physical mappings
• Reduces risk and
compliance exposure via
comprehensive data
governance

Oracle & Hortonworks
YARN Ready Partner
Certified on latest release of
Hortonworks Data Platform
Sandbox tutorial
Tutorial for
HWX Sandbox
Coming Soon!
ORCL Sandbox
Here Now!

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Data Integration & Governance
14
Dynamic Data Movement
– Low impact capture
– Continuous data staging
Data Transformation
– Bulk data movement
– Pushdown data processing
Data Federation
– Virtualized Data Services
Data Quality & Verification
– Fix quality at the source
– Verify data consistency
Metadata Management
– Lineage and Impact Analysis
– Business Glossary Semantics
Data Governance
Foundation
Oracle Data Integrator
(Transformation)
Enterprise Data Quality
(Profile, Cleanse, Match and De-duplicate)
Fast
Load
Oracle GoldenGate
(Movement)
Enterprise Metadata Management & Business Glossary
(Business Glossary, Data Lineage, Impact Analysis and Data Provenance)
Data Service Integrator
(Federation)
GoldenGate Veridata
(Online Data Verification)
ELT Processing
on Hadoop or SQL
Continuous Availability
Comprehensive capabilities for the end-to-end data integration
and governance of all data – including Hadoop based data

Leverage Wide Range of Modern Analytic Styles
How to Succeed With a Big Data Reservoir
15
Do:
– Directly link to a Line of Business
initiative
– Iterate on short cycles, plan for
small high-value deliverables along
the way
– Use tools, not only custom coded
programs
Do Not:
– Start with a techie-led research
project w/out a biz objective
– Over promise business results on
the market hype alone
– Assume MapReduce is the answer
to all your technical challenges
DBMS
(on prem or cloud)
Data First
Analytics
Model First
Analytics
Streaming
Analytics

Maximizing benefits:
1. Schema on Read
2. Cheaper Compute
3. Cheaper Storage
3 Core Patterns of Big Data Reservoir Success
16
DBMS
(on prem or cloud)
Sandbox
ETL Offload
Staging
Deep Data
Storage
Data Sandbox:
– Leader: Line of Business (LoB)
– Value: Faster access to business data, Faster
time to value on Analytics
– Innovation: Schema-on-read empowers
rapid staging and Data Discovery
ETL Offload:
– Leader: Information Technology (IT)
– Value: Cost avoidance on DW/Marts
– Innovation: YARN/Hadoop empowers lower
cost compute and lower cost storage
Deep Data Storage:
– Leader: Risk / Compliance (LoB)
– Core Value: High fidelity aged data
– Innovation: SQL on Hadoop engines enable
very low cost, queryable data access
Data First
Analytics
Model First
Analytics
Streaming
Analytics

Oracle Approach to Big Data Integration is Superior
17
DBMS
(on prem or cloud)
Sandbox
ETL Offload
Staging
Deep Data
Storage
Data Governance
Foundation
(Transformation)
Oracle GoldenGate
(Movement)
GoldenGate Veridata
Oracle GoldenGate:
– Non-invasive data capture
– Low-latency data movement
– Full or partial records staging
– Most proven integration tool worldwide
Oracle Data Integrator:
– No ETL engine is required
– Logical design separate from physical
– Deploys in Hadoop or off cluster
– Many options for movement
Metadata & Glossary:
– Search Driven
– Business Friendly
– Huge 3rd Party Support
– Automated Metadata Stitching
Data First
Analytics
Model First
Analytics
Streaming
Analytics

Oracle GoldenGate Capabilities for Big Data
18
HDFS (Files)
HBase (NoSQL)
Hive / Hive Streaming (SQL)
Flume & Storm (Streaming)
Kafka (MPP Pub/Sub)
Spark Streaming (Machine Learning)
Capture Database Transactions and
Deliver to Big Data in Real-Time
Capture
Trail
Route
Deliver
Pump
GoldenGate

