Data Science Data Pipelining

 

Data Scientists biggest gripes:

Preparing and cleaning data

Integrating sources together

Making sure we are using valid data

Having a scalable platform to build models on

Zetaris steps in providing a platform that gives data scientists the ability to:

Reduce data preparation cycles, through reusable real-time pipelines with built in data quality mechanisms

Access a single-pane-of-glass for data access, relieving integration issues

Scale workload's with a high-performing query engine, designed for Data Science work