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Spark & Data Science

Spark is a distributed open source database and analytics technology that is suitable for both data storage and the creation of machine learning models.
Spark, like Hadoop, is a distributed cluster computing engine that can process very large amounts of data across multiple nodes. In contrast to Hadoop, data is processed in-memory in the Spark cluster, which means that in many cases there is a significant increase in performance.
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Machine Learning with Spark

MLlib contains various machine learning algorithms for use in your data science project.

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Interfaces to other Tools

Spark offers various interfaces to data science tools such as Python and can be controlled by them.

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Realtime Analytics

Spark Streaming enables real-time analytics and streaming to be implemented with the Spark Cluster.

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In-memory Calculations

Spark is often much faster than comparable tools due to the complete storage of data in memory.

Hadoop for your Projects

Spark for your Projects

In the data science area, Spark has made a name for itself particularly through the freely available Spark library MLlib, which can execute various machine learning models in parallel on the Spark Cluster. This is particularly relevant for many companies in the Big Data context.

We advise you on the setup and application of Spark Clusters as well as the development and application of machine learning models in the data science area.

  • Machine learning with Spark
We use the MLlib library to develop and validate machine learning models that can be estimated in parallel on the Spark Cluster.
  • Development of Spark Cluster
We support you in setting up and setting up your Spark Cluster and implementing first test projects.
  • Support with Spark
We support you continuously in developing your data science competence and developing analytics projects.