Virtual Data Warehouse Appliances



From this document you will learn the answers to the following questions:

What type of disk storage is used in the Warehouse Appliance?

Where are virtual appliances being used?

What type of solution do end users want?

Similar documents
IBM Netezza High Capacity Appliance

High-Performance Business Analytics: SAS and IBM Netezza Data Warehouse Appliances

Emerging Technologies Shaping the Future of Data Warehouses & Business Intelligence

Kronos Workforce Central 6.1 with Microsoft SQL Server: Performance and Scalability for the Enterprise

Lenovo System x servers achieve top customer satisfaction scores in 1Q15. May 2015 TBR T EC H N O LO G Y B U S I N ES S R ES EAR C H, I N C.

WHITE PAPER Get Your Business Intelligence in a "Box": Start Making Better Decisions Faster with the New HP Business Decision Appliance

System x x86 servers from Lenovo achieve top customer satisfaction scores. January 2015 TBR T EC H N O LO G Y B U S I N ES S R ES EAR C H, I N C.

Business Intelligence

Advanced In-Database Analytics

Maximum performance, minimal risk for data warehousing

IBM System x reference architecture solutions for big data

Tap into Big Data at the Speed of Business

Microsoft Analytics Platform System. Solution Brief

Information management software solutions White paper. Powerful data warehousing performance with IBM Red Brick Warehouse

SQL Server 2012 Parallel Data Warehouse. Solution Brief

Microsoft s SQL Server Parallel Data Warehouse Provides High Performance and Great Value

EMC/Greenplum Driving the Future of Data Warehousing and Analytics

Five Technology Trends for Improved Business Intelligence Performance

Server Consolidation with SQL Server 2008

Business-centric Storage FUJITSU Hyperscale Storage System ETERNUS CD10000

HP and Business Objects Transforming information into intelligence

Data Warehouse Appliances: The Next Wave of IT Delivery. Private Cloud (Revocable Access and Support) Applications Appliance. (License/Maintenance)

TRENDS IN DATA WAREHOUSING

Oracle on Oracle. Hans Peter Kipfer Vice President, Engineered Systems EMEA

Hadoop for Enterprises:

Make the Most of Big Data to Drive Innovation Through Reseach

Customers award top satisfaction scores to IBM System x x86 servers. August 2014 TBR T EC H N O LO G Y B U S I N ES S R ES EAR C H, I N C.

Netezza and Business Analytics Synergy

QlikView Business Discovery Platform. Algol Consulting Srl

Big Data on the Open Cloud

SQL Server Business Intelligence on HP ProLiant DL785 Server

Protect Microsoft Exchange databases, achieve long-term data retention

Managing Big Data with Hadoop & Vertica. A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database

Supply Chain Management Build Connections

Flash Memory Arrays Enabling the Virtualized Data Center. July 2010

Evolving Solutions Disruptive Technology Series Modern Data Warehouse

White Paper February IBM Cognos Supply Chain Analytics

The Power of Predictive Analytics

Using and Choosing a Cloud Solution for Data Warehousing

Microsoft SQL Server and Oracle Database:

Please give me your feedback

Einsatzfelder von IBM PureData Systems und Ihre Vorteile.

HP ProLiant Gen8 vs Gen9 Server Blades on Data Warehouse Workloads

SOME STRAIGHT TALK ABOUT THE COSTS OF DATA WAREHOUSING

Microsoft SQL Server and Oracle Database:

BIG DATA APPLIANCES. July 23, TDWI. R Sathyanarayana. Enterprise Information Management & Analytics Practice EMC Consulting

Is Hyperconverged Cost-Competitive with the Cloud?

Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing

Big Data and Its Impact on the Data Warehousing Architecture

AT&T Leverages HP Vertica Analytics Platform to Change the Economics of Providing Actionable Insights to Decision Makers

SAN vs. NAS: The Critical Decision

Data Warehouse as a Service. Lot 2 - Platform as a Service. Version: 1.1, Issue Date: 05/02/2014. Classification: Open

Business Intelligence & Data Warehouse Consulting

An Oracle White Paper November Backup and Recovery with Oracle s Sun ZFS Storage Appliances and Oracle Recovery Manager

IT CHANGE MANAGEMENT & THE ORACLE EXADATA DATABASE MACHINE

Understanding the Value of In-Memory in the IT Landscape

IBM Sales and Distribution IBM and Manhattan Associates

Next Generation Data Warehousing Appliances

IBM PureApplication System for IBM WebSphere Application Server workloads

RapidDecision EDW: THE BETTER WAY TO DATA WAREHOUSE

SAS and Oracle: Big Data and Cloud Partnering Innovation Targets the Third Platform

Upgrading to Microsoft SQL Server 2008 R2 from Microsoft SQL Server 2008, SQL Server 2005, and SQL Server 2000

Cisco for SAP HANA Scale-Out Solution on Cisco UCS with NetApp Storage

!!!!! White Paper. Understanding The Role of Data Governance To Support A Self-Service Environment. Sponsored by

SAP HANA - an inflection point

Enterprise Application Performance Management: An End-to-End Perspective

Cisco Business Intelligence Appliance for SAP

Parallel Data Warehouse

Networking Modernize. Open Your Network to Innovation

Business Intelligence

EMC s Enterprise Hadoop Solution. By Julie Lockner, Senior Analyst, and Terri McClure, Senior Analyst

The Ultimate Guide to Buying Business Analytics

In-Memory Analytics for Big Data

Navigating the Enterprise Database Selection Process: A Comparison of RDMS Acquisition Costs Abstract

DEMAND SMARTER, FASTER, EASIER BUSINESS INTELLIGENCE

ENABLING OPERATIONAL BI

Colgate-Palmolive selects SAP HANA to improve the speed of business analytics with IBM and SAP

IBM PureFlex System. The infrastructure system with integrated expertise

Affordable, Scalable, Reliable OLTP in a Cloud and Big Data World: IBM DB2 purescale

IBM Netezza High-performance business intelligence and advanced analytics for the enterprise. The analytics conundrum

SAP integration and management services

Analyzing Big Data with Splunk A Cost Effective Storage Architecture and Solution

Transcription:

infrastructure (WX 2 and blade server Kognitio provides solutions to business problems that require acquisition, rationalization and analysis of large and/or complex data

The Kognitio Technology and Data Center has been at the forefront of the design and development of massively parallel technologies for more than fifteen years. The center has over a hundred man-years of development experience and is unique in its latest development of Kognitio WX 2, a high performance analytical database that serves as a robust data warehouse platform Contents Challenge 3 New Trends 4 The Data Warehouse Appliance 4 The Warehouse Appliance 5 Total Cost of Ownership: The Key Differentiator 6 Summary 7 Page 2

Computer scientists have spent decades considering the problem of how to effectively process ever-increasing amounts of data The Challenge Computer scientists have spent decades considering the problem of how to process ever-increasing amounts of data effectively in less time. The answer has always been somewhat like the answer to how do you would eat an elephant quickly? In other words, overcome the big problem (a huge amount of data to analyze in a short amount of time) by breaking it down to a large number of small jobs running in parallel. For example, assume you have to mow an acre of grass. If you have 10 people and 10 lawnmowers attacking the problem, you accomplish the job in one-tenth of the time. This is essentially the theory behind what is known as massively parallel processing (MPP), and it is how today s largest data warehouses handle the increasing volume of corporate data along with the increasing amount of analytics run using it. While this sounds relatively simple and straightforward, massively parallel computing infrastructures can be expensive to buy and complex to manage. To make matters worse, organizations of all sizes are becoming increasingly dependent on data analytics to assist in decision-making and optimal growth. Indeed, since 2000, data warehouse/business analytic infrastructures have become business-critical applications at companies. Organizations have always searched for better ways to understand their customers, supply chains, processes and value networks, and anticipate their needs. They have longed to improve the speed and accuracy of operational decision-making. In short, they want to know all the secrets hidden within the massive amounts of ever-increasing data volumes. While the desire to improve the analysis and timeliness of an organization s data has been prevalent for more than 20 years, the practical capability to do so has eluded all but the largest IT shops. Kognitio Limited 2008 Page 3

