© copyright 2014 hewlett-packard development company, …h41382....© copyright 2015...


The innovative HP Big Data Technology stackand the use cases


Realtime Analytics of extreme data

Helmut SchmittSales Manager DACH

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.3

Big Data is a Massive Disruptor

“A 100 fold multiplication in the amount of data is a 10,000 fold multiplication in the number of patterns we can see in that data.”

Philip Evans: Boston Consulting Group Fellow, Ted Talk


Industry-leading breath & depth of capabilities

ContextualSearch

DataExploration

Image/VideoAnalytics Geospatial

Analytics

SQL onHadoopAccelerated

AnalyticsSentiment

Analysis

PredicativeAnalytics

Haven Big Data Platform

Access Explore Enrich Analyze Predict Serve Act

Andmore…..

Core Big Data Business Capabilities

On-premise In the Cloud


DATA is an organization’s most strategic asset

Monetize

Differentiate

Personalize

Monitor

Meter

Optimize

Predict

…and more



Monetize

Differentiate

Personalize

Monitor

Meter

Optimize

Predict

…and more

…and its greatest risk

Regulate

Comply

Control

Secure

Address

Ensure



The Big Data Balance Sheet

Monetize

Differentiate

Personalize

Monitor

Meter

Optimize

Predict

…and more

Regulate

Comply

Control

Secure

Address

Ensure

Assets Liabilities


We will be the trusted partner for every organization

Store

*Explore

*Govern

*

Protect

*

Serve

*



The Big Data flow

Store & Explore Serve Govern & Protect

Business resiliency

Operations Analytics

Predictive Maintenance

Smart Metering

Patient analytics

Fraud prevention

Records Management

Advertising analytics

Legal & Compliance

Structured enterprisedata repositories

Cloud-basedrepositories

Mobile & social media

Offsite or removable data repositories

Address business & operational objectivesEnterprise Content ManagementEnterprise Search & Collaboration

Legacy Data Cleanup

Legal HoldsInformation Archiving Records

ManagementeDiscovery

Address legal & compliance objectives

Backup & Recovery

Disaster Recovery

Address informationmanagement objectives

Business Resiliency

Long-Term Retention

Unstructured enterprisedata repositories

Data


Used in association with ANPR

Vehicle Recognition

• Match Make and/or Model– Easy to train– Real-time matching

• Alert or Search for Vehicle without registration

• Validate database using ANPR result to identify illegal plated vehicles


Core Capabilities – Built for Speed• We boost performance

Use to take Now takes

1 hour 3.6 Seconds

8 hours (overnight) Under 30 seconds

What 1000% means:

"When we did the first queries, they were done so fast, we thought they were broken.“

- Michael Relich, Guess?


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

CPU

Memory

Disk

CPU

Memory

Disk

CPU

Memory

Disk

A B D C E A


Query Optimization Comparison

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

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 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


Analytical Features of VerticaVertica SQLStandard SQL-99 Conventions

Vertica Extended-SQLAdvanced Analytics with SQL

Vertica InnovationsAdvanced 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• Aggregate

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


HP Vertica Distributed R

Challenge: Customers want to use R for analytics. However, R scalability is always a question

SOLUTION: HP Distributed RBenefit:

• 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)

R-based Analytics

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 ClusteringCustomer segmentation, Fraud detection, Anomaly detection

Page Rank Identify influencers

CPU

Memory

Disk

CPU

Memory

Disk

CPU

Memory

Disk

R R R


• HP Vertica for SQL on Hadoop offers the only full-featured query engine on Hadoop

- Same Core Engine

- Hadoop Distribution Agnostic

- Enterprise-ready Solution

- World-class Enterprise Support and Services

- Open platform

- Ready for Haven

• Competitive price point

Introducing HP Vertica for SQL on Hadoop

Hadoop Storage

Vertica ANSI SQL

Data Exploration


One Query Engine to Serve it all

• Query data in place in Hadoop Formats

• Co-Locate and leverage existing Hadoop infrastructure

• HP Vertica performance on lower-cost infrastructure

• Single query engine across diverse formats and infrastructure

Query Engine

Format

File System Vertica (EXT4)

Vertica Optimized (ROS, Flex Tables)

HP VerticaANSI SQL

Hadoop (HDP, CDH, MapR NFS)

Hadoop (ORC, Parquet, et al)

Store Data in HP Vertica or any Hadoop Distribution


Which Version Is Right for You?

