Thomson Reuters Uses Cask to Enable Rapid Time to Value From Big Data

Cask Data Application Platform (CDAP) Accelerates Delivery of New Content Platform


PALO ALTO, Calif., Aug. 09, 2017 (GLOBE NEWSWIRE) -- Cask, the company that makes building and deploying big data solutions easy, today announced that Thomson Reuters, the world’s leading source of news, data and information that power professional markets, has selected Cask Data Application Platform (CDAP) to augment development of its new big data content platform built on Cloudera CDH. By reducing the Hadoop learning curve, eliminating large amounts of manual coding, and simplifying deployment and operations of their solution, the Thomson Reuters team built and deployed new custom applications, at scale, faster with CDAP.

"Big data technologies such as Hadoop and Spark offer a great foundation to drive innovation for the news and information industry," said Sandy Martin, Senior Director, Enterprise Content Platform Engineering at Thomson Reuters, “but the learning curve is steep. Our customers look to our world-leading data, content and information to unlock insight. Harnessing the power of big data enables them to quickly find more informed, trusted answers. In CDAP, we found a development and operations environment that allows engineers to accelerate learning and therefore to accelerate time to market for our new content platform.”

Thomson Reuters is creating a large-scale data repository for content ingestion and management to make the company’s most valuable asset – data – more accessible and more discoverable to its customers, creating new revenue opportunities. The data lake will allow their different lines of business to design customized data packages for their customers, drawing on the wealth of information from across the company. Designed for reusability and portability of big data solutions, CDAP proved to Thomson Reuters through a proof of concept that it is a useful tool to accelerate learning, scalable functionality and eventual time to market.

Typically, new Hadoop engineers have taken up to 6 months to onboard. During the proof of concept phase, Thomson Reuters found that it took developers with Java expertise and new to Hadoop, but using CDAP, only about 1 month to become productive in developing data applications.

"We are very proud to collaborate with Thomson Reuters, the world’s preeminent provider of news and information. Thomson Reuters is an industry leader, transforming the explosion of data into faster and better decision-making capabilities for its customers, worldwide,” said Jonathan Gray, co-founder and CEO at Cask. "CDAP has always been about making big data easier, and enabling our customers to focus on applications and insights that drive business value, rather than on solving mundane infrastructure and integration problems. During its evaluation and adoption of CDAP, the team at Thomson Reuters has shown that CDAP can simplify building and scaling complex data applications.

CDAP goes beyond solving common big data integration problems, such as those related to data preparation and data ingestion. Rather, CDAP provides complete data management and application lifecycle management for big data, offering the ability to rapidly build, package, test and deploy custom applications and scale them with agility. Through its time-saving abstractions, smart pre-integrations and the ability to create reusable components, it delivers significant, rapid value for enterprises in a data-heavy environment, slashing the time to take big data projects from preparation to production. Certified on all major Hadoop distributions on-premises and in the cloud, CDAP offers robust security, high availability and deep enterprise integrations, meeting stringent requirements for business-critical production environments in some of the largest organizations in the world.

Additional Resources
Learn more about Thomson Reuters’ use of CDAP, and join Cask for one of the following events:

  • A joint webinar on September 7 at 11am PT / 2pm ET with Vsu Subramanian, Vice President of Platform Engineering at Thomson Reuters, Sandy Martin, Senior Director of Enterprise Content Platform Engineering at Thomson Reuters, and Nitin Motgi, co-founder and CTO at Cask, titled: “Building a large-scale data lake at Thomson Reuters”
  • A case study describing Thomson Reuters’ use of CDAP, and providing an overview of the customers’ challenges, the Cask solution deployed, and its benefits to Thomson Reuters

About CDAP
The first unified integration platform for big data, Cask Data Application Platform (CDAP) lets developers, architects and data scientists focus on applications and insights rather than infrastructure and integration. CDAP is open source and accelerates time to value from Hadoop through standardized APIs, configurable templates and visual interfaces. With a radically simplified developer experience and a code-free self-service environment, CDAP enables enterprise IT to broaden the big data user base and seamlessly integrates with existing MDM, BI and security and governance solutions.

About Cask
Cask makes building and running big data solutions on-premises or in the cloud easy with Cask Data Application Platform (CDAP), the first unified integration platform for big data. CDAP reduces the time to production for data lakes and data applications by 80%, empowering the business to make better decisions faster. Cask customers and partners include AT&T, AWS, Cloudera, Ericsson, Google, IBM, Lotame, Microsoft, Salesforce, and Tableau, among others. For more information, visit the Cask website at cask.co and follow @caskdata.

About Thomson Reuters
Thomson Reuters is the world’s leading source of news and information for professional markets. Our customers rely on us to deliver the intelligence, technology and expertise they need to find trusted answers. The business has operated in more than 100 countries for more than 100 years. Thomson Reuters shares are listed on the Toronto and New York Stock Exchanges. For more information, visit www.thomsonreuters.com


            

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