1. Vertica Core
Technical Overview

2. Analytics architectures – Right tool, right job
On-premises RDBMS
Analytics are more stable and more performant. Security
Daily analytics
Cloud-based solutions
Easy to spin up and manage temporary workloads
Special workloads
Data lake & discovery
Hadoop
Storage and licensing is cheap. Not all data needs to have fast, advanced analytics

3. The appeal of Vertica Requirement Proof Extreme Optimization
Columnar design for high performance analytics
Aggressive compression
Scalable to petabyte scale
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
Total Cost of Ownership
Simply and predictable pricing
No penalty for additional hardware or connected users

4. The HPE Vertica portfolio
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
HPE Vertica Enterprise
Columnar storage and advanced compression
Maximum performance and scalability
Flex Tables for schema-on-read
Core HPE Vertica SQL Engine
Advanced Analytics
Open ANSI SQL Standards ++
R, Python, Java, Spark. Scala
HPE Vertica for SQL on Apache® Hadoop
Native support for ORC, more
Support for industry-leading distributions
No helper node or single point of failure
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!

5. HPE Vertica Core SQL engine
Core capabilities

6. Core capabilities – Built for speed
We boost performance
What 1000% means:
Use to take
Now takes
1 hour
3.6 Seconds
8 hours (overnight)
Under 30 seconds
“When we did the first queries, they were done so fast, we thought they were broken.”
- Michael Relich, Guess?
Slides that follow describe how we achieve such huge performance increases

7. Secrets to achieving performance increases
Columnar Storage
Compression
MPP Scale-Out
Distributed Query
Projections
Speeds Query Time by Reading Only Necessary Data
Lowers costly I/O to boost overall performance
Provides high scalability on clusters with no name node or other single point of failure
Any node can initiate the queries and use other nodes for work. No single point of failure
Combine high availability with special optimizations for query performance
Memory
CPU
Disk A B D C E

8. Row vs Columnar Storage
Row Storage
Columnar Storage
Traditional Database Storage Method
Requires all data be read on query
Limited compression possible
HP Vertica Database Storage Method
Speeds Query Time by Reading Only Necessary Data
Ready for Compression
Oracle didn’t built this feature from the ground up. We did

9. Example
SELECT SUM(volume) FROM trades WHERE symbol = 'HPQ' AND date = '5/12/2014'
Symbol
AAPL
GOOG
HPQ
IBM
Date
5/12/2014
Time
13:01:07
13:01:09
13:01:10
13:01:11
13:01:13
13:01:14
Price
530.25
32.09
187.84
Volume 20 5
150
1021 50
1230
122
Type
Market
Limit
Buy Stop

10. Compression Compression lowers costly I/O to boost overall performance
Symbol
AAPL
Compression lowers costly I/O to boost overall performance
Vertica uses lossless compression
integer packing for unencoded integers
Lempel–Ziv–Oberhumer for compressible data
Fast and distributable algorithms – splittable across MPP
ªç]AAPL
Parquet and ORC files wish they had the compression we do.

11. Clustering / MPP / Scale-Out
Parallel design
Enables distributed storage and workload with active redundancy
Automatic replication, failover and recovery
Shared-nothing database architecture
Provides high scalability on clusters
No name node or other single point of failure
Add nodes to achieve optimal capacity and performance
Lower data center costs, higher density, scale-out
Memory
CPU
Disk
Hadoop also has a scale out architecture.

12. Distributed query execution
Client connects to a node and issues a query
Node the client is connected to becomes the initiator node
Other nodes in the cluster become executor nodes
Initiator node parses the query and picks an execution plan
Initiator node distributes query plan to executor nodes
Initiator node aggregates results from all nodes
Initiator node returns final result to the user
Nodes are Peers
Any node can be the initiator
No name node or single point of failure
Query/Load to any node
Continuous/ real-time load and query
Memory
CPU
Disk
INITIATOR
Memory
CPU
Disk
EXECUTOR
Memory
CPU
Disk
EXECUTOR
Competitors have helper nodes and name nodes. We don’t

13. Live Aggregate ProjectionsB C D E …
Projections
Live Aggregate Projections A B D C E A B D A B D
SORT
ORDER
MOST
RECENT
SELECT a,b,d FROM projection_name ORDER BY a;
SELECT SUM(A), MIN(B), MAX(D) FROM projection_name

