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Matillion Data Loader 2.0 now generally available

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The enterprise cloud data integration platform, Matillion, has announced the release of Matillion Data Loader 2.0. This update is intended to empower enterprises to simplify data integration as well as accelerate insights with a cloud-native, no-code experience.

Matillion Data Loader works to bring users a single unified experience across batch loading and real-time, log-based change data capture (CDC) pipelines, and a consumption based pricing model in order to assist customers in better managing their data integration costs. 

According to the company, this release helps to control the complexity of creating and managing data pipelines by simplifying and accelerating the data loading process for users.

This platform also offers users predictable pricing based on the number of rows loaded per month via batch or CDC pipelines as well as deep integration into Matillion ETL for pre-built transformations. 

“Using data at speed and at scale is a huge differentiator for enterprises. The ability to bring analytics-ready data to more people in the business, while maintaining security and governance, reduces friction and opens up the door to new insights,” said Ciaran Dynes, chief product officer at Matillion. “Matillion Data Loader is an easy-to-use, SaaS platform that accelerates data-led decisions to give a competitive edge to enterprises, no matter their industry.”

In addition, Matillion Data Loader’s CDC capabilities enable users to capture and replicate all change events in near real time. CDC is implemented through a hybrid SaaS architecture, allowing data to remain in the customer environment for end-to-end security.

The integration with Matillion ETL allows change data to be transformed and loaded into a data platform to be analyzed, enabling more opportunities for fraud detection, marketing personalization, ecommerce recommendations, compliance auditing, AI/ML modeling, and other use cases.

To download and get started using Matillion Data Loader 2.0, click here

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