Accelerating Data Science and Real Time Analytics at Scale

Accelerating Data Science
and
Real-Time Analytics
at Scale
Nadeem Asghar, Hortonworks, Field CTO and
Global Head Partner Engineering
Steve Roberts, IBM, Big Data Offering Manager

Data
Time
Available
Data
Understood
Data
Enterprise
Amnesia
80 million
wearable health
devices will
be available by
2017.
2.5
quintillion
bytes of data
generated daily
by connected
machines.
There
will be
28 times
more
sensor-
enabled
devices
than
people
by the
year 2020.
25 gigabytes
of data per hour
is generated by a
connected car.
90% of cars will
be connected by 2020.
153 exabytes
of healthcare
data generated by
devices in 2013.
Increasing to 2,314
exabytes in 2020.
1.7 megabytes
of data per
second
generated by
every human
being on the
planet by 2020.

Centralized
Mainframes
Cognitive Era
E-Business
Distributed
Computing
Smarter Planet
Office
Productivity
Client/
Server
Personal
Computer
Data
Warehousing
Big Data &
Predictive Analytics
Cognitive
A New Era of Computing Has Emerged
Data InsightContext
Transactional
Database
Business
Intelligence
Big Data &
Analytics
Actionable
Insight in context
Reporting
Cloud

© 2018 IBM Corporation
A recruiting and HR
company, chose an
IBM & Hortonworks
full stack solution to
support their
Hadoop/Spark
workloads and
accelerate their
analytics and AI
projects
Business problem
Job-matching is their core business and accuracy
and speed of this matching is critical to their
success. This requires the intake and analysis of
terabytes of data daily – including recruiter and
company information, job listings, hiring histories,
and resumes. Future requirement to apply AI to
more complex data such as images, sound and
video.
Benefits
• Proven performance
• World class support
• Reliable security for personal data
• Built on open technologies, avoiding vendor
lock-in
• Scalable software defined storage proven
for analytics
• POWER9 and PowerAI supports their AI
research and development
From Data to AIIntelligent Job Matching

accident
risk
rate
90%
inspection
times
10X
number of
inspections
AI at the Edge

6 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Ã #1 Pure Open Source Hadoop Distribution
Ã 1000+ customers and 2100+ ecosystem partners
Ã Employs the original architects, developers and
operators of Hadoop from Yahoo!
Ã Best-in-class 24x7 customer support
Ã Leading professional services and training
Ã Data Science Leader
Ã OpenPOWERperformance leadership
Ã Flexible, software defined storage
Ã #1 SQL Engine for complex, analytical workloads
Ã Leader in On-premise and Hybrid Cloud solutions
+
IBM + Hortonworks = Unlocking Actionable Insights

DATA – More Volume and More Types
I N C R EAS I N G D ATA V AR I ETY AN D C O MP L EX I TY
USER GENERATED CONTENT
MOBILE WEB
SMS/MMS
SENTIMENT
EXTERNAL
DEMOGRAPHICS
HD VIDEO
SPEECH TO TEXT
PRODUCT/
SERVICE LOGS
SOCIAL NETWORK
BUSINESS
DATA FEEDS
USER CLICK STREAM
WEB LOGS
OFFER HISTORY DYNAMIC PRICING
A/B TESTING
AFFILIATE
NETWORKS
SEARCH MARKETING
BEHAVIORAL TARGETING
DYNAMIC FUNNELSPAYMENT
RECORD
SUPPORT
CONTACTS
CUSTOMER
TOUCHESPURCHASE DETAIL
PURCHASE
RECORD
SEGMENTATIONOFFER DETAILS
P E T A B Y T E S
T E R A B Y T E S
G I G A B Y T E S
E X A B Y T E S
ERP
BIG DATA
W EB
CRM

Business Analytics Must Evolve To Deal With Data Tipping Point
PROVIDE INSIGHT INTO THE PAST
via data aggregation, data mining,
business reporting, OLAP,
visualization, dashboards, etc.
UNDERSTAND THE FUTURE
via statistical models, forecasting
techniques, machine learning, etc.
ADVISE ON POSSIBLE OUTCOMES
via rules, optimization and
simulation algorithms

Data Science and Real-Time Analytics at Scale
End to End Data Science Workflow
Data Engineering
DISCOVER
ACQUISITION
PROCESSING
CURATION
Data Science
DATA
WRANGLING
FEATURE
ENGINING,VISUALIZATI
ON AND ANALYSIS
MODEL BUILDING,
TRAINING AND
TESTING
Deployment & Operationalize
REPORTS
DASHBOARDS
REAL-TIME
SCORING
BATCH
SCORING
REST SERVICES
PERFORMANCE
MGMT
SCHEDULING
Data Science Experience (DSX)
Enterprise Services: Multi Notebook Support, Versioning, Collaboration, Model Management
Hortonworks Data Platform (HDP)
Enterprise Services: Data, GPU, Deep Learning, Compute, Security, Governance, Metadata, Operations
Hortonworks Data Flow (HDF)
Enterprise Services: Data Ingestion Schema Registry, CEP
Hortonworks Data Flow (HDF)
Enterprise Services: Data Ingestion Schema Registry, CEP