14. Query optimization comparison
Vertica Projections
Are primary storage – no base tables are required
Can be segmented, partitioned, sorted, compressed and encoded to suit your needs
Have a simple physical design
Are efficient to load & maintain
Are versatile – they can support any data model
Allow you to work with the detailed data
Provide near-real time low data latency
Combine high availability with special optimizations for query performance
Traditional Materialized Views
Are secondary storage
Are rigid: Practically limited to columns and query needs, more columns = more I/O
Are mostly batch updated
Provide high data latency
Traditional Indexes
Are secondary storage pointing to base table data
Support one clustered index at most – tough to scale out
Require complex design choices
Are expensive to update
Provide high data latency

15. Solutions and applications
Core
Security & Manageability
Flexible Deployment
Data Sources & Formats
Solutions and Applications
Advanced Analytics

16. HP Vertica Pulse Perform sentiment analysis Solution Challenge
Performing sentiment analysis is time-consuming and tedious
HP Vertica Pulse
Entity extraction, sentiment
HP Vertica Place
Vertica Analytics Platform
Solution
Social Media Connector
HP Vertica Pulse
Scalable, in-database entity extraction and sentiment analysis
Aggregate and drill-down views
Easy to get started
Social
Hadoop
Machine

17. HP Vertica Distributed R
R-based analytics
Memory
CPU
Disk R
Challenge
Customers want to use R for analytics. However, R scalability is always a question
Algorithm
Use cases
Linear Regression (GLM)
Risk Analysis, Trend Analysis, etc.
Logistic Regression (GLM)
Customer Response modeling, Healthcare analytics (Disease analysis)
Random Forest
Customer churn, Market campaign analysis
K-Means Clustering
Customer segmentation, Fraud detection, Anomaly detection
Page Rank
Identify influencers
Solution
HP Distributed R
Analyze data sets too large for standard R
Perform complex analyses much more quickly (20x faster than Hadoop)
Use familiar R environment to explore data, develop, and execute algorithms
Operate on full data set (no down sampling)

18. data with understanding of geometry and/or geography
Vertica Place
Geospatial Analysis
Challenge
Analysis of
data with understanding of geometry and/or geography
Solution
HP Vertica Place
Optimized Spatial Joins with memory-resident Geospatial Indexing replaces expensive scans with easy look-ups
Easy to use OGC-standard based implementation with spatial functions to compute: distance, intersections
Simple integration with third party applications
SELECT STV_Intersect(gid, geom
USING PARAMETERS
index=‘/dat/states.idx’)
OVER()
AS (call_gid, state_gid)
FROM calls;

19. Security & Manageability Solutions and Applications
Advanced analytics
Core
Security & Manageability
Flexible Deployment
Data Sources & Formats
Solutions and Applications
Advanced Analytics

20. Business impact of advanced analytics
Advanced analytical functions speed development
Event-based Windows
Monte Carlo
Time Series Analysis
Regression Testing
Geospatial/Place
Social Media/Pulse
Much more
What analytical functions mean:
Use to take
Now takes
Hundreds of lines of code
Fewer lines of code
SELECT CustomerName,City FROM Customers;
INSERT INTO Customers (CustomerName, ContactName, Address, City, PostalCode, Country) VALUES ('Cardinal','Tom B. Erichsen','Skagenzzzzz 21',‘Cambridge',‘02140',USA');
INSERT INTO Customers (CustomerName, ContactName, Address, City, PostalCode, Country) VALUES ('Cardinal',’Massachusetts’ ');
SELECT CustomerName,State FROM Customers;
Other companies don’t have all the analysis we have.
It goes beyond the TPC-DS benchmarks

21. From hindsight to insight to foresight
Descriptive
Diagnostic
Predictive
Prescriptive
Pre-emptive
What is the attrition rate for the last 6 months?
Which customers have I lost?
Why has the attrition rate increased?
Which customers are the most likely to attrite if I don’t contact them?
Who will if I contact them?
Which customers should I target to maintain?
What if…?
What can I offer before the customer realizes the need?
Value add?
What
Happened?
Why did it
Happen?
What will
Happen?
What
should I do?
What more
Can I do?
INFORMATION
INSIGHTS
DECISION
ACTION

22. Shortcomings of Big Data analytical solutions
Item
Description
Example use case
WHERE clause subqueries
Fast calculations on data that you retrieve using SQL Windowing functions
Group clickstream data into sessions to analyze a web visitor’s browsing behavior.
Calculate moving-average for stock ticker data.
Data Types
Defining attributes with data types, such as money, time, and JSON
Validating Date and Time Values
Calculating Time Difference
Understanding which monetary units were used for a transaction
Understanding partial monetary units, like cents, from whole monetary units. For example, 3.15 specifies 3 dollars and 15 cents.
MERGE-JOIN
Joining data from separate tables
Looking up vendor information from a vendor table – ERP and CRM mash up
Mashing up CRM data with sentiment analysis
Leveraging reference data
Many, many uses
Geospatial Functions
Analysis of data with understanding of geometry and/or geography
Understanding if an object’s location is inside or outside a zone/circle/area
Latitude and longitude applications
Sentiment Scoring Functions
Parsing social media data to understand its relationship to negative/positive words and phrases
Acquiring data from twitter and performing sentiment analysis

23. Analytical features of Vertica
Vertica SQL
Standard SQL-99 Conventions
Vertica Extended-SQL
Advanced Analytics with SQL
Vertica Innovations
Advanced Analytics using Custom Logic
Vertica User Defined Extensions
Aggregate
Sessionization
Regression Testing
Analytics
C++
Java R
Connection
ODBC/JDBC
HIVE
Hadoop
Flex Zone
Analytical
Time Series
Time slice
Interpolation (Constant & Linear)
Gap Filling
Statistical Modeling
Window Functions
Event-based Windows
Conditional Change Event
Conditional True Event
Classification Algorithms
Graph
Event Series Joins
Page Rank
Monte Carlo
Social Media/Pulse
Text Mining
Patterns/Trends
Text-mining
Geospatial
Pattern Matching
Match, Define, Pattern Keywords
Funnel Analysis
Geospatial (Place)
Statistical

24. HP Vertica Enterprise – Proven
Advanced ANSI SQL Analytics
Massively Parallel Processing
Column Orientation
Application Integration
Highly Available
Automatic DB Designer
Advanced Compression
Management Console
Commodity Hardware (x86)
ANSI SQL ENGINE
Columnar Formats (ROS, Flex)
File System (EXT 4)
Commodity Hardware (x86)
ANSI SQL ENGINE
Columnar Formats (ROS, Flex)
File System (EXT 4)
Commodity Hardware (x86)
ANSI SQL ENGINE
Columnar Formats (ROS, Flex)
File System (EXT 4)
Commodity Hardware (x86)
ANSI SQL ENGINE
Columnar Formats (ROS, Flex)
File System (EXT 4)
Same core engine as HP Vertica with Hadoop as the data storage layer
Perform analytics regardless of the format of data or Hadoop distribution used
Robust, enterprise-ready solution with world-class enterprise support and services
Open APIs and developer tools with a vibrant ecosystem of partners to support your big data project
Ease management of big data – solution is part of a greater HP Enterprise Software Platform – Haven

25. Data ingestion and storage
Core
Security & Manageability
Flexible Deployment
Data Sources & Formats
Solutions and Applications
Advanced Analytics

26. On-premise data access
Streaming
Kafka, Trickle (Insert/Update)
Schema on read
Flex Zone: JSON, CSV, TEXT, Social Media
Batch
ODBC/JDBC, Bulk COPY, LCOP, ETL: Pentaho, Attunity, Informatica, Talend, ET AL
Unstructured
IDOL: Video, Audio, Voice Recognition, Facial Recognitiion
Hadoop
ORC Reader, MapR NFS, HIVE Serializer: HDFS, Parquet, AVRO
Vertica Cluster

27. HPE Vertica for SQL on Apache® Hadoop features and benefits
Query data, no matter where it is located
Install HP Vertica directly on your Hadoop infrastructure
ORC, Parquet, Avro, Vertica ROS and JSON supported
Full-functionality ANSI SQL
100% of TPC-DS queries
No helper node or single point of failure
Competitive price point
Analytical Applications R
Java
Python
SQL
HPE Vertica Core Engine
Store: ROS
Ingest: AVRO, JSON, etc.
Query: ORC & Parquet
Same core engine as HP Vertica with Hadoop as the data storage layer
Perform analytics regardless of the format of data or Hadoop distribution used
Robust, enterprise-ready solution with world-class enterprise support and services
Open APIs and developer tools with a vibrant ecosystem of partners to support your big data project
Ease management of big data – solution is part of a greater HP Enterprise Software Platform – Haven

