Insights into Real World Data Management Challenges

Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |
Insights into Real World Data Management
Challenges
Noble Raveendran
Principal Sales Consultant
Big Data & Analytics Presales
noble.r.raveendran@oracle.com

Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for
information purposes only, and may not be incorporated into any contract. It is not a
commitment to deliver any material, code, or functionality, and should not be relied upon
in making purchasing decisions. The development, release, and timing of any features or
functionality described for Oracle’s products remains at the sole discretion of Oracle.
Confidential – Oracle Internal/Restricted/Highly Restricted 2

Agenda
1. Market Opportunity & Data Trends
2. Key Customer Challenges
3. Customer Use Cases & Examples
4. Product Demonstration
5. Cloud Platform for Big Data
6. Summary

1. Market Opportunity & Data Trends

Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 5
Cloud based Big Data solutions will grow 3x to
4.5x faster than On-Premise deployments
Source: IDC FutureScape: Worldwide Big Data and Analytics 2016 Predictions
Cloud
On-Premise
Market Opportunity
Big Data-On Prem vs. Cloud

21.8
Billion $
Big Data in the Public Cloud will grow
from 2.1 Billion $ Market in 2016 to
21.8 Billion $ market in 2026
Source: Big Data in the Public Cloud Forecast, 2016 -2026 Wikibon
Market Opportunity
Big Data on Public Cloud

100%Percentage of enterprises that will
adopt Hadoop in the next 24 months
Source: Forrester Wave: Big Data Hadoop Distributions, Q1 2016
Market Opportunity
Big Data Enterprise Adoption

86%Percentage of enterprises that use
Kafka as the message broker for their
Fast Data ecosystem
Source: OpsClarity Survey: 2016 State of Fast Data and Streaming Applications
Market Opportunity
Fast Data Adoptions

17%
Big Data Software CAGR for the
next 10 years
Market Opportunity
Source: 2017 - 2027 Wikibon Big Data Forecast

“Computing hardware used to be a capital asset, while data wasn’t
thought of as an asset in the same way. Now, hardware is becoming a
service people buy in real time, and the lasting asset is the data.”
– Erik Brynjolfsson, Director, MIT Initiative on the Digital Economy
http://www.oracle.com/us/technologies/big-data/rise-of-data-
capital-wp-2956272.pdf
“The real danger is that the data and analysis becomes worth more
more than the installed equipment itself.
– Karim R. Lakhani, Professor, Harvard Business school
Data Trend
Data is the
Single Biggest
Asset for Most
Companies

Now it applies more broadly to high
volume, velocity, and/or complex data with
scale-out commodity hardware and
software where the Compute processing
comes to the data
https://wikibon.com/2017-big-data-and-analytics-forecast-usage-scenarios/
Data Trend
Big data
started out as
defined by
Hadoop

Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Oracle Confidential
Data Trend: Modern Data Architecture Characteristics
Polyglot
Fit for Purpose Data
Lambda/Kappa
Speed Layer
Batch Layer
Data
Sources
Data
Services
Pipelines
Data
Services
Data PipelineData
Sources
12

2. Key Customer Challenges

Data
Capital
Digitization
× Datafication
Enterprise
Productivity
Disruptive
Technology
Doing Big Data Successfully is Hard
“Only 27% of respondents described their Big Data initiatives as ‘successful’
and only 8% of respondents described them as ‘very successful.’ In fact,
organizations were found to be struggling even with their Proof-of-
Concepts (PoCs), with an average success rate of only 38%.”
Capgemini Consulting, Cracking the Data Conundrum. 2015

Big Data Success is Hard
Top
Challenges
Data in Silos,
scattered across
the enterprise
Most data locked
up in petrified,
difficult to access
legacy systems
Lack of Big Data
and analytics skills
No convincing
business case for
moving further
Ineffective
alignment of Big
Data and analytics
teams across the
organization
Oracle Confidential – Internal/Restricted/Highly Restricted 15
Source: Capgemini Consulting, Cracking the Data Conundrum. 2015
Customer Challenges

Make Big Data Success Simple
Complete
Integrated
Platform +
Business
Focus
Unified Data
Lake
Smart Data
Movement
Managed
Service &
Analytical
Tools Agile & Elastic
Cloud
Environment
Unified
Platform for
All Users
Oracle’s Big Data Strategy is to help our customers build differentiating Data Capital by making Big Data
simpler via a complete platform that productively integrates the best of the new technologies alongside
the existing enterprise mission-critical applications and skill sets.

