Leverage an intuitive visual interface to easily prepare data at a massive scale for enterprise-wide analytics and machine learning initiatives. Ensure the reliability of results by validating the data for accuracy and consistency. Furthermore, perform various data operations, such as blend, curate, and wrangle for creating and scaling data pipelines with in-built connectors

Data Fusion Capabilities of NewgenONE Platform

Blend and Curate Data with Ease

Utilize multiple data sources, including, NoSQL database, relational database, BLOB, real-time queue, etc., using in-built connectors

Prepare data visually and apply joins across multiple data sets and sources

Prepare massive-scale data through distributed extract, transform, and load (ETL) capabilities

Save the prepared data in multiple sources, including Hadoop, BLOB, elastic search, etc.

Wrangle Data to Create and Schedule Data Pipelines

Perform various operations on data, including extraction, filter, transform, and group by filters

Leverage the existing capabilities to create new features

Schedule and run the complete data pipeline seamlessly on new datasets using the distributed compute

Handle Large-scale Data with Ease

Utilize in-memory distributed data virtualization to manage large-scale data instead of locally storing a copy

Prepare and process large-scale data as a horizontally scalable platform

Scale Data Pipelines with In-built Data Connectors

Save the prepared data in multiple sources, such as Hadoop, Blob, and other file systems

Integrate multiple data formats, including relational, NewSql, NoSql, etc.

Reuse visual data pipelines and share them further across the team and other users

Jump into the Conversation
icon-angle icon-bars icon-times