In addition to extensive documentation, the accelerator included such components as a staging database, format files, processing scripts, DTS packages, and an empty SSAS cube. The OLAP services themselves also underwent a number of changes, with the introduction of new cube, dimension, and hierarchy types, as well as enhanced cube functionality.Īt the same time, Microsoft released the SQL Server Accelerator for Business Intelligence, which provided a template for creating BI solutions based on SQL Server technology. The data mining features were based on an open and extensible implementation of the OLE DB for Data Mining specification and included data mining algorithms developed by Microsoft Research. With the release of SQL Server 2000, Microsoft changed the name of OLAP Services to SQL Server Analysis Services (SSAS) and added data mining to help complete its analytical capabilities. In addition, SQL Server 7.0 introduced parallel execution for queries, breaking a query into subtasks and distributing them across multiple processors. SQL Server 7.0 also came with advanced query optimization aimed at addressing the types of queries typical to a data warehouse, such as a star query join with a large central fact table. Although not as elegant a tool as some would hope, it was a start, and it brought much needed attention to the importance and complexities of ETL operations and how integral they were to effective BI solutions. With DTS, data developers could pull data in from a myriad of data sources and easily transform the data, all with a few well-placed drag-and-drops and perhaps a bit of scripting. SQL Server 7.0 also shipped with Data Transformation Services (DTS), a processing engine with a drag-and-drop interface for performing the extract, transform, and load (ETL) operations often essential to a complete BI solution. Best of all for Microsoft, the concept of BI soon became inextricably tied to the SQL Server product, which continues to be the case today. Dubbed OLAP Services (codename Plato), the new technology not only brought sophisticated analytical capabilities to small and midsized business, but it also helped make BI a household name, ushering in a new era of data sense and sensibilities. What made this acquisition particularly noteworthy was not so much the fact that Microsoft had purchased yet another product, but rather that the company, two years later, rolled that product into its database management package with the release of SQL Server 7.0. In 1996, Microsoft acquired online analytical processing (OLAP) technology from Panorama Software Systems of Tel Aviv. Together the two operators helped to usher in an era of more complete and in-depth reporting. The ROLLUP operator added totals and subtotals to the aggregated data, and the CUBE operator added aggregated summaries across the various combinations of dimensional values. Perhaps the most notable of the 6.5 features to qualify for BI status were the new ROLLUP and CUBE operators in T-SQL, which you could use when grouping and aggregating data to include summarized information in your returned data. SQL Server 6.5 was, of course, not the first version of SQL Server (nor was it quite 6.5, if incremental numbering means anything), but it wasn’t until this version that we started to hear terms such as business intelligence and data warehousing spoken with any conviction in the presence of the SQL Server, young upstart that it was. Whenever Microsoft or its cronies bandy about the term BI, you can bet that SQL Server or one of its offspring is lurking in the background. Yet SQL Server remains at the heart of Microsoft’s BI drive, as it was in the beginning and continues to be through the pending release of SQL Server 2016. No longer seen as merely an adjunct to SQL Server, BI appears to have taken center stage, with SQL Server secondary to the greater BI initiative. Since the days of SQL Server 6.5, Microsoft has been trudging steadily into the world of business intelligence (BI), conquering territory and spreading the BI gospel far and wide.
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