
Databricks, and Talend (NASDAQ: TLND), announced a strategic partnership that includes the integration of their products to enable data engineers to more easily perform data integration at large scale.
“As part of our cloud data platform strategy, we rely on Databricks and Talend to help us run data engineering projects at huge scale using the best Spark analytics engine and enable more data engineers to take advantage of these features through an easy to use interface.” said Rene Greiner, Vice President, Enterprise Data Integration, Uniper, a global energy company. “High velocity data from our sensors and enterprise systems are used to help us make real-time decisions on maintenance, monitoring and asset management. This modern approach enables us to break down silos and better monetize data to help run our energy operations more efficiently.”
Integration between Talend Cloud and Databricks’ Unified Analytics Platform enables data engineers to perform data processing at large-scale using the powerful Apache Spark platform. Through this integration, users can access the scale and cloud benefits through a drag and drop interface, instead of manually coding data engineering jobs. Talend Cloud is integrated with both Azure Databricks and Databricks for AWS.
“We are proud to be teaming with the leading cloud data processing platform in the market,” said Michael Pickett, Senior Vice President, Corporate and Business Development, Talend. “Working closely with Databricks, our joint customers can achieve higher performance and faster innovation by using Talend Cloud to move workloads to Databricks.”
Databricks’ Unified Analytics Platform provides a cloud-based service capable of running all analytics in one place – from highly reliable and performant data pipelines to state-of-the-art machine learning – at an unprecedented scale. With capabilities like auto-config and auto-scaling, Databricks’ Unified Analytics Platform addresses challenges with operations and operational costs head-on by spinning up and down clusters as needed.
“Running data engineering projects at scale while keeping total cost of ownership low continues to be a struggle for organizations looking to get machine learning models to the last mile,” said Michael Hoff, Senior Vice President, Business Development at Databricks. “With Talend Cloud’s embedded data quality components, our joint integration provides a single source of trusted data which is essential when data teams are working with machine learning algorithms. It has never been easier for joint customers to run massive data pipelines.”
Talend Cloud is a highly secure and scalable Integration Platform-as-a-Service (iPaaS) that helps organizations put more data to work by increasing its availability, quality, and value. Talend Cloud’s native, portable, and unified data platform enables companies to liberate their data, so everyone can trust it and use it to drive business value. Talend’s native support for Databricks creates a powerful solution to easily spin up and down a big data cluster in the cloud, ingest and process large volumes of data, and control costs by only paying for resources used. The integration enables the delivery of insight-ready data at scale and helps businesses innovate faster.


Archive
- January 2021(32)
- December 2020(53)
- November 2020(59)
- October 2020(79)
- September 2020(72)
- August 2020(64)
- July 2020(71)
- June 2020(74)
- May 2020(50)
- April 2020(71)
- March 2020(71)
- February 2020(58)
- January 2020(62)
- December 2019(57)
- November 2019(64)
- October 2019(26)
- September 2019(24)
- August 2019(15)
- July 2019(24)
- June 2019(55)
- May 2019(82)
- April 2019(77)
- March 2019(71)
- February 2019(67)
- January 2019(77)
- December 2018(46)
- November 2018(48)
- October 2018(76)
- September 2018(55)
- August 2018(63)
- July 2018(74)
- June 2018(64)
- May 2018(65)
- April 2018(76)
- March 2018(82)
- February 2018(65)
- January 2018(80)
- December 2017(71)
- November 2017(72)
- October 2017(75)
- September 2017(65)
- August 2017(97)
- July 2017(111)
- June 2017(87)
- May 2017(105)
- April 2017(113)
- March 2017(108)
- February 2017(112)
- January 2017(109)
- December 2016(110)
- November 2016(121)
- October 2016(111)
- September 2016(123)
- August 2016(169)
- July 2016(142)
- June 2016(152)
- May 2016(118)
- April 2016(60)
- March 2016(86)
- February 2016(154)
- January 2016(3)
- December 2015(150)