Use Case Deep Dive
Credit Card Fraud Prevention

Building a Model
Ã Show of hands, how many have built a “Model”?
Ã What are some limitations?
– Conditional based logic: if/else binary decisions
Ã If you need a lot of data to build a good model, what tools can you use?
– Data volumes can eliminate the possibility of desktop tools
Ã Sampling?
– Well… we better get an even distribution of true and false positives in each sample, but wait that
requires data munging, back to what tools can we use.
Ã Security Concerns?
– Extracting data from it’s secure resting place and pushing it into other environments, often times
unsecure files or desktops where Matlab or R can be installed.
Ã Collaboration
– Push processing to the data using modern distributed tooling.

Credit Card Fraud Use Case
Ã Requirement: Detect fraudulent transactions.
Ã Goal: Save the card company money and build trust amongst card users. Cut down on
fraudulent crime
Ã Functional Requirement: Detect fraud in under 2 seconds at point of sale. Learn, adapt
and make smarter decisions over time.
Ã Design
– Distance: How far can one travel over a period of time before it is fraudulent?
– Category: How can we detect a purchase that a customer wouldn’t likely make?
– Frequency: How can we detect purchasing patterns that do not resemble the card holder?
Ã Ideas?
– White board some conditional logic, egregiousness vs binary
– Back test the data
– Build a model per card holder?

Rules, Statistics, Machine Learning
Ã Rule Based Logic
– Great for checking conditions that can prove to be 100% accurate. Easy to build and no reason to
over engineer.
– Example: Spending Limit. Card holder limit = $2,000
• If (currentPurchaseAmount+ balance > 2,000) then deny transaction
Ã Statistics
– Mean, median, mode, variance, deviation
– Anomaly detection. Outliers. (i.e. womens retail example)
Ã Machine Learning
– Supervised
– Unsupervised
– Trainable
– Adapt over time

Discovery
Ã Gathered all Credit Card Transactions
– Problem is they didn’t make sense
– No identifiable patterns, no log normal curves
– Gas $45, Chipotle $8.50, Steak dinner $88, Amazon shoes $55
Ã Classification

Outlier Detection: identify abnormal patterns
Example: identify anomalies
Features:
- Time frequency
- Category
- Amount
- Distance

Fraud Detection Demo Technical Architecture
Real-Time Data
Movement
(Apache Nifi)
Real Time Processing
(Storm)
Inbound Messaging
(Kafka)
D A T A I N
M O T I O N
D A T A I N
M O T I O N
Distributed Storage: HDFS
Many Workloads: YARN
Real-time Serving (HBase)
Spark
(Machine Learning)
UI and HTTP PubSub
(Jetty and Tomcat)
Data Science
(DSX)
Resource Allocation
(Docker)
Interactive Query
(Hive)
Authorization
(Ranger)
Governance
(Atlas)
All Running on Top of IBM Power Hardware

Use Case Demo
Credit Card Fraud Prevention

Page 18
Credit Fraud Analyst Inbox

Page 19
Credit Fraud Analyst Investigation

Page 20
Credit Fraud Analyst Action

Page 21
Hortonworks Data Flow- Backbone for Bi-Directional Communication

Demo Summary
Problems Solved
• Data Scientist teams can collaborate and learn new tools on a common frameworks.
• Choice of open source tools, notebooks, and languages.
• Run favorite notebook on all data in their HDP cluster.
• Deploy the model to production.
• Leverage the production model to deliver insights to business.
• Monitor the health and performance of models in production.

Page 23
Improved
Experience
/Reduced Cost
Immediate
Customer
Feedback
Years of
Customer
Transaction Data
Fraud Detection
Complete
Customer
Profile
Real time
ingest of
transactions
Proactively identify potential
fraudulent transactions to
protect the customer and
improve customer experience
• Proactively monitor every credit
card transaction using machine
learning to catch potential fraud
• Customer Service Analyst reviews
flagged transactions in real time via
a next generation application
running on the connected platform
• HDF controls real time flow of data
in and out of the connected
platform to the various source and
destination points
Innovate
Renovate
Purchase
Behavior
Insight
Journey to Fraud Detection

