These slides - based on the webinar - shed light on how business stakeholders make the most of information from their big data environments and the requirements those stakeholders have to turn big data into business impact. Using recent big data end-user research from leading IT analyst firm Enterprise Management (EMA), data from Vertica’s recent benchmarks on SQL on Hadoop, and firsthand customer experiences, viewers will learn: - Use cases where end users around the world are using big data in their organizations - How maturity with big data strategies impact why and how business stakeholders use information from their big data environments - How Vertica empowers the use of information from big data environments

1. John L Myers
Managing Research Director
Enterprise Management Associates
JMyers@EnterpriseManagement.com
@johnlmyers44
How Business uses Big Data to impact
the Bottom Line
Steve Sarsfield
Product Marketing
HPE Vertica
Steve.Sarsfield@hpe.com
@stevesarsfield

2. Featured Speakers
Steve Sarsfield, Product Marketing Leader, HPE Vertica
Based in Cambridge, MA, Steve is an evangelist and spokesperson
for Hewlett Packard Enterprise. He is a frequent speaker and
blogger on the subjects of big analytics, information quality and data
governance. He is also author of the book "The Data Governance
Imperative," published by IT Governance Publishing.
John Myers, Managing Research Director, EMA
John has over 10 years of experience working in areas related to
business analytics in professional services consulting and product
development roles. Additionally, John helps organizations solve their
business analytics problems, whether they relate to operational
platforms – such as customer care or billing – or applied analytical
applications – such as revenue assurance or fraud management.
Slide 2 © 2017 Enterprise Management Associates, Inc.

3. Agenda
• Big data and data lake adoption and implementation
• Use cases where end-users around the world are using big data in
their organizations
• How maturity with big data strategies impact why and how business
stakeholders use information from their big data environments
• How HPE empowers the use of information from big data
environments
• Question and Answer
Slide 3 © 2017 Enterprise Management Associates, Inc.

4. Data Lakes

5. Data Lake Adoption Increasing
Slide 5 © 2017 Enterprise Management Associates, Inc.
52.1%
67.1%
vs
Adopting Data Lake Strategies
in 2014/2015
Adopting Data Lake Strategies
in 2016

6. Data Lake Technical Drivers
Slide 6 © 2017 Enterprise Management Associates, Inc.
Multi-structured Data Management
Capabilities
Multiple Data Processing
Engines
Lack of Scale with Existing Platforms Economics

7. EMA Hybrid Data Ecosystem
Slide 7 © 2017 Enterprise Management Associates, Inc.

8. Example: Common Architecture
Slide 8 © 2017 Enterprise Management Associates, Inc.
Ingest &
Transform
Analyze Visualize
SQL
Python
R
NoSQL
Spark
ML
Java
Big Data
Warehouse
Data Lake
Structure
& Connect
Enlighten
Real-time
Dashboards
Business
Intelligence
Predictive
Analytics
Descriptive
Analytics
Ad-hoc
Queries
Connectors
PMMLs

9. Use Cases

10. Use Cases Associated with Big Data
Slide 10 © 2017 Enterprise Management Associates, Inc.

11. Big Data Projects:
Top Business Goals
Slide 11 © 2017 Enterprise Management Associates, Inc.
Market basket Analysis for
Cross-sell/Up-Sell
Social Brand Analysis for
Customer Engagement
Risk Analysis for
Fraud and Cost Reduction

12. Needing different kinds of analysis is common
Slide 12 © 2017 Enterprise Management Associates, Inc.
Security Analytics
• Are there any attacks happening
right now?
Weather Application
• Tell me the current
temperature and pressure Short, fast queries
Deeper analytics with
bigger data sets
Machine learning and
predictive
– What was the high/low for
my zip code?
– What was the high/low for
my state?
– What was the average
temperature?
– Highest and lowest of all
time?
– Can we predict conditions
tomorrow?
– What IP and where are most of
my events coming from?
– Has traffic spiked compared to
historical?
– Has any event happened liked
this over the last three years
– What new events should we be
tracking to predict security
events?