28. HP Vertica for SQL on Apache® Hadoop
Same Vertica MPP Columnar Architecture
Base ANSI SQL
Co-Located with Hadoop
Data Query Across parquet, ORC, JSON, and many other format
Hadoop Agnostic
Commodity Hardware (x86)
ANSI SQL ENGINE
Open Formats + ROS/Flex
HDFS
Commodity Hardware (x86)
ANSI SQL ENGINE
Open Formats + ROS/Flex
HDFS
Commodity Hardware (x86)
ANSI SQL ENGINE
Open Formats + ROS/Flex
HDFS
Commodity Hardware (x86)
ANSI SQL ENGINE
Open Formats + ROS/Flex
HDFS
Same core engine as HP Vertica with Hadoop as the data storage layer
Perform analytics regardless of the format of data or Hadoop distribution used
Robust, enterprise-ready solution with world-class enterprise support and services
Open APIs and developer tools with a vibrant ecosystem of partners to support your big data project
Ease management of big data – solution is part of a greater HP Enterprise Software Platform – Haven

29. New ORCFile reader Before After Hcat Connector ORC Reader
Open Source Project jointly developed by Vertica and Hortonworks Engineers
Query close to where data resides
Column Pruning, Predicate Pushdown
Hadoop Data Nodes
Hadoop Data Nodes
VSQLH SP1
VSQLH SP2
Before
After
Hcat Connector
SerDes
WebHCat
ORC Reader
External Table
WebHDFS
HDFS ORCFiles
HDFS ORCFiles
The ORCReader project as I mentioned before is Open Source on GitHub. Interestingly we have had contributors from Microsoft, but the majority of the code is from HWX and Vertica. The team in Pittsburg is happy to announce this project being pushed upstream into the main Apache Hadoop project.
The ORCReader is a NEW way to directly access ORCFile formatted data and replaces the initial access method used via Hcat Connector. Instead of using SerDes, the ORCReader uses External Table mechanism to access ORC data.

30. HPE Vertica for SQL on Apache® Hadoop users
Analysts no longer need to care where the data is located or how it is stored. They can use favorite BI/data visualization tools.
HPE Vertica ANSI SQL
Fastest
DBAs now have a single way to access data whether it is in Hadoop or Vertica and implements complete Information Life Cycle Management.
HP Vertica Optimized Storage
Fast
Hadoop Storage
Data engineers can now use SQL in addition to MapReduce, Hive, etc. to explore and analyze the data.
HPE Veritica for SQL on Hadoop Users
Analysts are most interested in using their preferred BI/data visualization tools to provide analytical insight to the organization in the form of dashboards and reports. They are less concerned with where the data stored, provided that they can actually query the data and visualize the results.
DBAs are under duress in handling the various workloads and requests from the organization. Using Hadoop for less-demanding, less-intensive analytical requests to tier off colder data and tapping into the rich, high-performance analytics on data stored in the ROS format helps them to address the full and varying needs of internal and external users.
Data Engineers can ingest all emerging forms of data, including AVRO and JSON, from sensor data and log files for increasingly popular analytical use cases. For top performance, these data engineers can store data in the highly optimized Vertica ROS format. If their organization is looking to derive value from the existing data stored in Hadoop data lakes – such as ORC and Parquet – HPE Vertica for SQL on Hadoop can fit that need, too.

31. Data storage options and performance
Query Engine
Vertica
ANSI SQL-99
HPE Vertica SQL on Hadoop
Vertica
ANSI SQL-99
Vertica
ANSI SQL-99
Vertica
ANSI SQL-99
Vertica
ANSI SQL-99
Format
Vertica ROS
Vertica ROS
Hadoop Format
Flex Tables
Flat Files
File System
EXT4
HDFS
HDFS
HDFS
HDFS
Fastest
Analytics
Structured
Slowest
Discovery
Semi-Structured
Performance

32. Hadoop agnostic Powerful SQL on Hadoop for any Hadoop distribution
Works with:
Cloudera Distribution Including Apache Hadoop (CDH) 4.6, 5.0, and 5.0.1
Hortonworks Data Platform (HDP) 2.1
MapR and 3.0.3
Also:
MapR NFS may be used as storage
Versions update frequently. Check online documentation for a detailed description and updates to compatibility information.