3. Customer Use Cases & Examples

• Native low latency, high performance native
connections to Oracle Event Hub Cloud Service
– Lower latency interactions between services
• Support for caching layer through Tachyon/Alluxio
• Native Integration with the following data stores
– Oracle Storage Cloud
– Oracle Database Cloud
– Oracle MySQL Cloud
High performance Streaming data analysis
• Fraud Detection
• Clickstream Analysis
• Real-time Analytics
Use Cases – BDCS-CE and EHCS

• Job scheduling & Job-specific clusters
• REST APIs to launch Jobs
• Client-side CLI
• Wide selection of Libraries
• Open Source Libraries [ Ex: SparkR, MLib etc. ]
• Oracle Provided Libraries [ Ex: Oracle R etc. ]
• Bring your own libraries
• Predicate, Projection Pushdown into
• Object Store
• Oracle RDBMS (in-cloud, on-premises)
Batch Jobs – Machine Learning, ETL, Cleansing
• Sentiment Analysis
• Machine Learning
• Customer Segmentation
• 360-degree customer view
Use Cases – BDCS-CE

• Zeppelin-based notebooks
• Ability to import/export notes
• Support for wide-variety of
interpreters
– Scala, Python, R , Spark SQL, Hive
Interactive Data Analysis
• Model Building
• Ad-Hoc Analysis
Use Cases – BDCS-CE

Energy Industry Leader
One of the world's largest providers of products
and services to the energy industry
Migrate
Entire Big Data on premises foot print to public cloud
Solution
Oracle Big Data Cloud–CE & Storage Cloud
Rapid provisioning & Lower TCO
• Significant cost savings in operating expenses
• Modernize Big data environment

Educational Institute
Leverage Big data technologies for research
Use Case
Generate experimental data & prepare for analysis
Visual analysis for novice users
Solution
Oracle Big Data Cloud-CE & Storage Cloud
Cost Savings & Ease of Use
• Start small, elastically Scale
• Use only when needed

ISV Solution
Billing platform for better customer service;
simplified management of subscription contracts.
Elasticity
Migrate & consolidate on prem platform to Big Data Cloud
Start Small, Grow elastically, use only when needed
Solution
Oracle Big Data Cloud-CE & Storage Cloud
Cost Savings
• Lowered subscription cost
• No admin cost (BDCS-CE managed service)

Big bank in EMEA
Banker and financial advisor to federal government
Data Lake
Modernize information strategy by maintaining
customer winnability
Solution
Lower TCO & Integrated
Platform
• Modernize Information strategy

Online Retailer
Outdoor sports equipment & apparel online retailer
Consolidate Data warehouse
Offload ETL using modern Big Data cloud platform
Use broader set of data management services
Marketing based big data initiative
Solution
Cost Savings
• Modernize Big data environment

Copyright © 2016 Oracle and/or its affiliates. All rights reserved. |
Customer Example: LinkedIn
26
• Needed to sync user profile data across
multiple physical locations in real-time as
users change their profiles
• Database to Database & Database to Kafka
• Over 30 transactional databases kept in sync
for Active-Active read optimized updates
leveraging GoldenGate – 4 way replication:
Virginia, Texas, Oregon, Singapore!
• Allows load balancing so that user application
data is always fresh and analytic data is up to
date with most recent user data
User Profile Data Distribution
Synchronize User Data Across Distributed Data Centers and Big Data

4. Product Demonstration

5. Cloud Platform for Big Data

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. 63
Oracle Cloud Platform
NetworkStorageCompute
Your Data Center
Oracle Cloud at Customer
Oracle Data Center
Oracle Public Cloud
IaaS
IDENTITY &
SECURITY
CONTENT &
EXPERIENCE
ENTERPRISE
INTEGRATION
DATA
INTEGRATION
BUSINESS
ANALYTICS
PaaS
Engage
Build
Integrate
Secure
Open
Hybrid Cloud
Comprehensive
Integrated
IT OPERATIONS
MANAGEMENT
APPLICATION
DEVELOPMENT
DATA
MANAGEMENT

