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Data Warehouse: Data Warehouse Architect
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A Data Warehouse is a separate database dedicated to decision support. Data is transferred in from transaction processing (OLTP) systems. It is accessed to provide management information through report writers, query tools, data access and retrieval tools, OLAP servers and enterprise information systems. A Data Warehouse is a software architecture, not a product.
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A Data Warehouse is not an individual repository product. Rather, it is an overall strategy, or process, for building decision support systems and a knowledge-based applications architecture and environment that supports both everyday tactical decision making and long-term business strategizing. The Data Warehouse environment positions a business to utilize an enterprise-wide data store to link information from diverse sources and make the information accessible for a variety of user purposes, most notably, strategic analysis. Business analysts must be able to use the Warehouse for such strategic purposes as trend identification, forecasting, competitive analysis, and targeted market research.
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The Operational Data Store area of the data warehouse is both a storage area and a set of processes commonly referred to as extract-transformation-load (ETL). The staging area is everything between the operational source systems and the data presentation area. The key architectural requirement for the data staging area is that it is off-limits to business users and does not provide query and presentation services. The only users who are allowed access to the ODS are select Data Managers and the DSS developers. The access to the ODS is to be used only for troubleshooting purposes or design of data structures needed for reporting.
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During the Architecture Review and Design stage, the logical Data Warehouse architecture is developed. The logical architecture is a configuration map of the necessary data stores that make up the Warehouse; it includes a central Enterprise Data Store, an optional Operational Data Store, one or more (optional) individual business area Data Marts, and one or more Metadata stores. In the metadata store(s) are two different kinds of metadata that catalog reference information about the primary data.
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FROM TERADATA TO NETEZZA, vendors of integrated systems designed to optimize data warehouse performance use a "shared nothing," disk-centric parallelization scheme. The query passes to a parsing engine or symmetric multiprocessing (SMP) front end, which optimizes it and breaks it into chunks. The parsing engine hands that work off to a massively parallel processing system. Multiple parallel processing units then retrieve data from direct- or network-attached disk drives, but each accesses its own dedicated data set. The answer sets are joined, and the result is presented to the requestor. In a Teradata system, the architecture includes SMP servers with proprietary interconnect hardware and EMC Fibre Channel SANs.
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Netezza is a fast-growing provider of enterprise-class data warehouse appliances that deliver breakthrough performance and ease-of-use at a fraction of the cost of traditional data warehouses. The Netezza Performance Server(R) (NPS(R)) system enables Fortune 1000 customers with terabytes of dynamic, detailed data to dramatically simplify even the most complex Business Intelligence (BI) initiatives. By architecturally integrating database, server and storage within a single appliance, the NPS system delivers 10 to 50 times the performance at half the cost of existing systems. The NPS appliance is being used by data-intensive organizations in telecommunications, financial services, retail, the life sciences, government and other markets to enable faster, more sophisticated analysis while allowing companies to leverage their existing infrastructure. Founded in 2000 and based in Framingham, Mass., the Company has raised more than $53M from leading venture capital firms, including Matrix Partners, Charles River Ventures, Battery Ventures, Orange Ventures and Sequoia Capital.
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Data Warehouse Architect