1. Steve Sarsfield @SteveSarsfield Vertica - Hewlett Packard Enterprise
Preparing your organization to derive insight from the internet of things
Steve Sarsfield @SteveSarsfield
Vertica - Hewlett Packard Enterprise
March 2017

2. 11/28/2017 3:17 AM
Driving customer demand for a smarter and more personalized product experience
Predictive maintenance
Fraud detection
Electronic health records
Presenter Name
Customer support
Product recommendations

3. Challenges Handling more data Time to deliver analytics
Costs of License
Tuning Costs
Skills to leverage new tools

4. The future belongs to those who analyze without limits
With analytics free from closed infrastructure and narrow deployment options
Traditional data warehouse lock-in
Cloud analytics deployment lock-in
Hadoop and open source

5. HPE Vertica
All built on the same trusted and proven HPE Vertica Core SQL Engine
HPE Vertica In the Cloud
Get up and running quickly in the cloud
Flexible, enterprise-class cloud deployment options
The HPE Vertica Portfolio
Regardless of how our customers want to consume and deploy Vertica, we have them covered.
Most importantly, the entire Vertica Portfolio is based on the same, trusted, field-proven Vertica SQL engine and rich analytical functionality.
So, whether customers need to access Big Data analytics via the cloud either as SaaS or run on select Amazon hardware, on-premise, or co-located Hadoop, no one provides the breadth of functionality and consumption models as HPE Vertica!
HPE Vertica Enterprise
Columnar storage and advanced compression
Maximum performance and scalability
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

6. The appeal of Vertica Requirement Proof Extreme Optimization
Columnar design for high performance analytics
Aggressive compression
Scalable to petabyte scale
Total Cost of Ownership
Simply and predictable pricing
No penalty for additional hardware or connected users
Ready for your Enterprise
SQL compliant to 100% of the TPC-DS benchmark queries
Secure and ACID compliant
No single point of failure
Open and Compatible
Open platform – Standards compliant SQL, Python, Java
Working with open source community on Spark, Hadoop, Kafka, etc.

7. Bridging the gap between high cost legacy EDWs and Hadoop data lakes
Legacy Electronic Data Warehouse
Declining performance at scale
Built on aging technology
Expensive w/ proprietary hardware
Limited deployment options
Data Lakes
Low-cost storage of Big Data
Some analytics capabilities
Holding area for certain data

8. Complexity – Example: Analytics Ready for Internet of Things
R, Python and Custom Analytics
Goal
Deliver analysis of critical data at the source of the data and provide faster time to insight
Access rich custom and predictive analytics in your favorite languages and tools, including R, Python, and custom functions.
Live Aggregate Projections
Speed up queries that rely on resource-intensive aggregate functions like SUM, MIN/MAX, COUNT and Top-K
Pattern Matching
Find matching subsequences of events, compare the frequency of event patterns
Event Windows
Break a sequence into subsequences based on certain events or changes
Event Series JOINS
Correlate events across streams when the times do not line up
SQL-99
Full ANSI SQL compliant

9. How to fill analysis gaps
Customer Segmentation
Channel & Location Analysis
Net Profit Revenue
Geospatial
Data Types
Geospatial – There are no native SQL on hadoop function in any of the solutions. However, you can bring in a solution like SpatialHadoop, a MapReduce extension to Apache Hadoop designed specially to work with spatial data. Very time-consuming
Data types – Vertica supports date, time and many more data types than SQL on Hadoop solution
In–place JOINs – On Vertica allows you to JOIN data that is sitting in your Vertica data warehouse with data that is sitting in an ORC or Parquet file in Hadoop. In other solutions, you must move the data.
Time Series Gap - Since both time and the state of data within a time series are continuous, it can be challenging to evaluate SQL queries over time. Input records often occur at non-uniform intervals, which can create gaps. To solve this problem Vertica provides: 1) Gap-filling functionality, which fills in missing data points; 2) Interpolation scheme, which constructs new data points within the range of a discrete set of known data points. This is not available on Hadoop solutions
Event window - Event-based windows let you break time series data into windows that border on significant events within the data. This is especially relevant in financial data where analysis often focuses on specific events as triggers to other activity.
Sessionization - Sessionization, a special case of event-based windows, is a feature often used to analyze click streams, such as identifying web browsing sessions from recorded web clicks.
Time series gap analysis
Event window functions
Sessionization
Statistical functions
In-place JOINs
With some solutions, you may be required to fill the gaps with
Spinning together two or more open source projects
Moving and copying big data
Using Generic Data Types
Data munging
Custom Code

