Technologies

Sophisticated challenges can only be met with sophisticated technologies. That’s why we’ve built in-depth expertise in the following areas where our solutions are based:

  • Big Data

    Big Data describes the economically meaningful extraction and use of decision-relevant insights from diverse and differently structured sources of information, which are characterized by high dynamics of change and an unprecedented extent. Big Data provides methods, technologies and tools that translate the explosion of volumes of diverse information into sound and timely management decisions, making a key contribution to improving competitiveness.

  • NoSQL

    NoSQL (not only SQL) refers to a heterogeneous set of data management systems with different functions. These were created for use cases in which the available SQL-based databases reached their performance limits and therefore could hardly be used. Thus, NoSQL is intended not to be limited to SQL databases only and is therefore an addition to SQL.

  • In-Memory

    In-memory databases are memory-resident, so data analytics are applied directly to data held in random access memory (RAM). The memory offers significantly higher access speeds and simpler algorithms for access. For this reason, in-memory databases are much faster and their access times are more predictable than those of disk-accessing database management systems.

  • SQL

    SQL (Structured Query Language) is a database language for defining data structures in relational databases as well as for editing (inserting, modifying, deleting) and querying data bases based thereon. Through the use of SQL one strives for the independence of the applications from the used database management system. SQL is considered a classic in business intelligence, with which even very complex data analyzes can be programmed.

  • Hadoop, Spark & Co.

    We process large-scale unstructured data on Linux systems with the Apache Hadoop Framework, and implement machine learning with Apache Spark. We use Hive as the Data Warehouse and Pig and Spark for the analysis development. We use this open-source technology to turn sluggish Big Data Analysis into fast and accurate Small Data Analysis.

  • Visualization

    The visualization of data analysis should be professionally prepared and easy to understand. In addition to the open source programming languages R and Python, which have a variety of visualization capabilities, we also use QlikTech and MicroStrategy interactive visualization tools.

  • GPU Parallel Processing

    Highly complex analyzes can quickly reach even the limit of professional PCs and even modern server systems. We can move data processing from the CPU to many graphics cards or their GPUs (Graphic Processing Unit) and thus use very compute-intensive algorithms in parallel to large-volume data sets.