Cloud data integration provider Matillion Ltd. today made its free data ingestion tool available with Snowflake Computing Inc.’s Partner Connect, with an aim to help businesses quickly and easily load critical business data from a wide range of sources to Snowflake’s popular cloud data warehouse service.

U.K.-based Matillion offers extract/transform/load services for cloud data warehouses such as Snowflake, Amazon Redshift and Google BigQuery. ETL refers to the three steps — extract, transform and load — that are used to prepare and blend data from multiple sources so it can be analyzed for business use.

Matillion said its enterprise customers often deal with multiple types and sources of data, and they need a way to centralize that information. A recent IDG survey found that data professionals report needing 400 different data sources to feed their business intelligence and analytics systems.

The Matillion Data Loader tool helps companies to easily load data into the Snowflake data warehouse from sources such as Microsoft SQL Server, Google Analytics, Salesforce and others. The tool helps provide a no-code experience for citizen data professionals and others who need to set up data pipelines easily.

Snowflake customers can access Matillion’s tool via Snowflake Partner Connect, and then begin loading data to Snowflake without leaving that platform.

The Data Loader works by itself as a lightweight solution to load data and works with Matillion ETL to join and transform data sources for analytics, the company said. Matillion said it will be available in Snowflake Partner Connect early in the third quarter of this year.

“Enterprises need their data readily available for analytics-driven insights more than ever before,” Matillion Chief Executive Officer Matthew Scullion said in a statement. “Including Matillion Data Loader in the Partner Connect program now means data teams and business stakeholders have easy access to a free, no-code solution that ingests data directly into Snowflake, supporting data professionals at any stage of their data journey across both simple and complex use cases.”

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