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Talend, Cloudera Partner on Large-Scale Data Processing

Talend is marrying up its expertise in open source data management software with Cloudera's Hadoop-based services to announce a new technology partnership to integrate the two companies’ open source solutions. Aiming to address scalability issues for large-scale data-intensive distributed applications, this new joint effort is aligned to focus on simplified data transformation and processing.

Pooling technology resources in this way, Talend and Cloudera will attempt to give enterprises using the Hadoop software framework's data distribution capabilities working at a level to support thousands of nodes and petabytes worth of data.

Talend says its data integration environment makes it possible to create and execute Hadoop data transformation jobs directly with an intuitive drag-and-drop interface. At the same time, Cloudera's Distribution for Hadoop (CDH) is designed to run on inexpensive hardware thus avoiding the need for costly high-range servers. So in theory, running Cloudera's massively parallel MapReduce architecture to execute data integration processes across Talend's technology with native support for Hadoop, data clusters can be managed under this joint solution that accommodate for peak data volumes and complex transformations.

The companies say that this technology partnership demonstrates the power of the open source Hadoop platform in terms of compatibility, database import/export functions and Hive, the data warehouse infrastructure built on top of Hadoop.

Talend itself says that companies that need to extract data from applications can leverage Talend's connectors to extract it from disparate sources and load it into Hadoop using Cloudera's Distribution for Hadoop data-loading features.

"Customers use Cloudera's Distribution for Hadoop to filter through immense amounts of complex data," said Mike Olson, CEO of Cloudera. "The integration of Talend and Hadoop will simplify and accelerate this quest, enabling organizations of all sizes to tackle the largest and most complex data projects with ease."

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