End-users are demanding a solution that is less complex, less risky and easier to implement and maintain New trends However, powerful trends have impacted the data warehousing space in the past several years. These trends are creating a convergence of an organization s historical desire to derive value from the data, with the opportunity and, more importantly, the capability to address the growing demand for business analytics with a simpler, cost effective approach. Now that most IT organizations have implemented the large ERP packages and have web-enabled most key customer applications, the focus has moved toward data warehousing and analytics. Technology innovation continues to drive down the costs associated with server processing power (Moore s Law) and storage capacity. Software licensing costs are beginning to be impacted by these trends, in addition to the growing influence that open source software is having on commercial software licensing and pricing. Greater computing capacity at a lower cost offers an opportunity to redefine what big signifies with respect to an analytic data warehouse or data mart. Multi-terabyte sized analytic stores are becoming the norm, not the exception. Processing power is becoming less expensive, but organizations are chewing up that capacity as fast as it is available. By continuing to innovate how data is used, or even creating new classifications of data (such as sub-transactional data), organizations will continue to stress traditional analytic infrastructure. So how can the complexity issue be addressed? The Data Warehouse Appliance Since the concept of the data warehouse was first introduced, end-users have demanded a solution that is less complex, less risky and easier to implement and maintain. Many end-users wish they could simply purchase a data warehouse the way they purchase a payroll application. Unfortunately, business analytic needs are constantly evolving, making productization of the warehouse difficult. Even the word evolving is inaccurate, as it implies constant but slow-moving change. The reality is that analytic needs within an organization continually change and evolve very rapidly. Additionally, demands for immediate tactical analysis (versus longer-term strategic analysis) make traditional analytic infrastructures inherently complex. Page 4

Innovative vendors are now emerging to attack warehouse complexity by taking advantage of many of the previously mentioned trends in industry standard hardware and software. While delivering a packaged data warehouse may be a little more difficult, marts and complexity can be addressed through the productization of a warehouse or data mart s underlying infrastructure. The Warehouse Appliance The virtual data warehouse appliance dispenses with the need for proprietary hardware and combines the price/performance of Intel-based processors, industry standard, open source software and low-cost disk storage in a single cabinet. The combination of Kognitio WX 2 and blade servers (IBM, HP, etc.) is purpose-built to handle analysis against terabytes of data quickly and simply. By using a number of blades, which can easily be added to deliver linear scalability or on-demand computing, these virtual data warehouse appliances are uniquely designed to eat the elephant that is a multi-terabyte analytic data store. The market for virtual data warehouse appliances is growing quickly. Kognitio is the pioneering vendor leading the virtual data warehouse appliance software trend running on industry standard blades. The Kognitio WX 2 system scales from less than 100GB of user data up to 50TB and more. Other, proprietary vendors are already rushing to market with similar proprietary hardware-based solutions, and some users are buying proprietary with its inherent risks. This is most likely because they are not aware of the better performance and TCO of blade-based solutions. But why are large companies willing to invest in such a new trend? Kognitio Limited 2008 Page 5

IT organizations must look for the proper tools to address the fast-changing needs of their business-user clientele Total Cost of Ownership: The Key Differentiator Total cost of ownership (TCO) is a major issue at the forefront of virtually every IT organization today. However, defining what TCO actually equates to can be ambiguous at times for many organizations. We define it as the initial purchase price for the solution in addition to how long it takes for the vendor to deliver an acceptable working production environment. Then we add the cost of maintaining or sustaining a well-performing stable environment. It is this third piece that often comprises as much as 80% of the TCO for an application. This portion consists primarily of personnel costs to monitor and tune the system. Since virtual appliances are using off-the-shelf standard blade server hardware to address large analytic workloads and scale incrementally, one blade at a time, the time-to-value piece of the TCO equation is rather simple. Time-to-value is an extremely important metric because it directly drives an organization s return on investment (ROI) for the warehouse or mart environment. Some early adopters of the Kognitio WX 2 solution have reported provisioning times of less than four hours to produce a working sustainable analytic environment - compared to four weeks or more with an Oracle/Sun/EMC 2 infrastructure to do the same thing. More importantly, performance was as much as 60 times faster on the virtual data appliance. WX 2 Platform & Architecture General Purpose Platform Software Deployment Other apps. e.g. SAS, BO Web WX2 Virtual Appliance Memory 10010101001 10010101001 10010101001 Processors Disks Blade Server WX 2 VIRTUAL APPLIANCE Multiple Parallel Data Feeds SQL Query Results SQL Query Results SQL Query Results Multiple Concurrent Query Streams Page 6 Server Farm

