The document outlines the evolution of modern data platforms, highlighting the transition to open-source databases and the importance of data analytics in driving personalized digital experiences. It discusses the rise of NoSQL and other flexible data models that cater to varying data types and the significance of machine learning in extracting insights from large datasets. Additionally, it emphasizes the strategic value of data for businesses and explores various industry-specific use cases across sectors like finance, retail, and healthcare.

1. © 2017 IBM Corporation
A Modern Data Platform to meet all of
your data type needs
@mRcSqZd
Technical Leader
IBM Cognitive Systems
#ThinkCIO

2. Your customer expect innovative and
personalized digital experiences….driven by data
Personalized
and Engaging
Location Aware On Demand

3. By 2018, more than
of new in-
house apps
will be developed on
an Open Source database,
of existing apps
will be converted.
70%
50%
“The State of Open-Source RDBMSs”

4. Superior digital experiences
must be built, they can’t be bought

5. 75%
of application development supporting digital
business will be built not bought by 2020
Source: Gartner

6. Seizing Opportunity…
Sources: E&Y and Forrester
12%Amount of data most companies
estimate they are analyzing
80% Time analysts will spend trying to
create data sets to draw insights
83%Of companies agree that data
is their most strategic asset
Crossing The Data Chasm

7. DB-Engines Ranking
https://db-engines.com/en/ranking_trend

8. The Data Analysis Journey
Data
Foundation
New Data
Source
Data
Lake
Data
Science
Differentiation
Business Value
New Business
Models
BigData &
Analytics
NextGen
Data Sources
Traditional

9. Data Flood
• INCREASING DATA VARIETY
Search
Marketing
Behavioral
Targeting
Dynamic
Funnels
User
Generated
Content
Mobile Web
SMS/MMS
Sentiment
HD Video
Speech To
Text
Product/
Service Logs
Social
Network
Business
Data Feeds
User Click
Stream
Sensors Infotainment
Systems
Wearable
Devices
Cyber
Security
Logs
Connected
Vehicles
Machine
Data
IoT Data
Dynamic
Pricing
Payment
Record
Purchase
Detail
Purchase
Record
Support
Contacts
Segmentation
Offer
Details
Web
Logs
Offer
History
A/B
Testing
BUSINESS
PROCESS
PETABYTESTERABYTESGIGABYTESEXABYTESZETTABYTES
Streaming
Video
Natural
Language
Processing
WEB
DIGITAL
Intelligence
Data NeedFrom to
Source: NVIDIA

10. New data sources Social LocationSensors
Data
Services
Transactional
b
SQL
PostgreSQL
compatible
SQL
MySQL
compatible
New data models - NoSQL
Document, Column, Key Value, Graph
Conventional data platform
Mobile
“New” relational databases
Open Source SQL
New data models
Hadoop/Spark
Unstructured Data
A Modern Data Platform is born

11. 11
Leading Open Source Technologies
Available and Optimized for Linux on Power
***Paquetes y Software disponible para Linux en Power: https://developer.ibm.com/linuxonpower/open-source-pkgs/

12. New Database Paradigms
Distributed
Hierarchical
Databases
A scalable data
architecture
A parallel and distributed
programming model
Open source community
innovation (Apache
Hadoop)
Relational
Databases
NoSQL
Databases
Analytics capability for
multiple
data types, often used in
mobile and social
workloads
Scalability and flexibility
for different data store
models
ACID compliance for
Transactions and
Reporting
Instant insight from
real-time operational
data
Tuning and indexing for
performance
In Memory Databases
Increased performance by bringing data closer to compute
ACID = Atomicity, Consistency, Isolation,
Durability
BASE = Basically Available, Soft State,
Eventually Consistent

13. IBM Internal and Business Partners Only
What is NoSQL ?
• Stands for “Not Only SQL” *
• Introduced in 2009 by Eric Evans of Rackspace, a committer on the Cassandra project
• Often used in the sense of “Not only SQL” to describe the surge of new projects and products.
• Use non-relational data stores, does not require fixed table schema (rows/columns)
• More flexible data schemas, variety of data store models, run well on distributed networks
• Key value, document store, wide column, graph, search ……
• Popular with developers building mobile/cloud scale apps with frequently changing data
• NoSQL data stores relax one or more of the ACID properties used by Relational data
•Atomicity, Consistency, Isolation, Durability
• NoSQL shifts data store properties to BASE to support exponential growth of data
•Basically Available, Soft State, Eventually Consistent
* Source: A Brief History of NoSQL - http://blog.knuthaugen.no/2010/03/a-brief-history-of-nosql.html
13

14. NoSQL Open Source DB Types
Complexity
Size
Key Value
Stores
Wide Column
Document
Stores Graph
Scales Easily Hard to Scale
Basic Function Rich Function
14

15. Apache Spark
• An Apache Foundation open source project. Not
a product.
• An in-memory compute engine that works with
data. Not a data store.
• Enables highly iterative analysis on large volumes
of data at scale
• Unified environment for data scientists,
developers and data engineers
• Radically simplifies process of developing
intelligent apps fueled by data.
from
http://spark.apache.org