Data Science Solution
Community Open Source Scale & Enterprise Security
• Find tutorials and datasets
• Connect with Data Scientists
• Ask questions
• Read articles and papers
• Fork and share projects
• Code in Scala/Python/R/SQL
• Zeppelin & Jupyter Notebooks
• RStudio IDE and Shiny
• Apache Spark
• Your favorite libraries
• Data Science at Scale
• Run Spark Jobs on HDP Cluster
• Secure Hadoop Support
• Ranger Atlas Support for Data
• Support for ABAC
Model Management
• Data Shaping Pipeline UI
• Auto-data preparation & modeling
• Advanced Visualizations
• Model management & deployment
• Documented Model APIs
Data Science Experience
Freedom:
Choose the right tool for
your team and business.
Productivity:
Make both experienced and
novice data scientists more
productive.
Trust:
Confidently deploy insights
generated from the most
current data and trends.

enterprise-ready
software distribution
built on open source
tools for ease
of development
performance
faster training times
for data scientists
+

IBM Power Systems
designed to deliver
breakthrough performance
for data
threads per core
processor cache
memory bandwidth
open innovation
+++
MOREvs.
x86
+ BETTER
L1 ßà L4
COMMUNITY
availability | scalability | reliability | serviceability
get more work done
fastest memory lives on cores
more data than ever is flowing
faster innovation and value
MEANS
26

Accelerate Data Science with Power Systems
Test results based on running a machine learning workload based on k-means clustering algorithm on data sets size ranging from 1GB to 15 GB. Test System details – Power Systems
S822 LC HPC – 20 Cores, 512 GB RAM and SSD, Power Systems S822LC Big Data – 20 Cores, 512 GB, HDDs, Intel Server with Broadwell E5 2640 v4 – 20 cores, 512 GB and SSD,
Intel Server with Broadwell E5 2699 v4 – 44 cores, 512 GB, HDD
• Increase Data Science Team productivity
• Reduce model training time
− 2.5X with S822LC for HPC vs E5-2640 v4
(with SSD)
− 1.5X with S822LC for Big Data vs E5-2699 v4
(with HDD)
• Leverage larger datasets for model
training
• 2.5X larger dataset in the same time (1200 Seconds -
~5GB for x86 server E5 2640 with SSD vs 13GB for
Power server S822 LC HPC with SSD)
0
600
1200
1800
2400
3000
3600
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Data Size (GB)
Elapsed time to form 5 clusters in 100 Iterations using
k-means clustering with one user
S822LC HPC with SSD S822LC BigData with HDD
E5 2699 v4 with HDD E5 2640 v4 with SSD
ElapsedTime(seconds)

The Perfect Blend of Data Science and an Enterprise Data Lake
28
Better
Together
datascience.ibm.com
Boost Data Science Team
Productivity: model training
in less than half the time
versus x86
Blazing Fast Insights for Line
of Business: A 1.7x
improvement in time to result
Secure and Reliable Data Access at Scale: Open, comprehensive data
lifecycle and security management on the most reliable servers.
For clients building a high
performing Data Science
practice with a fast, scalable,
enterprise Data Lake
Acomplete solution of Data Science
and Hadoop software, hardware and
quick start services.

29 © 2016 IBM Corporation
Image Name Software Versions Linux Version
HDP 2.6.2 HDP 2.6.2 RHEL 7.3
HDP 2.6.4 HDP 2.6.4 RHEL 7.4
HDP/HDF Security Governance Demo HDP 2.6.3, HDF 3.0.3 RHEL 7.4
HDP/HDF Credit Card Fraud Detection Demo HDP 2.6.3, HDF 3.0.3 RHEL 7.4
HDP/HDF IOT Trucking Demo HDP 2.6.3, HDF 3.0.3 RHEL 7.4
Hortonworks Preconfigured Images available on IBM POWER8
Size Flavor Options Description
Small 8 vCPUs, 24GB memory, 50GB disk
Medium 16 vCPUs, 32GB memory, 200GB disk
Large 24 vCPUs, 48GB memory, 500GB disk
1. Go to IBM Power DevelopmentCloud (PDC):Link
2. Follow the Get Started process via the “Go to Program to Get Started” link and register for IBM PDC as a Partner or Open
Source Developer
3. When you reach the IBM PDC “Make a Reservation” page,click Requestpromo code
4. SelectRed Hat Linux for the Image Category.Enter the vCPUs and memory using values from the size/flavor options in the
table below.In Other requirements field,enter one of the Image names from the table below.Click Submit.
5. Wait for an approval email.Then,follow the instructions in the Create Reservation guide to complete your reservation.
6. On the reservations page,select the company profile that shows VMaaS, enter the Promo code received in the email,and
click Apply.
7. In the next form, select the desired Flavor and Image name.

How to Get Started with Hortonworks on OpenPOWER Systems
• Learn more about the benefits of IBM Power Systems and OpenPOWER
• Join the Hortonworks Community: https://community.hortonworks.com/
• Learn more about the benefits of Hortonworks: http://hortonworks.com/training/
• Sign up for Free Data Science and Cognitive Computing courses:
https://cognitiveclass.ai/
• Try the solution: IBM benchmark centers, on the cloud or on your premise

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