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Some data warehouse projects do indeed fail to deliver the promised return on investment.
Yes, as well as some of the big data warehouse projects.
Data warehouse projects have special requirements for the physical architecture of the database system.
These requirements distinguish data warehouse projects from operational data stores and are often underestimated.
Similarly to business intelligence and data warehouse projects, the process of integrating data can be a time-consuming and complex task.
"Many data warehouse projects are delayed and over budget, and some are flat-out failures," says Joseph Wenig, managing partner for data warehouse solutions at NCR. "Companies grossly underestimate the difficulty of getting one up and running".
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Extraction, Transformation and Loading (ETL) represent a big portion of a data warehouse project.
The third effect of a data warehouse project is that one gets useful information out of the warehouse.
Building the ETL process is potentially one of the biggest tasks of building a warehouse; it is complex, time consuming, and consumes most of data warehouse project's implementation efforts, costs, and resources.
The second effect of a data warehouse project is that people look at the entire enterprise as an integrated process, instead of separate parts only vaguely related to each other.
Hive is an open source data warehouse project with an extension based on Hadoop cloud computing platform published by apache software foundation, thus it supports a wide of data types, various kinds of structured and unstructured data with complex and heterogeneous storage formats [11].
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