Il mondo dei DB Cambia : Tecnologie e opportunita` · Il mondo dei DB Cambia : Tecnologie e...

Il mondo dei DB Cambia : Tecnologie e opportunita` Giorgio Raico Pre-Sales Consultant Hewlett-Packard Italiana

©2011 Hewlett-Packard Development Company, L.P.

The information contained herein is subject to change without notice

© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 2

Data Consumption Taxonomy

Transactional/

OLTP

Business Reporting &

Analytic Applications

(Visualization)

Data Management Platform

Data

Integration

Enterprise

Data

Warehouse

Data Mart

Analytics

CRM

Order

ERP

Finance

So

urc

es

Select Extract

Transform Integrate & Load

Reports

OLAP

Apps

Exec Dashboards

Drive transactions

Manage and store information Generate insight

EDW

DM DM

DM

Analytic

Applications

Reporting, Dashboards,

OLAP, Information

Delivery

Unstructured

Data


• Transaction processing

• Real-time processing

• Schemas structure data

• ACID DB system table

• Interactive fast response

• TB data

• Analytic processing

• Not designed for low-latency

access

• The consistency of data is weak

• Distributed with data replication

• Batch oriented; not interactive

• Peta Byte-order data

Transactional Unstructured

INFORMATION ASYMMETRY There will always be a gap between

what you want to know and what is

knowable

4


Information Asymmetry

Social media

Call detail records

Gene sequencing

Sensors

Customer purchasing history

Algorithmic trading

Click streams

Game interactions

A New Class of Information

6 HP Restricted. For HP Internal and Partner Use Only.

Standard Unstructured

Amount 15% 85%

Growth 22% 62%

Is Snoopy a dog ? Information Asymmetry

Wh

at th

ey

Th

ink

Wh

at

they

Do

© Copyright 2011 Hewlett-Packard Development Company, L.P. HP RESTRICTED 8

Customer

Intentions & Suggestions Influence & Evangelism

Preferences Responsiveness

Structured Data

Improve existing knowledge Customer Profile Transactions

Unstructured Data Expose new knowledge Customer Perception

New generation customer intelligence


A Brief Background on Databases

As Prices Decline, Devices Proliferate

As Devices Proliferate, Data Creation Explodes

Consumer Content Creation and Consumption is Increasing

What Issues Do Today’s Data Pose?

Data Volume, Often Data Item Tend to Be Big

Data Items are Being Created Rapidly , New data type

Structure (or Lack of) Cataloging Data



If a Relational General Purpose Databases Can Do every thing , Why bother?

Cost and Performance

Shortcomings of Relational Databases Today

Data Volumes

Parallelization : MPP , Appliances , Clusters

If you go parallel on HW you have to coordinate : Shared Nothing

The trade off when sorting : Columnar Databases

The Hard Drive Bottleneck: In Memory Databases

Speed up latency : Flash Technology



New Data type and use cases are creating New Companies and New

business

Extracting Meaning from Unstructured Data and Human created data

Meta-tagging : Even Unstructured Data Needs Structured Analysis

Transactional Data Machine Data ( web log , click log )

Unstructured Text Data ( blogs , posted text , social )

Other Unstructured Data ( photo , video , voice )


Moving Forward : Drive The Change HP will support your choices

- Change HW/O.S without DB update

- Change HW/O.S. with DB Update

- Change DB same HW/O.S

- Change DB and HW/O.S


Integrity HP-UX

Options

X86/Options

OLTP/SAP

Sybase ASE

IBM DB2

Oracle

EnterpriseDB

Sybase ASE

IBM DB2

NonStop SQL

Oracle

EnterpriseDB

Sybase IQ

IBM DB2

Oracle

Datonix

OLTP DW/DM BI

Sybase IQ

SAP HANA

Vertica

MS SQL Server

EnterpriseDB

Sybase IQ

SAP HANA

Oracle

MS SQL Server

Sybase ASE

EnterpriseDB

Oracle

Sybase ASE

MS SQL

Server

Oracle

Database Options

Datonix

Query Object


Database Options

Database Description

Sybase ASE

Sybase ASE is row-type relational database to support the SAP and non-SAP operational workloads. It

can also be used for Data Warehouse implementations. Sybase can be implemented on Integrity or

ProLiant platforms. Supported Operating Systems are HP-UX, Linux and Windows

HP NonStop SQL

The HP NonStop SQL is an excellent choice for customers who want to migrate off Oracle RAC

Database for their mission-critical enterprise applications offering the highest levels of platform and

database availability and resiliency. NonStop SQL is fully integrated with the NonStop hardware and

software and built upon a scalable shared-nothing, fault-tolerant architecture. It is ANSI standards-based

and ideal for consolidating OLTP and DW workloads.

EnterpriseDB Advanced

Server 9.0

EDB is a database company supporting the Open Source PostgreSQL. It is a row oriented database that

is compatible with the Oracle database. EDB can run on HP-UX, Linux or Windows. It can be deployed

on Integrity or ProLiant platforms for OLTP or Data Warehouse workloads.

Microsoft SQL Server SQL Server has complementary systems that are packaged with SQL RDBMS. These include: an ETL

tool (SQL Server Integration Services or SSIS), a Reporting Server, an OLAP and data mining server

(Analysis Services), and several messaging technologies, specifically Service Broker and Notification

Services. It is a robust enterprise database that can run on a X86 platform in a Windows environment. It

can support OLTP, OLAP as well as CRM or SAP workloads


Database Options (cont.)