10. Perhaps the ultimate architecture is all-inclusive
Apache Spark, Hadoop and Kafka
HPE Vertica
Optimal Use Case
Deep Analysis
Massive scale
Many concurrent users
Challenges:
After transformation is done in Spark, need faster load of data into Vertica for SQL Analytics
Supply data to Spark machine learning analytics
Solution: Vertica open-source connector to Apache Spark
Benefits:
Fast, scalable data transfer, exploiting Vertica’s parallelism, HDFS connectivity, and fluency with open source data formats
Optimized query where processing is pushed down to Vertica
Spark users can benefit from Vertica’s very advanced SQL analytics
Features:
Analyze-in-place without data movement via native ORC and Parquet readers
Any Hadoop
Run ON the Hadoop cluster or ON Vertica cluster
Features:
Vertica performs optimized data load from Spark
Spark runs queries on Vertica data
Kafka
Spark
Optimal Use Case
Small, fast running queries
ETL and complex event processing
Operational analytics
Hadoop
Optimal Use Case
Data lake
Warm, cold storage
Data discovery
ETL
Features:
Share data between applications that support Kafka
Data streaming into Vertica

11. Average annual benefit: $3,014,583
11/28/2017 3:17 AM
“The choice was simple: the change to Vertica was much more cost effective than scaling their current Oracle system, while offering a much improved performance to execute very complex analytics use cases”
ROI: 351%
Payback: 4 months
Average annual benefit: $3,014,583
Presenter Name

12. Suunto – Internet of Things (IoT)
Suunto enriches extreme-sport experience with IoT wearable analytics on Vertica
Challenges
User data needed to be instantly gathered and compared to not only the user’s own historical data, but also data from specific sub-segments of Suunto users who have completed similar performance and demographic characteristics
Management and analysis of 20 to 30 million individual training sessions per month in near real-time
Results
Ability to organize over 1 billion data measurements and cluster sub-segments of this data in a manner that allows for meaningful performance evaluation and improvement based upon real user data
Combine a variety of data measurements from devices and users to instantly provide summary performance data, peer comparisons, and training regimens
Vertica has enabled Suunto to provide a new level of value that feeds the competitive nature of Suunto’s customers.
HPE Confidential 12
28 November 2017
“Vertica helps provides analytics so that athletes
can train better and achieve more.”

13. Checklist for preparing for IoT
Open up your systems (not just open source)
Reconsider expensive legacy
Solutions that scale
Consider differing analytical workloads
Skills to leverage new tools

14. Think outside the box - New York Genome
Develop algorithms to find molecular cause of diseases
Deal with errors in DNA sequencing
Share results to community of scientists
Compare tumor DNA to patient’s blood to find variant
Precision medicine
Suggest drugs to interfere with mutation
Specific cancer drugs
Arthritis
Alzheimer's
Parkinson's
Asthma
Diabetes
Autism
Cancer G C
A T
2-3 years old
Collaboration between 17 or so hopitals – so that they wouldn’t all have to start their own genome center
Cornell, NYU, Stoneybrook medical, NY stem cell foundation
Gene Sequencing Data
3 Billion letters
150 GB per person stored raw data
450 GB with analytics
Cancer genome – just under a TB
Compare to Reference Gene

15. Thank you Steve.Sarsfield@hpe.com Community Edition my.vertica.com
11/28/2017 3:17 AM
Community Edition
my.vertica.com
Thank you
Presenter Name