NoSQL databases are a popular alternative to relational database systems. They offer new access methods, data models and are made for "Big Data". Typically, access methods in these systems are very simple, and complex queries with aggregations and joins are not supported. That is why frameworks like Hadoop MapReduce, Spark or Flink are called into action. But using these frameworks involve a lot of coding effort, and they are not usable for people without coding experience.

The language NotaQL has three aims:

• An easy-to-learn concise language to define data transformations in a few lines of code. These also include transformations on flexible schemas and complex data types.
• NotaQL allows for cross-system transformations by supporting input and output engines for various database systems, file formats, and services with different data models.
• Transformations in NotaQL can be executed in different ways. Classical approaches iterate over the whole input data, transform, aggregate and store the result into the target system. Incremental approaches can reuse the result of a former computation and only process the changed data.

NotaQL can be used for different applications like data integration, migration, polyglot persistence, and data analytics. A transformation platform allows for a distributed execution of NotaQL transformation in different incremental and non-incremental ways.