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Data Integration: The Code Generator Approach
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This white paper outlines the issues faced by BI operational staff in maintaining high quality of
BI information, and discusses technologies that have the potential to dramatically raise the
reliability and quality of BI information, improve how BI teams use their time and resources
to deliver rapid ROI and free resources to focus on answering business questions based on
reliable and meaningful data.
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- Customers are often required to buy dedicated servers to run/host the proprietary software – dramatically increasing the cost of implementation.
- When deploying large-scale data integration projects, performance degradations occur as a result of the centralized architecture – forcing customers to buy more costly hardware servers and software licenses.
- To describe the processing required, a proprietary language is typically used. This implicates an additional training and support cost on an ongoing basis.
- Data integration software vendors have to carry the high development costs linked to the direct support of a large number of hardware platforms, including multi-processor servers.
Customers frequently require a flexible and distributed architecture that traditional tools based on proprietary engines do not provide. IT teams want data integration software to adapt to their needs and the existing architecture.
The RDBMS Proprietary Approach
A few years ago, RDBMSs did not have the necessary functions required to perform complex transformation rules and could not efficiently join data sets. This is no longer true. Since the mid 90s, at the end of the marketing battle over compatibility between their SQL and ISO-89/ISO-92, RDBMS vendors have focused their engineering efforts on enhancing the functionality of their SQL language and improving the performance and reliability of their engines. For instance, in the last ten years, major RDBMS vendors have increased the number of SQL features available to programmers by a factor of ten to twenty. And RDBMS vendors now provide a long list of out-of-the-box utilities to enable impressive performance.
Indeed, most RDBMS vendors have started to leverage the power of the enhanced SQL and now include a data movement piece of software in the server package, seriously challenging the market owned by traditional (independent software) vendors. The data movement tools provided by RDBMS vendors have improved to the point where they could be described as “light ETL or data movement” software. Such tools use the RDBMS as an engine to perform transformations and aggregations. Their distributed approach: enables the execution of the job to take place on the sources, target or staging servers – wherever the processing is most efficient, given the IT architecture. Those “light ETL tools” are true SQL generators and prove that today’s SQL is sufficiently powerful enough to perform all the work needed to integrate data even when complex transformations are involved. They are limited, however, and their limitations are clearly linked to three factors, due to the inherent nature of these products:
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