Microsoft SQL Server
Microsoft is the world’s largest software company. Founded in 1975 and headquartered in Redmond, it has become a household name, primarily due to its Windows operating system and Office suite. Aside from these products, Microsoft has a vast range of enterprise software and cloud offerings including its own database, browser, various servers and ERP solutions.
Microsoft’s strategic development in the BI area dates back to 1996 when it purchased OLAP technology from Panorama. In just a few years Microsoft moved from being a surprising entrant into the OLAP market to become the market share leader. Its main database technologies are bundled in SQL Server, which many companies use today to build data warehouses, data marts or even data lakes. This package includes data integration (Integration Services, also known as SSIS) as well as multidimensional and relational data management. Reporting Services (SSRS), a solution for formatted reporting, is also included with SQL Server. The SQL Server relational database supports the building of a relational, analytic data model (star or snowflake schema). Moreover, SQL Server offers functionality for building a relational (tabular) or multidimensional (OLAP) metadata layer in SSAS using SQL Server Data Tools. This review only refers to Microsoft SQL Server as an analytical relational database.
Over time, SQL Server for BI and advanced analytics has expanded greatly in terms of performance, data support and functionality. Examples of this include its in-memory processing, the extension of the engine by JSON functions for the processing of unstructured data and the integration of R for advanced analytics. In addition to SQL Server, Microsoft has released other products that focus on the development of highly scalable solutions (e.g., Analytics Platform System including Parallel Data Warehouse, a Hadoop distribution with HDInsight). These tools use functions of Microsoft SQL Server or can be closely integrated with it.
With Microsoft Azure, further innovative options are available (data storage and processing). Examples of those options include the cloud-based services Azure SQL Database, Azure Data Lake, Azure Data Warehouse for data storage, as well as non-relational engines such as the Azure Cosmos DB.
User & Use Cases
Microsoft SQL Server is a functional all-round package for data management in BI, especially in medium-sized companies. 28 percent of participants using SQL Server are from large companies. SQL Server can usually be found in local BI applications, as a mart or in additional Microsoft data management products.
The high level of use of SQL Server as a data warehouse (53 percent) and data integration tool (64 percent) is not surprising. This is part of its core functionality. With its Master Data Management Services, SQL Server also offers additional functionality used by 43 percent of respondents, which is unexpectedly high. Also a third of respondents use SQL Server for data preparation and data warehouse automation.
We are not aware of any technical tool in the SQL Server package that directly covers these areas. For automation for instance, database administrators and developers can orchestrate SSIS packages in a workflow so that loads are executed automatically. However, we believe that there is much more to data warehouse automation than that. We do not see any native SQL Server functions that can react to changes in the domain-oriented data model (top-down) or in the sources (bottom-up) and automatically adjust the ETL process.
Number of users using Microsoft SQL Server
Number of technical users using Microsoft SQL Server
Company size (number of employees)
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