33. Security & manageability
Core
Security & Manageability
Flexible Deployment
Data Sources & Formats
Solutions and Applications
Advanced Analytics

34. Security
SSL security protocol to authenticate, encrypt and verify access
Use SQL to GRANT/REVOKE to database, schema, table, views, insert/update/delete, etc.
Manage Users in the Vertica Management Console or use LDAP/Kerberos for external management
Security
Authentication
Authorization
Encryption

35. Database designer Leverage Typical Queries Sample Data/Schemas
Historical Statistics and Logs
Optimize
Query Performance
Data Loading
Storage Footprint
Benefit
Faster Queries
Lower Hardware Costs
Shortened design time
Lower costs to maintain and optimize

36. Workload management Problem: Rogue queries can take over
Affect loads or tactical queries
Steal Resources from Daily Analytics
Disrupt Business Processes
Solution: Workload Management
Reserve resources for high-priority queries
Apply run-time prioritization to manage CPU and I/O
Take control of rogue queries
SELECT "Physical Exam".ParticipantId, "Physical Exam".SequenceNum, "Physical Exam".Date, "Physical Exam".Day, "Physical Exam".Weight_kg, "Physical Exam".Temp_C, "Physical Exam".SystolicBloodPressure, "Physical Exam".DiastolicBloodPressure, "Physical Exam".Pulse, "Physical Exam".Respirations, "Physical Exam".Signature, "Physical Exam".Pregnancy, "Physical Exam".Language, AverageTempPerParticipant.AverageTemp, FROM "Physical Exam"
INNER JOIN AverageTempPerParticipant
ON "Physical Exam".ParticipantID=AverageTempPerParticipant.ParticipantID

37. Security & Manageability Solutions and Applications
Deployment & services
Core
Security & Manageability
Flexible Deployment
Data Sources & Formats
Solutions and Applications
Advanced Analytics

38. Vertica customer experience
Intelligent approach to implementation, support, and expansion
Professional Services
Architecture and implementation best practices to maximize value
Training
Implementation assistance
Health check audits
Technical Support
Deep product expertise to provide world class support
24x7 Critical issue support
Deep technical expertise
Case management
Product updates
HPE Big Data Community
Tools to engage with the Vertica community
MyVertica user forum
Web-based training
Scripts and code sharing
Customer / Technical Account Management
Proactive relationship management to ensure your success with Vertica
Account review
Issues escalation
Executive engagement
Product management
Solution Architects / Information Development / Partner Engineering
Best practices, complete solutions
Clear concise technical documentation
Partner relationships and tools

39. Vertica professional services
Targeted Vertica implementation expertise and guidance
Focus Areas
Training
Implementation
Assessment
What we provide
Expert Vertica assistance
On site project team mentoring
Vertica platform implementation and best practices advice
Public, private, and free resources
System Administrators
DBAs
Application Developers
Online and in Person
Vertica proactive project assistance
SW installation
Data loading
Query performance tuning
Go Live support
Continuity planning
Cluster maintenance
Vertica Health Check
On site expert system review and data collection
Detailed audit report of findings
Implementation of recommendations (optional)

40. Vertica makes data matter
Purpose built for Big Data from the first line of code
Gain insight into your data 50x-1,000x faster than legacy products
Real time analytics
Infinitely scale your solution by adding an unlimited number of low cost nodes
Massive scalability
Built-in support for Hadoop, R, and a range of ETL and BI tools
Open architecture
Store 10x-30x more data per server than row databases with patented columnar compression
Optimized data storage
On-premises
Private Cloud
Public Cloud
Hadoop
Deploys to:
Vertica Analytics Platform Overview
The HP Vertica Analytics Platform solves real-world Big Data challenges. It is purpose-built for organizations of all sizes to monetize data at hyper-speed and massive scale needed to differentiate in today’s competitive economic climate.
The HP Vertica Analytics Platform delivers:
Blazing Fast Analytics - Gain insights into your data in near-real time by running queries 50x-1,000x faster than legacy products
Massive Scalability - Infinitely scale your solution by adding an unlimited number of industry-standard servers
Open Architecture - Protect and embrace your investment in hardware and software, with built-in support for Hadoop, R, and a range of ETL and BI tools
Optimized Data Storage - Store 10x-30x more data per server than row databases with patented columnar compression
Deployment is up to you …
What can the HP Vertica Analytics Platform do for you?
The HP Vertica Analytics Platform is truly built for analytics with technology born of the modern age. It is not a back-end legacy database, nor does it merely store your data. The HP Vertica Analytics Platform enables you to have a conversation with your data in ultimately finding the answers you need to monetize Big Data.

41. Thank you
Thank you.