A6 big data_in_the_cloud

1. Big Data In the Cloud
Simon Tang
Master Principle Sales Consultant
BI and Big Data Team, HK
October, 2016

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Innovation

4. &
4Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Innovation
Execution

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Transforming to Big Data
From Data Warehouse
• Relational
– On-Premises
• Transactional data
• Analytics + Data Mining
To Big Data
• Relational + Hadoop and NoSQL
– On-Premises + Cloud
• Transactional + Social, Web and IoT
• Analytics + Data Mining + Machine
Learning
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Transactional
Data Warehouse
SQL
Social, Web
Data Lake
IoT
Fast Data
node.jsJavaREST Python ScalaR
To Big Data
Consolidate Metadata
across Silos
Unique Architecture to
deliver Performance:
Optimize queries
Pushing down processing
Extend Security model
From Data Warehouse
Applications
Simplicity
Specialization
Performance
Analytics
Complexity
Fragmentation
Delays
Oracle Big Data SQL

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Existing
People
Existing
Applications
More Data

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Time to root cause identification (Reduction in) Time to insight
Confidential – Oracle Internal/Restricted/Highly Restricted 8
The Value of Minimizing Data Movement
86%
7 days
3 weeks
4 hours

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Graph
• Massively-Scalable Graph Database
• 40+ in-memory parallel algorithms
• Simple standard interfaces: R, Python...
9
Comprehensive Data Science Capabilities
Any analysis across relational, Hadoop, Spark and NoSQL

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Graph - Sample Use Cases
Traffic Pattern – Shortest path
Multi-Modal Routing
Find closest store within a
specified drive time

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Who is most important? There Are Lots of Answers.
• Answers from Aggregation
– Who spends the most?
– Who buys the highest margin goods?
– Who is most consistently a top contributor?
• Answers from Connectivity
– Who’s most influential?
– Which supplier do I depend on the most?
– What is the right product mix for millennials?
Tabular questions:
Well-suited to SQL-like tools
Graph questions:
We need something different!

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What Big Data problems can Graphs address?
Purchase Record
customer items
Product Recommendation Influencer Identification
Communication
Stream (e.g. tweets)
Graph Pattern MatchingCommunity Detection
Recommend the most
similar item purchased by
similar people
Find out people that are
central in the given
network – e.g. influencer
marketing
Identify group of people
that are close to each other
– e.g. target group
marketing
Find out all the sets of
entities that match to the
given pattern – e.g. fraud
detection
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Faster Time to Results: Graph
• Parallel and Distributed Graph
Processing
• Optimized for Infiniband network
• Ultra balanced workload
partitioning using state-of-the-art,
proprietary techniques
1
10
100
1000
2 4 8 16 32
ExecutionTime(seconds)
logscale
Cluster Size
Oracle PGX outperforms
GraphX up to160X
Weakly Connected Components (WCC)
1.4 Billion edge graph

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Graph
• Massively-Scalable Graph Database
• 40+ in-memory parallel algorithms
• Simple standard interfaces: R, Python...
Machine Learning
• Scale to big data using R and SQL
• 40+ parallel, distributed algorithms
• Automated data preparation
and modeling
• Enhances and extends SparkML
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Comprehensive Data Science Capabilities
Any analysis across relational, Hadoop, Spark and NoSQL

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Machine Learning -
Better Information, Valuable Insights and Predictions
Customer Months
Cell Phone Churners vs. Loyal Customers
Insight & Prediction
Segment #1
IF CUST_MO > 14 AND INCOME <
$90K, THEN Prediction = Cell Phone
Churner
Confidence = 100%
Support = 8/39
Segment #3
IF CUST_MO > 7 AND INCOME <
$175K, THEN
Prediction = Cell Phone Churner,
Confidence = 83%
Support = 6/39
Source: Inspired from Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management by Michael J. A. Berry, Gordon S. Linoff
R

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Be Specific in Problem Statement
Poorly Defined Better
Predict employees that leave •Based on past employees that voluntarily left:
• Create New Attribute EmplTurnover  O/1
Predict customers that churn •Based on past customers that have churned:
• Create New Attribute Churn  YES/NO
Target “best” customers •Recency, Frequency Monetary (RFM) Analysis
•Specific Dollar Amount over Time Window:
• Who has spent $500+ in most recent 18 months
How can I make more $$? •What helps me sell soft drinks & coffee?
Which customers are likely to buy? •How much is each customer likely to spend?
Who are my “best customers”? •What descriptive “rules” describe “best
customers”?
How can I combat fraud? •Which transactions are the most anomalous?
• Then roll-up to physician, claimant, employee, etc.

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Faster Time to Results: Machine Learning
Oracle Big Data Cloud Service
outperforms Spark MLlib by 10X
264
sec
24
sec
Spark Oracle
• Re-designed, optimized algorithms
• Better parallelization, distribution
• Efficient memory utilization
• Superior solver technology
Linear Model algorithm over airline data
Predict 8,000+ coefficients over 120M records

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Machine Learning: Not Just For Data Scientists

19. EXPERIMENT
ANALYZE
& ACT
2
Manage, secure and
make all data available
Connect people
to information they need
Innovation through data
experiments and advanced analytics
Transform workplace
and workforce through insights
COLLECT MANAGE
Oracle’s Unified Big Data Management and Analytics
Strategy

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22. DataFlow ML CS
Big Data Preparation CS
IoT CS
Big Data CS
NoSQL Database CS
Data Visualization CSBig Data Discovery CS
Stream Analytics CS*
R on Hadoop*
Spatial and Graph*
2
EXPERIMENT
ANALYZE
& ACT
COLLECT MANAGE
DataFlow ML CS
Big Data Preparation CS
IoT CS
GoldenGate CS
Event Hub CS
Data Integrator CS
Big Data CS
NoSQL Database CS
Big Data CS CE
Big Data SQL CS
NoSQL Database CS
Exadata CS
Data Visualization CS
Business Intelligence CS
Spatial and Graph*
Advanced Analytics*
R on Hadoop*
Big Data Discovery CS
Stream Analytics CS*
R on Hadoop*
Spatial and Graph*
* Bundled with other Cloud Services
Oracle Cloud Platform for Big data

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New At Oracle OpenWorld
Oracle Big Data
Cloud Service –
Compute Edition
Oracle Dataflow
ML Cloud Service
Oracle NoSQL
Database Cloud
Service
Oracle Event Hub
Cloud Service

24. Big Data CS
• Spark and all Hadoop services
• Bursting new
• Dedicated compute and storage
• Automated, Flexible
• Includes: R, multimedia, Spatial,
Graph analytics; data integration
tools, Cloudera Enterprise Data Hub
• Big Data SQL Cloud Service coming
Big Data CS – Compute Edition
• Spark and HDFS services
• Fully Elastic
• Shared compute and storage
• Fully Managed
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Choice of Big Data Cloud Services Announcing

25. Customer Data Center
Purchased
Customer Managed
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Choice of Big Data Deployment Models
Big Data Appliance
Customer Data Center
Subscription
Oracle Managed
Big Data Cloud Machine
Oracle Cloud
Subscription
Oracle Managed
Big Data Cloud Service

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