• For Hadoop environments only• Full MPP SQL engine• Includes JOINs, time series

analysis and Key Value• Management tools including

workload management, database designer and back-up and restore

• Hadoop Agnostic Compatibility• Flex Zone• Compression and Columnar Store• Java UDx

SQL on Hadoop

HP Vertica EE

Highly Optimized HP Vertica EXT4 file system

Accelerated Analytics , Live Aggregate projections, Geospatial and Sentiment Analysis

HP Vertica for SQL on Hadoop

• Discover Data

• Control Costs

• Leverage Hadoop Infrastructure

• No Frills, No Brainer

HP Vertica Enterprise Edition

• Boost Performance

• Faster Analytics

• Deeper Analytics

• Customize Analytics Infrastructure

• All the bells and whistles

C++ UDx / UDL


High End Scalability

Vertica Community edition:

Up to 3 nodes

Up to 1 Terabyte

Free for productive use

Scale up to Enterprise edition

Add nodes on the fly

Scale up to PB

Embed Hadoop

Think Big – Start Small


Leaders don’t make compromises

• Promotional Testing • Behavior Analytics

• Claims Analyses • Click Stream Analyses

• Patient Analyses • Network Analyses

• Clinical data Analyses • Customer Analytics

• Fraud Monitoring • Compliance Testing

• Financial Tracking • Loyalty Analysis

• Trading Analytics • Marketing Analytics


Communications, Media & Ent.

ConsumerWeb

Health & Life Sciences

RetailFinancial Services

Energy Public Sector

Clickstream Analytics ✓ ✓ ✓ ✓ ✓ ✓

Customer Analytics ✓ ✓ ✓ ✓ ✓ ✓ ✓

Hadoop Accelration

EDW Modernization

Fraud Detection ✓ ✓

Transaction Analytics ✓ ✓ ✓

Compliance ✓ ✓

Security ✓ ✓ ✓

Operations Analytics ✓

Sensor Data Analytics ✓ ✓

HP Vertica’s Top Use Cases & Verticals Click to view Use Case


Big Data @ World TourPräsentationen:

14:20 - 14:50 Uhr- Big Data Anwendungsfälle – handfeste DemosBernd Mußmann

15:20 - 15:50 Uhr - Der innovative HP Big Data Technologiestack und seine EinsatzgebieteHelmut Schmitt

16:00 - 16:30 Uhr - Big Data Infrastrukturen und Services für das datenorientierte UnternehmenPhilipp Koik & Jochen Mohr

16:40 - 17:10 Uhr - Big Data as a Service – Herangehensweisen und BeispieleJens Scheffler

Ausstellung:

Cape-to-Cape

HP Big Data Referenzarchitektur

HP Big Data für die IT

HP Software Technologie-Stack:

HP Big Data Services

Transformation Experience Workshop:

GoalsUnderstanding what Big Data is Defining customer’s Big Data challenges Evaluating business and IT priorities Introducing HP Big Data solutionsBuilding a Big Data transformation roadmap

Customer Benefits

Understand the benefits, scope, scale and critical success factorsLeverage best practicesGain stakeholder commitmentEstablish a common understandingParticipants C-level, senior staff/initiative owners (5- 8 persons) 2-3 Sr. HP consultants, HP Sales


HP Big Data Transformation WorkshopGoals

• Understanding what Big Data is • Defining your Big Data challenges • Evaluating business and IT priorities • Introducing HP Big Data • Building a customized Big Data transformation roadmap

Your Benefits

• Understand the benefits, scope, scale and critical success factors• Leverage best practices• Gain stakeholder commitment• Establish a common understanding, consensus and alignment

Participants :

• C-level, senior staff/initiative owners (5- 8 persons) • Senior HP consultants, HP Sales

Location & Time-slots :

• Reception/Check-in desk for Big Data Transformation Workshop, Level 1, Kap Europa

• Information desk for Transformation Workshops, Level 4- entrance of exhibition hall

• Session Options:-11:00 -11:30, 12:00 -12:30, 13:30-14:00, 14:20 – 14:50, 15:20 –15:50, 16:00 – 16:30, 16:40 – 17:10


Rekordjäger Rainer Zietlow


Thank you

© copyright 2014 hewlett-packard development company, …h41382....© copyright 2015...

Documents