16. Spark on POWER in the Enterprise: Workload
characteristics
Machine Learning
SNAP ML
Graph Analytics
Security, Fraud Detection
Social Network Analytics
Video/Speech Analytics
Object Recognition
Dialog
Transaction processing
Websphere
Future Workloads add a new dimension:
Understanding the WorldTraditional Workloads:
Automating the World

17. Spark on Power – Many Options
• Hortonworks Data Platform
• Spark comes with managed version of Hadoop
• Integrates seamlessly with YARN and HDFS
• Spark is part of larger ecosystem of Hadoop
components
• Platform Conductor
• Purpose built Spark platform
• Optimized for running spark based workloads
• Data Science Experience Local
• Data science collaboration environment
• Brings together Spark / Python / Rstudio / Notebooks /
in single platform
• Open source spark
• Build and manage your own platform

18. The Modern Data Platform on IBM POWER Systems
Data Lakes
Data Management, Streaming
New Apps on Open Source DBs
Advanced Analytics & Insights Cognitive
Workloads
PERSIST ANALYZE & ACT
Data
Sources
INGEST
Conventional
Data Sources
Third-Party
Data
Transactional
Data
Application
Data
New
Data Sources
Machine &
Sensor Data
Image & Video
Mobile,
Location
Social Data
Internet of
Things Data
Relational /
Transactional
Databases
Data
Warehouses
NoSQL DBs
Data in
Motion apps
Sense and
Respond
Discovery and
Exploration
Machine
Learning /
Deep Learning
Predictive
Artificial
Intelligence
Open Source SQL
Hadoop
Spark New Ind. Apps:
360 client view
Fraud Detect.
Recommend.
Conventional
Data Platform
THINK &
AUTOMATE

19. Data Engineering - Basic Workflow summary
PERSIST
ANALYZE & ACT
Relational /
Transactional
Databases
Data
Warehouses
NoSQL DBs
Open Source SQL
Streams / IOT Conventional
Data Platform
Speed and Accuracy are critical desired outcomes for AI projects

20. Unstructured, Landing, Exploration and Archive
Operational Data
Real-time Data Processing & Analytics
Transaction and
application data
Machine,
sensor data
Enterprise
content
Image, geospatial,
video
Social data
Third-party data
Information Integration & Governance
Data is Prerequisite to AI
Deep
Analytics data
zone
EDW and
data mart zone
Risk, Fraud
Chat bots,
personal
assistants
Supply Chain
Optimization
Dynamic
Pricing,
Recommenders
Behavior
Modeling
Vision,
Autonomous
Systems
Speed and Accuracy are critical desired outcomes for AI projects

21. MongoDB,
Postgres
MongoDB
Postgres
MongoDB,
Redis
MongoDB
MariaDB
Community
EnterpriseDB
Postgres, HWX
Cassandra
MongoDB, EDB,
Neo4j
Postgres
Ambientia
MariaDB
EnterpriseDB
Splendid Data
(Postgres)
Redis
Postgres
PowerAI / SunDB c
PowerAI / MongoDB
MongoDB, MariaDB
MariaDB,
Redis
Postgres, Redis
MongoDB
Internal & BP use only
Hortonworks
Hortonworks
Hortonworks
Hortonworks
Hortonworks
Hortonworks
MongoDB, EDB,
PowerAI
MongoDB
Hortonworks
Egyptian Ministry of
Interior
Hortonworks
Hortonworks
Clients with Modern Databases on Power

22. “AI LADDER”
Fixed
Deployment
Private
Cloud
Machine
Learning
Analytics
Data
AI
© Hortonworks, Inc. 2011-2018. All rights reserved. | Hortonworks confidential and proprietary information.

23. Machine
Learning
Analytics
Data
AI
© Hortonworks, Inc. 2011-2018. All rights reserved. | Hortonworks confidential and proprietary information.
EDW Optimization – Enterprise Data Lake
• Offload data and ETL to Hadoop
Hortonworks Data Platform
IBM Elastic Storage Server
EDW Modernization – New Data + Analytics
• Capture and analyze new streaming data
• Federate across EDW and Hadoop
Hortonworks DataFlow
IBM BigSQL
SAP Data Hub and Vora
Data Science – Machine Learning
• Build accurate predictive models
• Leverage Data Lake
Data Science Experience
Data Science – Deep Learning
• Use larger data sets and neural networks to
unleash deep insight
PowerAI
Spectrum Conductor with Deep
Learning Impact

24. Say “Hello”
to POWER9
1.8x
more memory
bandwidth
vs x86
2x
faster core
performance
vs. x86
2.6x
more RAM
supported
vs x86
9.5x
max I/O bandwidth
vs. x86

