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Databricks Open Sources Delta Lake for Data Lake Reliability

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Databricks, the leader in Unified Analytics and founded by the original creators of Apache Spark, announced a new open source project called Delta Lake to deliver reliability to data lakes. Delta Lake is the first production-ready open source technology to provide data lake reliability for both batch and streaming data. This new open source project will enable organizations to transform their existing messy data lakes into clean Delta Lakes with high quality data, thereby accelerating their data and machine learning initiatives.

“We’ve believed right from the onset that innovation happens in collaboration – not isolation. This belief led to the creation of the Spark project and MLflow. Delta Lake will foster a thriving community of developers collaborating to improve data lake reliability and accelerate machine learning initiatives”

Watch the Spark + AI Summit 2019 keynotes live.

While attractive as an initial sink for data, data lakes suffer from data reliability challenges. Unreliable data in data lakes prevents organizations from deriving business insights quickly and significantly slows down strategic machine learning initiatives. Data reliability challenges derive from failed writes, schema mismatches and data inconsistencies when mixing batch and streaming data, and supporting multiple writers and readers simultaneously.

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“Today, nearly every company has a data lake they are trying to gain insights from, but data lakes have proven to lack data reliability. Delta Lake has eliminated these challenges for hundreds of enterprises. By making Delta Lake open source, developers will be able to easily build reliable data lakes and turn them into ‘Delta Lakes’,” said Ali Ghodsi, cofounder and CEO at Databricks.

Delta Lake delivers reliability by managing transactions across streaming and batch data and across multiple simultaneous readers and writers. Delta Lakes can be easily plugged into any Apache Spark job as a data source, enabling organizations to gain data reliability with minimal change to their data architectures. With Delta Lake, organizations no longer need to spend resources building complex and fragile data pipelines to move data across systems. Instead, developers can have hundreds of applications reliably upload and query data at scale.

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With Delta Lake, developers will be able to undertake local development and debugging on their laptops to quickly develop data pipelines. They will be able to access earlier versions of their data for audits, rollbacks or reproducing machine learning experiments. They will also be able to convert their existing Parquet, a commonly used data format to store large datasets, files to Delta Lakes in-place, thus avoiding the need for substantial reading and rewriting.

The Delta Lake project can be found at delta.io and is under the permissive Apache 2.0 license. This technology is deployed in production by organizations such as Viacom, Edmunds, Riot Games and McGraw Hill.

“We’ve believed right from the onset that innovation happens in collaboration – not isolation. This belief led to the creation of the Spark project and MLflow. Delta Lake will foster a thriving community of developers collaborating to improve data lake reliability and accelerate machine learning initiatives,” added Ghodsi.

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