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Oracle® Exadata and IBM® Netezza® Data Warehouse Appliance

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Oracle® Exadata and IBM® Netezza® Data Warehouse Appliance IBM Software Information Management Oracle® Exadata® and IBM® Netezza® Data Warehouse Appliance compared by Phil Francisco, Vice President, Product Management and Product Marketing, IBM and Mike Kearney, Senior Director, Product Marketing, IBM Information Management Oracle Exadata and IBM Netezza Data Warehouse Appliance compared Contents Introduction Introduction 2 IBM Netezza data warehouse appliances focus on technology designed to query and analyze big data. Online Transaction Processing (OLTP) “ Netezza was part of the inspiration for and data warehousing 3 IBM Netezza data warehouse appliances are disrupting the market. Wishing to exploit data at Query performance 5 Exadata. Teradata was part of the lower costs of operation and ownership, many of our Simplicity of operation 10 inspiration for Exadata. We’d like to customers have moved their data warehouses from Value 14 Oracle. Oracle has now brought Exadata to market, thank them for forcing our hand Conclusion 16 a machine which apparently does everything an IBM and forcing us to go into the Netezza data warehouse appliance does, and also hardware business.” processes online transactions. This examination of Exadata and IBM Netezza as data warehouse — Larry Ellison, January 2010 platforms is written from an unashamedly IBM viewpoint, however to ensure credibility we have taken advice from Philip Howard, Research Director of Bloor Research and Curt Monash, warehouses. More important, our customers create President, Monash Research. new business value by deploying analytic applications which they previously considered To innovate requires us to think and do things beyond their reach. differently, solving a problem using new approaches. IBM Netezza data warehouse appliances deliver “Netezza was part of the inspiration for Exadata. excellent performance for our customers’ Teradata was part of the inspiration for Exadata,” warehouse queries. IBM Netezza data warehouse acknowledged Larry Ellison on January 27, 2010. appliances offer customers simplicity; anyone with “We’d like to thank them for forcing our hand and basic knowledge of SQL and Linux has the skills forcing us go into the hardware business.”1 While needed to perform the few administrative tasks delivered with Larry Ellison’s customary pizzazz, required to maintain consistent service levels through dynamically changing workloads. IBM Netezza data warehouse appliances’ performance with simplicity 1 See http://oracle.com.edgesuite.net/ivt/4000/8104/9238/12652/ reduces their costs of owning and running their data lobby_external_flash_clean_480x360/default.htm 2 Information Management Oracle Exadata and IBM Netezza Data Warehouse Appliance compared there is a serious point to his comment: only the best All we ask of readers is that they do as our Online Transaction Processing catch Oracle’s attention. Exadata represents a customers and partners have done: put aside strategic direction for Oracle; adapting their OLTP notions of how a database management system (OLTP) and data warehousing database management system, partnering it with a should work, be open to new ways of thinking OLTP systems execute many short transactions. massively parallel storage system from Sun. Oracle and be prepared to do less, not more, to achieve Each transaction’s scope is small, limited to one or a launched Exadata V2 with the promise of extreme a better result. small number of records and is so predictable that performance for processing both online transactions often times data is cached. Although OLTP systems and analytic queries. Therefore, Oracle Exadata V2 is One caveat: The IBM Netezza data warehouse process large volumes of database queries, their a general purpose platform for managing mixed appliance team has no direct access to an Exadata focus is writing (UPDATE, INSERT and DELETE) to a workloads. Oracle Database was designed for OLTP. machine. We are fortunate in the detailed feedback current data set. These systems are typically specific But data warehousing and analytics make very we receive from many organizations that have to a business process or function, for example different demands of their software and hardware evaluated both technologies and selected IBM managing the current balance of a checking than OLTP. Quite simply, some workloads for data Netezza data warehouse appliances. Given Oracle’s account. Their data is commonly structured in third warehousing perform much better and are more cost size and their focus on Exadata, publicly available normal form (3NF). Transaction types of OLTP effective on a system that is purpose-built for information on Exadata is surprisingly scarce. systems are stable and their data requirements are analytics. Exadata’s data warehousing credentials The use cases quoted by Oracle provide little input well-understood, so secondary data structures such demand scrutiny, particularly with respect to to the discussion, which in itself is of concern to as indices can usefully locate records on disk, prior simplicity and value. several industry followers, e.g., Information Week.2 to their transfer to memory for processing. The information shared in this paper is made This eBook opens by reviewing differences between available in the spirit of openness. Any inaccuracies In comparison, data warehouse systems are processing online transactions and processing result from our mistakes, not an intent to mislead. characterized by predominantly heavy database read queries and analyses in a data warehouse. It then (SELECT) operations against a current and historical discusses Exadata and the IBM Netezza data data set. Whereas an OLTP operation accesses a warehouse appliance from perspectives of their small number of records, a data warehouse query query performance, simplicity of operation and value. might scan a table of billions of rows and join its records with those from multiple other tables. Furthermore, queries in a data warehouse are often so unpredictable in nature, it is difficult to exploit caching and indexing strategies. Choices for 2 See http://www.informationweek.com/news/business_intelligence/ warehouses/showArticle.jhtml? articleID=225702836&cid=RSSfeed_ structuring data in the warehouse range from IWK_News 3NF to dimensional models such as star and 3 Information Management Oracle Exadata and IBM Netezza Data Warehouse Appliance compared snowflake schemas. Data within each system In this century, corporations and public sector Organizations are redefining how they need and feeding a typical warehouse is structured to reflect agencies accept growth rates for data of 30-50 want to exploit their data; this eBook refers to this the needs of a specific business process. Before percent per year as normal. Technologies and development as the second-generation data data is loaded to the warehouse it is cleansed, practices successful in the world of OLTP prove less warehouse. These new warehouses, managing de-duplicated and integrated. and less applicable to data warehousing; the index massive data sets with ease, serve as the corporate as aid to data retrieval is a case in point. As the memory. When interrogated, they recall events This eBook divides data warehouses into either first- database system processes jobs to load data, it is recorded years previously; these distant memories or second-generation. While this classification may also busy updating its multiple indices. With large increase the accuracy of predictive analytic not stand the deepest scrutiny, it reflects how many data volumes this becomes a very slow process, applications. Constant trickle feeds are replacing of our customers talk about their evolutionary path to causing load jobs to overrun their allotted processing overnight batch loads, reducing latency between the generating greater and greater value from their data. window. Despite working long hours, the technical recording of an event and its analysis. Beyond the team misses service levels negotiated with the simple SQL used to populate reports and First-generation data warehouses are typically business. Productivity suffers as business units wait dashboards, the warehouse processes linear loaded overnight. They provide information to their for reports and data to become available. regressions, Naive Bayes and other mathematical business via a stable body of slowly evolving SQL- algorithms of advanced analytics. Noticing a sudden based reports and dashboards. As these simple spike in sales of a high-margin product at just five warehouses somewhat resemble OLTP systems – stores drives a retailer to understand what happened their workload and data requirements are Technologies and practices successful in the and why. This knowledge informs strategies to understood and stable – organizations often adopt promote similar sales activity at all 150 store the same database management products they use world of OLTP prove less and less locations. The computing system underpinning the for OLTP. With the product comes the practice: applicable to data warehousing… warehouse must be capable of managing these database administrators analyze each report’s data sudden surges in demand without disrupting regular requirements and build indices to accelerate data reports and dashboards. The business users are retrieval. Creep of OLTP’s technology and techniques demanding the freedom to exploit their data at appears a success, until data volumes in the the time and in the manner of their choosing. warehouse outstrip those commonly managed in Their appetite for immediacy leaves
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