25. A Modern Data Platform to meet all of
your data type needs
@mRcSqZd
Technical Leader
IBM Cognitive Systems
#ThinkCIO

26. Big Data creating new Cognitive opportunities and
use-cases
Industry Data Source Cognitive Use-Case
Manufacturing / Factory /
Assembly
Audio, Video, Images Analysis of equipment sounds to anticipate machine failures; Manufacturing and Robotics.
Human Resources Text Data extraction and sentiment analysis for automated best-practice HR advice
All / Report Generation Sales data, etc.. Generative models for natural language detailed and summary reports automatically
generated.
Retail / Commerce Transactional Data Find correlations between customer demographics and products; Correlations between
products and other products; etc.
Hyperscale Datacenter Sensor / Logs Reinforcement learning to optimize best possible cooling and operating configurations for
energy efficiency.
Medical Images Automatic detection of diabetic retinopathy.
Retail / Commerce Behavior history Recommendations based on behavior analysis, patterns; Cross-sell and up-sell;
Telco CDRs/switches, subscribers, billing
systems, etc.
Customer retention, targeted promotions, fraud detection, service optimization etc..
Banking Emails, customer account
histories/behaviors, sentiment, securities
data, loan records, etc…
Customer insights to cross sell products, target offers; Call center
automation/optimization; Financial fraud and risk management.
Mobile Banking (w/
Telco)
Airtime usage, contacts, transactions,
mobile wallet, personal info
Customer insights to target offers; New forms of credit risk / loan approval / credit
worthiness.
Financial Financial market data, Quandl public DB,
NY Times database
Leverage market data and sentiment from earlier time zone for actionable insights in
current market (“follow the sun”).

27. Finance Industry specific Use Cases
Industry Data Source Use-Case
Financial Customer account history data, emails, call center logs, web
interaction,
Customer behavioral analysis, attrition prediction. (Spectrum Conductor,
PowerAI)
Financial Customer data, tranactions Fraud detections based on pattern / behavior analysis. ; Trend analysis.
Financial Credit card transactional data (OracleDB/AIX), Teradata
warehouse, Object store, Account history data.
Credit risk and fraud analysis
(OracleDB/AIX, Teradata, Puresystem, Spark, Swift/Cleversafe, PowerAI)
Financial Mortgage processing data, Core Logic, Analyze and then predict mortgage defaults and/or pre-payments; Analyze
macro effect on housing market; Rental rate estimation, credit analytics,
etc..
Financial / Insurance Image Automated claims processing (image classification of pictures of damage)
Financial KDB Timeseries Database (high frequency trading) Pattern analysis, clustering, decision trees. (Anaconda/Python, XGBoost,
K-Means, Tensorflow, Jupyter)
Financial RDBMS, Timeseries behavioral (premium payments, call center
interactions, emails, trades, calls, portfolio performance); Static data
(demographic, mortality, policies, portfolio strategy,
Customer attrition prediction and Product recommendation (up-sell / cross-
sell).
Financial Social media Natural language processing interpreting customer sentiment. (Tensorflow)

28. Sample Industry Use Cases
Industry Data Source Use-Case
Retail Images of products Computer vision based check-out. (PowerAI, FPGA based
Inferencing acceleration)
Supply Chain IoT, RFID, sensors, traffic data, weather Dynamic Route Optimization
Government Video Traffic management with real-time vehicle classification (VisionBrain,
Caffe, Tensorflow)
Government Large scale HPC operational data Optimize large scale HPC workload plan, parameters, experiments and
execution.
Government Drone, Satellite and other Images; Text Image analysis for real time threat protection; Anomaly detection,
patterns, sentiment, measures of opinion/action changing; Facial
recognition; Language translation, augment human translators.
Security Aggregation of community cameras and wecams,
images/video.
Aggregated community surveillance, learning normal community
behaviors (traffic, vehicles, pedestrians, etc.) and flagging/alerting
anomalies.
Telecom/Media Language, text, video, images. Improve accessibility, accuracy and speed of captioning; Meta-data
for content libraries and automated media tagging. Drone/aerial data
for preventative maintenance of equipment.
Media and
Entertainment
Augmented reality, Intelligent game agents, precision
recommendations
Auto / Aero Images, sensor data, Voice Autonomous and assisted driving; Accident avoidance; Voice
controlled controls/actions.

29. Industry /
Function
Data Source Use-Case
Energy / Utilities /
Oil and Gas
Facility/Equipment sensors; Seismic data and
images.
Identify failing or failed components; Anomaly detection and
customer pattern predictions; Re-interpret existing seismic
topology.
Energy / Utilities Drone/aerial images and video; Replace hazardous manual electrical tower inspections with
remote-controlled drones, leveraging image/video
classification to identify maintenance issues. (E870 in-
memory RDBMS, ESS)
Healthcare Biopsy images. Deep learning based early cancer detection.
Healthcare /
Lifescience
MongoDB, Neo4j, Enterprise DB Genomics sequencing with applications like BWA and
GATK, with additional workflows leveraging ML/DL
(Spectrum Conductor for Spark, Spectrum Scale, H2O,
Tensorflow )
Healthcare /
LifeSciences
Medical images, CAT scan, X-ray, MRI Improved diagnostic capability, anomaly detection;
Combine image recognition with genomic data; Drug
discovery – improved molecular analysis
Sample Industry Use Cases