Oracle Cloud Platform: Broad, Deep, Integrated
APPLICATION
DEVELOPMENT
IDENTITY &
SECURITY
CONTENT &
EXPERIENCE
ENTERPRISE
INTEGRATION
IT
MANAGEMENT
DATA
INTEGRATION
BUSINESS
ANALYTICS
Engage
Build
Integrate
Secure
DATA
MANAGEMENT
• Database
• NoSQL Database
• Big Data
• Big Data - Compute
• MySQL
• Database Backup
• Event Hub
• IT Analytics
• Log Analytics
• App Performance
Monitoring
• Infrastructure
Monitoring
• Orchestration
• Identity
• Security Monitoring and Analytics
• Compliance
• CASB
• Content and Experience
• WebCenter Portal
• Social
• Java
• Application Container
• Mobile
• Application Builder
• Developer
• Integration
• SOA
• Managed File Transfer
• Internet of Things
• Process
• API Platform
• GoldenGate
• Big Data Preparation
• Data Integrator
• Analytics Cloud
• Data Visualization
• Business Intelligence
• Essbase

Oracle Big Data Cloud Service – Compute Edition
The Gold Standard of Big Data Platforms in the Oracle Cloud – Managed by the Data Experts
Oracle Big Data Cloud Service – Compute Edition
enables you to rapidly, securely and cost-
effectively leverage the power of an elastic,
integrated Big Data infrastructure to unlock the
value in Big Data.

Big Data – Compute Edition
• Elastic –
– Scale from 1 Node to 100s of Nodes
– Scale Compute & Storage independently
• Managed
• Bring your data, bring your code
• leave the rest to us
• Highly Available
• storage cloud as data lake
• zero data loss and reduced downtime
• Measurable Differentiation
• Integrated with the Oracle Database, Data Analytics Stack and Oracle PaaS/SaaS offerings
• Single platform for analyzing Big Data and transactional data
Overview

Cluster in minutes
Spin-up a cluster or expand/shrink it in
minutes
In-Memory Optimizations
Utilization of Tachyon and other
optimizations to deliver better performance
for in-memory analytics workloads
Engineered for tiered-data storage
Optimized to effectively utilize all classes of
storage from memory to archival cloud
storage to deliver best price-performance
Smart Data Movement
Ability to filter data at source for RDBMS and
Object Store to deliver higher performance
through smaller data movement
Performant
Job-Pipeline Level Isolation
Each job-pipeline is completely isolated
from other jobs
Single-Sign On
Integrated with IDCS so users can login
through single sign-on
Secure
Storage Cloud Service
Leverage Storage cloud as your data
lake. Browse, Upload, Consume files
from the Storage Cloud using the inbuilt
browser.
Database Cloud Service
Automatic integration with Oracle and
MySQL cloud services through
association
Event Hub Cloud Service
Native access to ensure low latency,
high throughput between these
services.
Integrated
BDCS CE - Features
* 50 OCPUs include up to 750 GB RAM
* 100 OCPUs include up to 1500 GB RAM
Fully Managed
Use Hadoop without worrying about Cluster
set-up, configuration, management
Start small and scale
Start small, use additional capacity based on
workloads without having to scale-up/down
manually.
Independently Elastic
Independently scale compute tier or storage
tier.
REST APIs
REST based API access to all functionality
Zero downtime upgrades
Tenant controls to set limits on resource
consumption
Metering & Quota Management
Monitor usage and performance metrics
Simple

Oracle Event Hub Cloud Service
The Most Popular Platform for Fast Data on Oracle Cloud - Managed by the data experts
Oracle Event Hub Cloud Service enables you to
rapidly, securely and cost-effectively operate on
Streaming Data by leveraging the World’s Most
Popular Message Broker.

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | 69
Oracle Event Hub Cloud Service
Key Features
• Apache Kafka delivered as a managed service
• Available in dedicated and multi-tenant flavors
• Elastic – by the nodes (dedicated), by the partitions (multi-tenant)
• On Oracle Public Cloud or On-Premise through Cloud@Customer
Measurable Differentiation
• Lower latency and Higher throughput than competitors
• Open Standards based
• Available in the cloud or on-premise
Benefits
• Real-time Streaming data platform
• Easy to use with REST APIs
• High-performance Native API support
• Lift and shift Kafka workloads from on-premise
• Elastic – scale from thousand to millions of events per second
• Reliable – Highly available with in-cluster replication and cluster - mirroring

6. Summary

Developers
developer.oracle.com

Insights into Real World Data Management Challenges

Insights into Real World Data Management Challenges

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