The virtual data warehouse appliance also surpasses its traditional warehouse infrastructure brethren in the area of maintenance. Appliances are load and go environments. Since they process the data efficiently with a high disk-to-processor ratio, creating a massively parallel query engine in a box, they do not require indexing. More importantly, they do not require any specific physical database design or help to make the database optimizer use indexes designed so painstakingly by a DBA. The result is that organizations spend the bulk of their time actually querying data, not tuning the database to query the data. They get rapid results and insight much quicker than ever before. What a concept! Summary With the demand for data analysis increasing, IT organizations must find the proper tools to address the fast-changing needs of their business-user clientele. While the data warehouse appliance may not be the same as a data warehouse in a box (or cabinet), it does simplify the underlying analytic infrastructure. While no tool yet addresses the needs of the entire spectrum of analytic needs, the data warehouse appliance model is sure to be an option that most IT organizations will want in their analytic toolbox. Complexity of Design Reduced by 70% Time to Implement Reduced by 60% Time to Gain ROI Reduced by 85% Requirement for Bulk Storage Reduced by 80% Ongoing Administration Reduced by 75% Complex Query Throughput Increased by 2500% Data Warehouse Database using MPP technology (Kognitio WX 2 ) Traditional Data Warehouse Database Virtual data warehouse appliances offer considerable advantages in terms of time, speed and ease of use Kognitio Limited 2008 Page 7

About Kognitio Kognitio is an innovative, technology-rich company, providing leading-edge solutions to business problems that require the acquisition, rationalization and analysis of large or complex data. Kognitio s offering is centred around three areas: WX 2, the fastest and most scalable analytical database on the market, DaaS and data migration expertise. All three areas are complemented by an extensive professional services team helping businesses to gain a competitive advantage from their data. With its industry-leading analytical database offering, WX 2, Kognitio is able to rapidly turn a company s raw data into valuable business insight, empowering its customers to realize comprehensive answers to critical business questions. Kognitio s DaaS model allows its customers to focus on running their businesses and increasing their bottom line. By also adopting Kognitio s outsourced approach, customers are able to reduce start-up time and costs, as well as avoid expensive product acquisition costs. With a strong specialization in the insurance (life and pensions) as well as financial services, Kognitio s data migration services help companies to provide lower risk and lower cost solutions to companies that are rationalizing and consolidating operational platforms. Kognitio delivers competitive advantage to clients in various industries, including telecommunications, retail, the financial sector, leisure, hospitality and utilities. About Kognitio WX2 Kognitio WX 2 is the most powerful and scalable analytical database in the industry. It enables organizations to query, in detail, vast amounts of granular data in seconds. The software-only solution uses high-speed, Massively Parallel Processing (MPP) technology to deliver an extremely fast data mart/warehouse platform to organizations seeking to gain intelligence from their data. Kognitio WX 2 runs on low-cost non-proprietary, industry-standard hardware, does not use indices or data partitions and can be scaled to handle hundreds of terabytes of data with performance that delivers answers in real time. This technology delivers the most comprehensive, cost-effective Business Intelligence database platform in the industry and offers delivery mechanisms from pure product through to fully managed services. More than fifteen years of development have been focused on providing and refining the best tool for corporate Business Intelligence users to freely engage with ever-increasing volumes of data and/or disparate sets of data. Kognitio WX 2 enables business to work its data harder: to take more benefit out; in shorter times scales; with considerably less effort; and without the need for a complex large-scale IT installation. Tests have shown Kognitio WX 2 to run up to 60 times faster than typical databases and at a lower cost of ownership when compared to the lifecycle cost of other solutions. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording or any information storage and retrieval system, without prior permission in writing from the publisher.