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. In recent years, Microsoft has focused its business on cloud-based solutions such as Azure. AI and machine learning have also become increasingly important in product development.
Compared to the huge business the company does in a range of different markets, its BI revenues are relatively small. Nevertheless, Microsoft is a strong presence in the BI market and its offering is strategic to complement existing solutions and to drive cloud revenues. In the past, the vendor spread its BI capabilities across the Office, SharePoint and SQL Server product lines, providing tools for formatted reporting, analysis and dashboards.
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 discusses 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 with 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., SQL Server Big Data Cluster). These tools use Microsoft SQL Server functions or can be closely integrated with it.
Data and analytics capabilities in the Azure cloud are currently strategic for Microsoft. 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 and Azure Data Warehouse for data storage, as well as non-relational engines such as the Azure Cosmos DB.
Scalable, performant, on-premises analytical systems such as Microsoft’s MPP database Analytics Platform Systems are either not available anymore or are much more expensive than equivalent cloud services. However, this review only covers Microsoft SQL Server.
User & Use Cases
Microsoft SQL Server is a functional all-round package for data management in BI, especially in medium-sized companies. 26 percent of participants using SQL Server are from large companies. SQL Server can usually be found as an enterprise data warehouse, 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 (65 percent) and as a data integration tool (62 percent) is not surprising. This is part of its core functionality. With its Master Data Management Services, SQL Server offers additional functionality used by 30 percent of respondents, which is unexpectedly high. 43 percent claim to use SQL Server for data preparation although we are not aware of any technical tool in the SQL Server package that directly covers this area. Usage of SQL Server for data warehouse automation also seems high at 22 percent. 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.
Total number of users per company
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Company size (number of employees)
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