13. Big Data Maturity

14. Functional Components of a Data Lake
Slide 14 © 2017 Enterprise Management Associates, Inc.

15. Functional Components by Maturity Score
Slide 15 © 2017 Enterprise Management Associates, Inc.

16. Consider depth of analytical capabilities and concurrency
Slide 16 © 2017 Enterprise Management Associates, Inc.
99
99
80
59
29
19
40
70
VERTICA ENTERPRISE
VERTICA SQL ON
HADOOP
IMPALA
HIVE ON TEZ
HIVE ON SPARK
TEST FAILURES IN STANDARD QUERIES
1 long
2 medium
5 small
Test failures in continuous queries:
Selected from TPC-DS queries that
execute without error on both systems
when executed alone.
Single Queries Multiple Queries

17. Leverage data warehouse to access data lake
Slide 17 © 2017 Enterprise Management Associates, Inc.
Hadoop Data Lake Vertica Big Data Warehous
CREATE TABLE customer_visits (
customer_id bigint,
visit_num int)
PARTITIONED BY (page_view_dt date)
STORED AS ORC;
Customer information in Hadoop Customer information in Data Warehouse
SELECT customers.customer_id FROM orders RIGHT OUTER JOIN
customers
ON orders.customer_id = customers.customer_id
GROUP BY customers.customer_id HAVING
COUNT(orders.customer_id) = 0;
Vertica Engine
ROS
 Leveraging Web Logs to gain customer insight
 Sensor and IOT data for pre-emptive service
 Marketing Programs Tracking
 Tracking impact of application updates
 Many more uses

18. HPE Vertica Use Cases

19. Rethinking the Data Warehouse
Slide 19 © 2017 Enterprise Management Associates, Inc.
Spark and
in-memory Solutions
Vertica and columnar
data warehouse
Hadoop
• Hardware
costs
• Spark
Expertise
• Per TB
• Data
Warehouse
Expertise
• Per node
licensing
• Hadoop
Expertise
Cost Emphasis
• Licensing Costs
• Per user
• Per connected
system
• Per processor
• Per amount of
data
• Tuning and
expertise
• Getting it to handle
big data
• Maintenance
• Specialized
Hardware
• Closed system
costs
Cost Emphasis
Legacy
data
warehouse

20. Use of Open Source Technologies
Slide 20 © 2017 Enterprise Management Associates, Inc.

21. Cloud Has A Role
• Ingest important data and analyze in real-time
• Understand Immediate challenges
• Tier off data to lower-cost tier for deeper analysis
• Provide daily analytics to meet SLA
• Analyze Big Data for description and predictive
• Cost effectively support concurrent users w/ full SQL support
• Tier off data to lower-cost tier as it ages or SLA changes
• Ingest data of unknown value
• Provide a data lake for data science
• Store data for compliance
• Fire up extra analytics for
a monthly, quarterly, end-
of-year calculation
• Handle seasonal
variations in data and
special projects
• Ensure expensive
hardware doesn’t sit idle
most of the time

22. Cloud Implementations of various technologies
Slide 22 © 2017 Enterprise Management Associates, Inc.

23. HPE Vertica Enterprise
– Columnar storage and advanced compression
– Maximum performance and scalability
HPE Vertica
23
All built on the same trusted and proven HPE Vertica Core SQL Engine
Core HPE Vertica SQL Engine
• Advanced Analytics
• Open ANSI SQL Standards ++
• R, Python, Java, Spark. Scala
• In-database machine learning
HPE Vertica for SQL on Hadoop
– Native support for ORC and Parquet
– Support for industry-leading distributions
– No helper node or single point of failure
HPE Vertica In the Cloud
– Get up and running quickly in the cloud
– Flexible, enterprise-class cloud deployment options
Try it on my.vertica.com

24. How to Use Big Data and Data Lakes to impact the Bottom
Line!
• Big Data and data
lakes are growing in
adoption.
• Data lakes are NOT
just for exploration.
• Maturity matters in
how you implement
and what you
implement in your
data lake
Slide 24 © 2017 Enterprise Management Associates, Inc.

25. Slide 25 © 2017 Enterprise Management Associates, Inc.
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