Talend 6 – the first Spark-powered Data Integration Platform?

Talend has launched Talend 6, said to be the industry’s first and only data integration platform with native support for Apache Spark and Spark Streaming. By leveraging over 100 Spark components, Talend 6 delivers unmatched data processing speed and enables any company to convert streaming big data or IoT sensor information into immediately actionable insights.

  • 9 years ago Posted in
“Fast analytics absolutely requires fast data integration, so we are very happy to be first to market with native Spark and Spark Streaming support,” said Mike Tuchen, CEO, Talend. “We made a big bet on Spark, because we understand that in an increasingly competitive marketplace, every moment is material when it comes to business critical information.”
 
Whether companies are looking to increase revenue, improve business process or enhance customer service, Talend 6 provides the integration speed and velocity required for real-time analytics. As detailed in the latest Talend blog (www.talend.com/blog), support for Spark and real-time big data integration creates a range of dynamic new use cases across virtually every vertical market segment – from retail and healthcare to agriculture.
 
“We’ve worked with Talend for a number of years and have together helped many organisations easily bring data from various data sources into Cloudera,” said Tim Stevens, vice president, Corporate and Business Development, Cloudera. “We are excited to see Talend’s commitment to Spark and look forward to this next chapter in our relationship as we jointly introduce Spark to customers, not only to deliver a new level of ease and speed, but also to innovative on new data possibilities.”
 
Existing Talend customers can convert MapReduce jobs to Spark with the click of a button, resulting in an immediate 5x performance increase. Talend also anticipates the new platform will improve developer productivity 10x as compared to hand-coding thanks to its intuitive design interface and prebuilt Spark components with automated ‘no coding required’ Spark code generation. In order to further streamline support for all project types, Talend provides a new, built-in Lambda Architecture offering a single environment for working with bulk and batch, real-time, streaming and IoT data.
 
“We believe Apache Spark has an opportunity to become the default in-memory engine for high performance data integration and analytics,” said Matt Aslett, research director, data platforms and analytics, 451 Research. “Building on its existing capabilities for in-Hadoop MapReduce processing with early native Spark and Spark Streaming support, Talend is positioned to capitalise on the demand for real-time analytics.” 

"This is an exciting time in the evolution of Spark as more leaders like Talend adopt the engine and help drive it into the mainstream,” said John Tripier, head of business development at Databricks. “For years large web companies have been turning real-time data management and analytics into a major competitive advantage, with Apache Spark and intuitive integration platforms like Talend 6, the playing field is about to get a lot more even.”
 
In addition to support for Spark, the new Talend 6 platform is packed with a broad array of additional enterprise-class features and enhancements including:
 
  • Support for Continuous Delivery: A development approach that helps IT meet the need for speed of business by enabling integration development, testing, deployment and operations to work together as one streamlined delivery team. The automated build, test and release process lowers risk while increasing agility and innovation.
  • Master Data Management (MDM) REST API and Query Language Support: This feature makes it easier to embed a 360-degree view of the customer into web and mobile applications.  This means that customer insight gleaned from both traditional and new big data sources like web logs, social media, mobile and SaaS applications can be leveraged in real-time. 
  • Data Masking and Semantic Analytics: Allows organisations to better manage compliance and privacy constraints by easing the process of concealing and securing personal data as it is shared throughout the enterprise. This protects customer data from abuse or breaches, while the semantic analyser enables business users to better understand their data with data-type auto-discovery and HDFS file profiling.
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