Business Value of the GoldenGate Approach
19
Continuous Data Staging
– Don’t make the business wait
– CDC is by default, not an add-on
– Least invasive on sources
– Hadoop staging is fresh
Integrated, Native Capture
– Don’t create unnecessary risk
– Keep current with DB patches
– Certainty around licensing
– Proven best performance
Most Widely Proven
– 1000’s of customers
– Most demanding high volume
– Used for High Availability (HA)
– Dependable results
vs.
Batch Data Movement
– Typical ETL vendors all default to batch data
movement in their reference architectures
– Changed Data is an immature add-on
– ETL loading into Hadoop is mainly “batch mode”
Clumsy & Risky Data Capture
– Not in sync with Oracle Database versions
– Some can “talk the talk” but their CDC tech can’t
touch Oracle GoldenGate scale/performance
– Patches and Licensing create business risk
Niche, Low-End
– Some vendors only cover a few platforms
– Some vendors are broad, but don’t scale
– Few vendors have the reliability and dependability
to cover HA use cases
vs.
vs.
…the “Other Vendors”

Oracle Data Integrator (ODI) Capabilities for Big Data
20
Flume
Hive on MR, Tez, Spark
Logs
OLTP DB
SQOOP
OGG
Pig on MR, Tez, Spark
ODI
SQOOP
Any DW
OGG
Spark
Oozie
OEDQ OEMM
Data Validation
& Cleansing
Metadata Mgmt
& Lineage
API/File
Hive/HCat,
HDFS,HBase
Hive/HCat,
HDFS,HBase
NoSQL
Flume
Map once at the logical level, and then choose which Big Data or
Hadoop framework you want to run in!

Business Value of ODI: Low Cost and High Dev Efficiency
21
No ETL engine is
required
Separation of
Logical and
Physical design
Physical exec on
SQL, Hive, Pig, or
Spark
Runtime exec in
Oozie or via ODI
Java Agent
Rich set of pre-
built operators
User defined
functions
Eliminate your ETL Engines and improve Developer efficiency –
now, everybody can be a Big Data developer!

Hadoop Cluster
Spark
Sqoop
Hive
Pig
ODI
Oozie
Sqoop
Data Flow Approaches to Big Data Integration
22
Hadoop Cluster
Spark
Sqoop Sqoop
Hive
Pig
Manual Code
Hadoop Cluster
ETLETL HDFS
Hadoop Cluster
ETLETLETL
HDFS
1. Traditional ETL Tools
(execute entirely outside of Hadoop)
2. ETL Tools with Native “on” Hadoop
(require proprietary code on Data Nodes)
3. Manual Coding
(ultimate flexibility, but at a very high cost)
4. ODI Native in Hadoop
(no ETL Engine & no Data Node footprint)
ETL
*small ODI Agent may optionally install off cluster or
on Name Node, no dependencies on Data Nodes
GG
BEST

Oracle Metadata Management & Glossary for Big Data
23
Comprehensive Data Lineage
Business Friendly Navigation
Business & IT Collaboration
Easy to Use, Search Driven

Value of Metadata and Business Glossary
My dashboard
does not match
this report…why?
Where did
this data
come
from?Where can I find
the data I need for
analytics?
Which ETL mappings or
BI Reports will be
affected by my column
change?
What systems does
the data flow
through?
TRUSTED DATA IT CERTAINTY

Big Data Governance Lifecycle Tooling
25
Operational Data Flows
Business Sources
Quality KPIs Case
Management
Governance Cockpit for Data Stewards & Stakeholders
Exception
Review
Metadata
Management
Business
Glossary
Design Time
Support People and Processes with an end-to-
end tooling capability!

…to manage Risk/Compliance
 Records retention
 Rediscovery
 Litigation support
 Data access management
 Information security and protection
Minimize corporate liability through proper
governance of data
…to drive Business Value
 Metadata discovery
 Metadata & glossary cataloging
 Data profiling
 Data cleansing lifecycle
 Data remediation
Maximize opportunity by ensuring trusted
data is easily available for data driven
business processes
26
The Data Governance Opportunity with Big Data
Solving business and IT data challenges

