Allows Organizations to Maintain Data Integrity and Governance as Data Volume and Access Increase
Redwood City, CA - 21 aprile 2016

Talend, a global leader in big data integration software, today announced that Talend Big Data Integration now supports Cloudera Navigator. This integration enables end-to-end data governance on Apache Hadoop, including field-level data lineage and impact analysis.

Today, an increasing number of organizations are subject to government regulations or greater internal data controls. Talend’s integration with Cloudera Navigator enables these companies to monitor, audit and understand data access on Apache Hadoop just as they do on traditional data warehouses, without compromising agility.

“Companies face many trust and compliance challenges as they collect and connect greater data volumes and provide data access to more individuals throughout the organization,” said Tim Stevens, vice president, corporate and business development, Cloudera. “This is why Talend Big Data Integration and Cloudera Navigator make a strong combination, enabling the fine-grained data governance necessary for turning data into a trusted source that helps precisely track data movement, transformation, mapping and filtering.”

With Talend, Cloudera Navigator users gain a holistic view of their data assets in Hadoop and benefit from Talend’s capability of combining of Spark, Spark Streaming and Machine Learning for integration. Talend Big Data Integration generates optimized code for Spark – including MLlib and Spark Streaming – and also synchronizes its metadata with Cloudera Navigator, enabling organizations to gain industry-leading governance capabilities.

“Hadoop is rapidly entering a phase of mainstream adoption that requires the type of comprehensive, enterprise-class data governance and control that Cloudera Navigator and Talend can provide,” said Ciaran Dynes, vice president of products, Talend. “Together, we’re meeting the needs of the most stringent governance requirements by showing not only what happened at the data input and output level, but also at a very granular level of detail, including joins, filters and maps, right down to the field level of the data.”

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