For any data driven development, engineers and data scientists spend 80% of their time in data wrangling. Momentum Connect helps automate this process so as to improve the productivity of all stakeholders. You can speed up the data wrangling process by ingesting, cleaning, blending, and transforming a wide variety of data formats from external systems at high speed and scale.

Momentum Connect consists of the following four components:

  1. Ingester
  2. Transformer
  3. Emitter
  4. Pipeline
Momentum Connect architecture


Ingester provides a set of connectors to pull data from a large number of systems. For example, you can connect and ingest data at large scale from:
Ingester can ingest a wide variety of data formats:
Momentum provides a pluggable architecture to develop new connectors and attach to Ingester to be able to ingest data from systems that we do not currently support.


having accurate data is important. Therefore, data ingested from the source may require cleaning or correction. Moreover, you may require to blend data from different sources or transform them to be usable, meaningful and trustworthy. Most machine learning algorithms require data to be in certain formats. 

Transformer provides a UI driven approach to do data wrangling. Here is what you can do with the Transformer:


All data processed within Momentum are stored within a distributed file system. This allows us to create enterprise scale data warehouses. However, it may be needed to transmit the data from Momentum to external systems. Emitter is designed to do exactly that.


Automate data ingestion and transformation using Pipeline. Here is what you can do with Pipeline: