Splice Machine Partners with Talend to Streamline Data Workflows on Hadoop

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Provider of Hadoop RDBMS together with Talend’s integration solutions enables customers to power real-time, operational applications affordably, at scale

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Splice Machine, provider of the Hadoop RDBMS, today announced a partnership with Talend, a global leader in Big Data integration software, enabling customers to simplify data integration and streamline their data workflows on Hadoop.

A common struggle for data-driven enterprises is managing unnecessarily complicated data workflows with bloated ETL pipelines and a lack of native system integration. To solve these Big Data integration issues, Splice Machine and Talend have partnered to cohesively bring together an IT architecture to power real-time operational applications that are overwhelmed by the increase in data growth and the subsequent cost pressures.

Splice Machine leverages HBase and Hadoop in its scale-out architecture because of their proven auto-sharding replication and failover technology. The Company’s partnership with Talend gives businesses the best of all worlds: a standard ANSI SQL database, the proven scale-out of Hadoop, and the ability to leverage current staff, operations, and applications without specialized hardware or significant application rewrites.

“We are proud to partner with Talend to bring our customers the benefit of two different approaches for managing data integration affordably and at scale,” said Monte Zweben, co-founder and CEO, Splice Machine. “Talend’s rich capabilities drag and drop UI, and adaptable platform allow for increased productivity and streamlined testing for faster deployment of web, mobile, OLTP or IoT applications.”

Customers migrating from legacy databases to Splice Machine can continue using their existing Talend ETL process with minimal rewrites. To leverage the power of Hadoop’s parallel processing capabilities, customers can use Talend Big Data Integration to move data from source systems into HDFS, and perform transactions on that data in Hadoop, without hand coding. They then can use Splice Machine’s bulk loader to ingest the data from HDFS into the Hadoop RDBMS.

“Splice Machine is an innovative company that is showcasing the capabilities of Hadoop beyond data storage,” said Ashley Stirrup, Chief Marketing Officer, Talend. “We are pleased to be teaming with a like-minded company that is disrupting the status quo and providing another compelling reason for companies to upgrade from legacy data management solutions.”

Organizations that wish to build operational data lakes with Splice Machine can augment Talend’s data integration technology with its data quality capabilities. These tools can profile data, identity anomalies, cleanse, and monitor data quality on the Splice Machine database.

For more information about Splice Machine, please visit http://www.splicemachine.com.

About Splice Machine
Splice Machine’s Hadoop RDBMS is designed to scale real-time applications using commodity hardware without application rewrites. The Splice Machine database is a modern, scale-out alternative to traditional RDBMSs, such as Oracle®, MySQL™, IBM DB2® and Microsoft SQL Server®, that can deliver over a 10x improvement in price/performance. As a full-featured Hadoop RDBMS with ACID transactions, the Splice Machine database helps customers power real-time applications and operational analytics, especially as they approach Big Data scale.

About Talend
Talend’s integration solutions allow data-driven organizations to gain instant value from all their data. Through native support of modern big data platforms, Talend takes the complexity out of integration efforts and equips IT departments to be more responsive to the demands of the business, at a predictable cost. Based on open source technologies, Talend’s scalable, future-proof solutions address all existing and emerging integration requirements. Talend is privately-held and headquartered in Redwood City, CA. For more information, please visit http://www.talend.com and follow us on Twitter: @Talend.

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