Database Description

SAP HANA SAP HANA is new solution available for high-performance analytics and BI. SAP has

announced that it will also support SAP OLTP by the end of 2012. It can be used to

migrate/improve some conventional Oracle technology currently in use by customers.

HANA includes both database memory manager and analytics capabilities, as well as

the ability to integrate with other sources and targets.

Sybase IQ Sybase IQ is a column-type relational database to support the BI data store

workloads. This is the preferred Sybase solution for BI workloads.

Vertica The HP Vertica Analytics System is a fully integrated analytical mart solution. To learn

more, go to http://www.hp.com/go/vertica

Cross/Z The Datonix/Query Object is an Event database management system with columnar-

hierarchical-relational-molap architecture

http://www.hp.com/go/vertica


Database types


“Big Data” originating with analytics – beyond BI

Defining the world of analytics

• Traversing enormous diverse data types to spot patterns

− 10s - 100s of terabytes (TB), petabytes (PB), and yes - even Exabyte's (EB)

• Business needing faster --“real time” (seconds - minutes vs. hours to days)

analytic results

− Combining data from silos

− Analyzing diverse data types and Sources

− Connect data from various business units (cross analyze, access, & reference )

• Growing at exponential rate

− Structured data – data stored in databases

− Unstructured – all other data including emails, social media, blogs, free form feedback,

documents, transaction, multimedia (images, videos, etc.)

− 90% of enterprise information is unstructured

− Data size being a constant moving target


HP Analytics and big data solutions

Vertica

• Real-time, SQL-compliant, ad hoc analytics • Connectors enable information transfer

between Hadoop and Vertica

Autonomy IDOL

• In depth context-based analysis of big data • Builds additional rich, contextual meta data

Hadoop

• Efficient, low cost, open source repository to store and analyze vast amounts of data

• Push button simplicity • Low cost and optimized performance with

real-time and historical monitoring

Red Hat

Enterprise

Linux

Machine-

generated

Social media

Customer

feedback forms

Emails

Str

uc

ture

d

Connecto

rs

Un

str

uc

ture

d

Connecto

rs

Databases warehouses

ERP, CRM

Ad hoc analysis

Connecto

rs

Hadoop

distribution

s Cloudera,

MapR,

Hortonworks

HP

Converged

Infrastructur

e

Autonomy

IDOL

HP

In

sig

ht

CM

U

Visualization

tools

RDBMS,

analytics, dashboards,

Excel

Vertica

TS Consulting

Services

Deep and robust insight end-to-end


HP Vertica Analytics System

Analytics for “real-time” business intelligence

Limitless scaling - add nodes for capacity and

performance

Extreme compression

Columnar

Simplicity, MPP, no single point of failure

Bi-directional Hadoop data connectors

Real-time and ad hoc analytics for next generation business insight of Hadoop

solution

19 HP Confidential

© Copyright 2012 Hewlett-Packard Development Company, L.P.

The information contained herein is subject to change without notice.

• The digital universe will expand by almost half in 2012 - 90% of that data is unstructured

• Traditional systems are not designed to analyze unstructured data

• Hadoop is designed specifically to extract business value from unstructured data

Risk Modeling Fraud Detection Customer Retention Sentiment Analysis Web Mining

Financial Services Government Retail Telecom

Media

Why do we care about Hadoop?



Fast OLTP with Range Queries

No SQL

Vertica

Autonomy

HP Solutions for Hadoop

HP Partners

HP Partners Cloudera MapR Hortonworks

HP Servers and HP Networking

Insight CMU Hadoop Management Software

Vertica

Autonomy

HadoopEcosystem

Augmenting Hadoop

Enabling an ecosystem of end to end, scalable platforms

Datameer

Karmasphere

Consulting Services and Support

Meaning Based Analytics

Ad hoc SQL Compliant Analytics



Combining the strengths

Hadoop for exploratory analysis

Especially with existing MR, Pig scripts

Vertica for interactive analysis

For shared features, often faster than Hadoop with a fraction of hardware

resources

Vertica’s Hadoop connector

+


NOW with

• Data Sources are diverse

Text, Sound, XML, Video and Audio

• It does not exactly match

“Is Snoopy a dog?”

• Meaning is dynamic

• Meaning is multi-layered

• Meaning is relative

• Meaning is a common currency

And Now Unstructured and Humans


Human Information is made up of ideas, is diverse, and has context.

Why is Processing Human Information

Different?

Ideas don’t exactly match like data does; they have distance.

Information is not static – it’s dynamic and lives everywhere.

Meaning is a common currency across all information types.

Social Media Video Audio Email Texts Mobile Transactional

Data

IT/OT Documents Search Engine Images


When the IT world started machines could

not understand the real world of rich

information, so a useful simpler analogy was

created this gave rise to the structured data

world, it has proved very useful.

Over the years there have been many

technology changes, the T in IT has

changed many times, Mainframe , client

server, IP, Cloud……….

IT Platforms Operate On Data with NO

Sentiment Meaning and Context


Thomas Bayes Claude Shannon


The IT industry handles 10%

of the problem, we do 100%

GRAZIE

31

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