Most Heterogeneous, Deep 3rd Party Coverage
27
 Hadoop HBase
 Hadoop Hive/Flume
 HP Enscribe
 HP NonStop
 HP Neoview
 Hypersonic SQL
 IBM DB2 i Series
 IBM DB2 UDB
 IBM DB2 z Series
 IBM Informix
 IBM Netezza
 JMS / MQ
 Microsoft Access
 Microsoft SQLServer
 MySQL
 Pivotal Greenplum
 PostgreSQL
 Salesforce.com
 SAP BW / BI
 SAP ERP / ECC
 SAS
 SQL/MP
 SQL/MX
 Sybase ASE
 Sybase IQ
 Teradata
 Adaptive
 Altova
 Apache Hcatalog
 Apache Hive/HQL
 Borland
 CA ERwin
 Cloudera Impala
 COBOL Copybook
 DataStax
 Embarcadero
 EMC ProActivity
 GentleWare
 Google BigQuery
 Grandite
 Hadapt Hive
 Hortonworks Hive
 IBM Cognos
 IBM DB2
 IBM DataStage
 IBM Discovery
 IBM Federation Server
 IBM Lotus Notes
 IBM Netezza
 IBM Rational Rose
 IBM Rational Architect
 Informatica Metadata Mgr.
 Informatica PowerCenter
 CoSORT
 ISO SQL Standard (DDL)
 MapR Hadoop Hive
 MicroFocus
 Microsoft Access
 Microsoft Office Excel
 Microsoft Visio
 Microsoft SQL Server
 Microsoft SSIS
 Microsoft Visual Studio
 Microstrategy
 Magic Draw
 OMG CWM Standard
 OMG UML Standard
 Oracle BI Answers
 Oracle BI Enterprise Edition
 Oracle BI Server
 Oracle DAC
 Oracle Data Integrator
 Oracle Data Modeler
 Oracle Database
 Oracle Designer
 Oracle Hyperion Applications
 Oracle Hyperion Essbase
 Oracle Warehouse Builder
 Pivotal Greenplum
 PostgreSQL
 QlikView
 SAP BO Crystal Reports
 SAP BO Designer
 SAP BO Desktop Intelligence
 SAP BO Repository
 SAP BO Data Integrator
 SAP BO Data Steward
 SAP Master Data Management
 SAP Sybase PowerDesigner
 SAP Sybase ASE Database
 SAS Data Integration Studio
 SAS BI Server
 SAS Information Map
 SAS Metadata Management
 SAS OLAP Server
 Select
 Sparx Architect
 Syncsort
 Tableau
 Talend
 Teradata
 Tigris
 Visible
 W3C DTD & XSD Schema
Operational Integration (Movement / Transformation) Metadata Harvesting (Glossary, Lineage & Impact Analysis)
 Oracle Database
 Oracle Exadata
 Oracle Big Data Appliance
 Oracle TimesTen
 Oracle OLAP
 Oracle Business Intelligence
 Oracle BI Applications
 Oracle E-Business Suite
 Oracle JD Edwards Enterprise One
 Oracle JD Edwards World
 Oracle Fusion Applications
 Oracle Governance Risk and Compliance
 Oracle Fusion AIA
 Oracle Retail Applications
 Oracle Agile BI / DW
 Oracle Agile PLM for Process
 Oracle iFlex FlexCUBE
 Oracle iFlex Mantas
 Oracle Hyperion Applications
 Oracle PeopleSoft
 Oracle Siebel CRM / OnDemand
 Oracle Communications
 Oracle WebLogic Server
 Oracle Coherence Data Grid
 Oracle SOA Suite
 Oracle Enterprise Service Bus
+ open APIs and standards
based meta-model
No other vendor can compare:
• 50+ systems for Operational Integration
• 70+ systems for Metadata Harvesting

Data Governance
Foundation
Differentiated Technical Approach from Oracle
28
Dynamic Data Movement
– Real-time by default, not ETL
– Least invasive on sources
– Proven best performance
– Native Oracle integration
No ETL Engines
– Take processing to the data;
don’t move the data
– Leverage the data engines for
workloads (Hadoop or SQL)
Most Heterogeneous
– Leverage open source Hadoop,
not proprietary distributions
– Hadoop is the Hub, not ETL tools
– Open metadata standards
(Transformation)
Fast
Load
Oracle GoldenGate
(Movement)
Data Service Integrator
(Federation)
GoldenGate Veridata
ELT Processing
on Hadoop or SQL
Continuous Availability
Comprehensive capabilities for the end-to-end data integration
and governance of all data – including Hadoop based data

Question & Answer session will be conducted electronically,
using the panel to the right of your screen
About Oracle and Hortonworks
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