Today data engineering and data science teams depend on many hybrid data sources that make finding the right datasets and tracing the lineage of data through pipeline processing impossible. Bringing the Informatica 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.
The Informatica and Databricks partnership introduces product integrations that allow faster development and complete governance for data engineering workloads:
- Informatica’s Cloud Data Integration and Databricks’ Unified Analytics Platform enable data teams to quickly ingest data directly into a managed data lake from hundreds of hybrid data sources.
- Informatica’s Big Data Management with Databricks’ Unified Analytics Platform allows data teams to easily create performant, scalable data pipelines for big data. Using Informatica’s visual drag and drop workflows, data teams can define their data pipelines to run on highly optimized Apache Spark™ clusters in Databricks to provide high performance at scale.
- Informatica’s Enterprise Data Catalog provides support for tracking data lineage of pipelines with Databricks’ Unified Analytics Platform, and makes Databricks tables available as part of the data catalog.
Informatica is also announcing 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.