Our mission is to empower data analysts and enable them to derive insight from massive, heterogeneous, and fast-evolving data sets.
Our key areas of activity include scalable data management systems and tools, data mining, and open data/information marketplaces.
The research department lead by Prof. Dr. Markl is in close cooperation with the Database Systems and Information Management Group (DIMA) at TU Berlin and participates in the Berlin Big Data Center (BBDC).
The BBDC, funded by the Federal Ministry of Education and Research (BMBF), is a competence center for big data under the management of TU Berlin.
“Apache Flink”  is a stream-processing framework for distributed, high-performing, always-available, and accurate data streaming applications. It originated from the joined research project "Stratosphere" , funded by the Deutsche Forschungsgemeinschaft (DFG). After a successful incubator phase, Flink graduated to a top-level project of the Apache Foundation  and became one of the most important and promising projects within the Apache Big Data Stack. Flink has a big and lively community, numerous well-known users, such as Zalando, Alibaba, and Netflix, and features it's own annually conference “FlinkForward”  taking place in Berlin and San Francisco.
The Myriad Toolkit facilitates the specification of scalable data generation programs with complex statistical constraints via a special XML data generator prototyping language.
The Myriad Toolkit uses advanced PRNG algorithms to implement offset-based access to the elements of the generated domain type sequences within a bounded time. This feature facilitates an efficient data-parallel execution mode. Data generation programs created with the Myriad Toolkit therefore can be scaled-out in a massively parallel manner in order to quickly generate large synthetic datatets with complex statistical dependencies.
Peel is a framework that helps you to define, execute, analyze, and share experiments for distributed systems and algorithms. A Peel package bundles together the configuration data, datasets, and workload applications required for the execution of a particular collection of experiments. Peel bundles can be largely decoupled from the underlying operational environment and easily migrated and reproduced to new environments.