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Analytical solutions with Microsoft SQL Server

Use the functions of Microsoft SQL Server to build an analytical solution. We will be happy to advise you.Get in touch with us
What is Microsoft SQL Server?

Microsoft SQL Server is the central building block of a Microsoft-centric analytical platform, which is built on relational database technology at its core. 

In addition to special functions for analytical data processing within the RDBMS, these are:

  • SQL Server Integration Services (SSIS): data integration solution specifically tailored for use with SQL Server.
  • SQL Server Analysis Services (SSAS):
  • Supports the so-called "Tabular" and "Multidimensional" mode, i.e. modeling analytical data relationally/in tables or multidimensionally/in cubes
  • SQL Server Reporting Services (SSRS): the predecessor to PowerBI
Why should Microsoft SQL Server be used?

SQL Server 2019 offers companies recognized good and extensive functions for building an analytical solution.

Both data integration, storage, analysis and reporting can be realized, and through the tight integration of PowerBI, extensive visualizations can be created and data can be given to consumers. Extensions around Big Data, to the cloud and more are possible. 

A selection of our customer projects

We have already used Microsoft SQL Server in numerous customer projects.

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The widespread use and performance of SQL Server prompted us to enter into a strategic partnership with Microsoft years ago. Numerous data warehouse projects testify to a successful partnership, now with Gold Partner status and numerous certifications.

We develop analytical solutions in which SQL Server is a central component. 

Features that distinguish the Microsoft SQL Server:

Optimized for speed, scalability and real-time analytics:

  • Clustered Columstore Indices
  • Disk-based and memory-optimized tables
  • In-memory OLTP processing
  • Intelligent Query Processing
  • Automatic Tuning

Funktionen rund um Enterprise Security:

  • Datenentdeckung und Klassifizierung
  • Dynamische Datenmaskierung
  • Sicherheit auf Zeilenebene


  • High Availability
  • Hybrid Cloud: Integration on-premises and Cloud (Azure)
  • Deployment under Kubernetes
  • Data virtualization (Polybase)
  • Machine learning framework