Thursday, August 18, 2022
Tech ObserverEnterprise ITData CenterDatabricks and Informatica join hands to sail through hybrid data

Databricks and Informatica join hands to sail through hybrid data

and Informatic have partnered to introduced product integrations that can provide efficient data ingestion, simplified creation of high-volume data pipelines, and integrated data governance for data discovery and end-to-end lineage.

The company said that data engineering and data science teams depend on many sources that make finding the right datasets and tracing the lineage of data through pipeline processing impossible. Bringing the capabilities for discovery, lineage, ingestion and preparation together with Databricks’ Unified Analytics Platform provides an analytics solution for intelligent data pipelines that leverages the correct datasets and provides end-to-end data lineage for analytics and machine learning implementations.

Informatica also announced support for Delta Lake, the new open source project from Databricks, to provide an analytics-ready place to store massive amounts of data. Delta Lake provides ACID transactions and schema enforcement that brings reliability at scale to data lakes and makes high-quality datasets ready for downstream analytics.

“This seamless integration between Databricks and Informatica enables data engineers to easily discover the right datasets and ingest high volumes of data from multiple sources into Delta Lakes,” said Ali Ghodsi, co-founder and CEO, Databricks.

“This means joint customers can use the reliability and performance at scale from Databricks to make data ready for analytics and machine learning – and get intelligent governance to find, track and audit that data from end to end.”

“Trusted, high-quality data and efficient use of data users’ time are critical success factors for analytics and data science projects,” said Chakravarthy, CEO, Informatica.

“Informatica’s support for Databricks allows data engineers to rapidly build serverless pipelines to ingest and govern data from a variety of sources at scale while empowering data scientists using Databricks to quickly find and prepare the data for their analytics and data science projects in a self-service fashion.”

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