Trifacta Launches the Data Engineering Cloud for Snowflake

Trifacta, the Data Engineering Cloud company, today announced advanced Pushdown Optimization capabilities for Snowflake. This innovation enables faster data transformation and emphasizes Trifacta’s leadership in data engineering to deliver consumable data for analytics and machine learning. Further, Trifacta’s native support for Snowflake SQL uniquely allows data professionals to build the entire transformation logic, allowing users of all skill sets to work on huge data directly within Snowflake’s Data Cloud.

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“We’re thrilled to see Trifacta continue to innovate with their pushdown optimization on Snowflake’s platform”

“Our research shows three-quarters of organizations use or plan to use the cloud for data and analytics. As cloud data warehousing and ELT approaches continue to grow, speed of execution and ease of use are critical for data professionals looking to transform large data sets for downstream analytics and machine learning,” said Matt Aslett, Research Director at Ventana Research. “Trifacta’s Pushdown Optimization for Snowflake comes at a critical time and solidifies the company’s position as a key innovator in data engineering.”

With data architectures transitioning to modern ELT (Extract, Load, Transform) approaches, modern cloud data solutions are becoming key to loading the data before the transformation. Pushdown optimization techniques help data workers convert transformation steps into SQL scripts. Alternatively, data engineers who like to leverage code can complement their technical expertise with advanced and more complex data transformations using their own code.

“We’re thrilled to see Trifacta continue to innovate with their pushdown optimization on Snowflake’s platform,” said Tarik Dwiek, Head of Technology Alliances at Snowflake. “This allows our customers to embrace both no-code and code-based options to interact directly with their data using Snowflake tables in near real-time, enabling faster data transformations than with traditional approaches.”

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“We’re excited to offer Trifacta’s visual and interactive solution within Snowflake for data workers to explore raw data and to ensure data quality,” said Sean Kandel, Co-founder and CTO at Trifacta. “Trifacta’s rich, intuitive interface provides previews of recommended transformations in real-time for analysts to review before committing the changes. With pushdown optimization for Snowflake, we are expanding our data engineering capabilities for users to run their recipes natively easily and quickly, reducing transformation time from hours to minutes.”

“Timely availability of subscription and product usage dashboard is key to business decision makings,” said Yasuko Hirao, Autodesk. “As we transition towards modern data pipeline approaches such as ELT with pushdown optimization, we’re excited to adopt these capabilities from Trifacta and achieve efficiency for our analytics.”

Trifacta’s Pushdown Optimization with Snowflake offers automatic SQL generation for data transformation at the required speed, scale, and quality. Key benefits include:

Increased Productivity

Pushdown Optimization with Snowflake offers faster data transformations directly on Snowflake’s platform across entire Snowflake tables. This eliminates the need to write complex SQL statements or code allowing data users to focus on the data, increasing their productivity by over 2x.

Quicker Data Transformations with High-Quality

Trifacta offers faster data transformations by leveraging the power of Snowflake’s platform. With Pushdown Optimization, data loading and data transformation happen simultaneously, delivering high-quality data, providing organizations and data professionals with the required insights for analytics and machine learning.

Scale of Operations

Trifacta’s support of Snowflake leverages the scale of the cloud enabling data transformations across any scale of data. From a few MB of data to exabytes of data, Trifacta provides intelligent data transformations helping data professionals understand their data at the most granular level, with the desired scale.