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. Microsoft completed its acquisition of the business social network LinkedIn towards the end of 2016.
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 refers to Microsoft SQL Server as an analytical relational database.
Over the time, the SQL Server for BI and advanced analytics has expanded greatly in terms of performance, data support and functionality. An example for this is the in-memory processing, the extension of the engine by JSON functions for the processing of unstructured data or the integration of R for advanced analytics. In addition to the SQL Server, Microsoft has released other products that focus on the development of highly scalable solutions, e.g. Analytics Platform System incl. Parallel Data Warehouse or an Hadoop distribution with HDInsights. 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 for those options are the cloud-based services Azures 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. 33 percent of participants also confirm its use in large companies. SQL Server can usually be found in local BI applications, as a mart or in additional Microsoft data management products. 75 percent of those surveyed use SQL Server for data integration. This might seem strange at first. Here are two explanatory attempts: 1. Users are probably increasingly using the supplied Integration Services (SSIS) for data preparation and count these as the SQL Server. They use SQL Server as a “stage” to integrate data before it is prepared for analysis. More than 50 percent of participants use it as a data warehouse or data mart, as well as for data warehouse automation. For automation, database administrators and developers can orchestrate SSIS packages in a workflow so that loads are executed automatically. We believe that data warehouse automation is much more than that and therefore needs to be differentiated. We do not see any native SQL Server functions that allow to react to changes in the domain-oriented data model (top-down) or in the sources (bottom-up) and to automatically adjust the ETL routes.
Percentage of employees using Microsoft SQL Server
Number of Product Users
Company size (number of employees)