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Thomson Reuters makes real-time data easily accessible in the cloud as part of plans to deliver all its financial data and tools in the cloud

Thomson Reuters makes real-time data easily accessible in the cloud as part of plans to deliver all its financial data and tools in the cloud

Enhancement to Elektron Data Platform makes all real-time pricing data more easily accessible via a cloud API, including over 70 million instruments from over 500 global exchanges and thousands of OTC markets.

 In a major step forward for the consumption of financial data in the cloud, Thomson Reuters is making its comprehensive real-time data accessible in the cloud to make it easier for firms to power their cloud-based business applications with real time pricing information.

This means clients will have increased flexibility in how they use the data, from portfolio management to analytics or post trade uses, whether they need the data on-premise, in the cloud or on mobile.

The new delivery option can simplify access to real-time price information across the financial community and beyond by taking away the need to invest in on-premise infrastructure. It also helps clients be more nimble by helping accelerate time to market for businesses that need access to real-time data.

The Thomson Reuters Financial & Risk business is committed to making all its trusted data and services available easily to clients in the cloud via the Elektron Data Platform. The Elektron Data Platform is an integrated content and capabilities platform designed to enable clients to get the data and analytics they need from a single trusted platform with the ability to integrate their own data, offering a range of delivery options to distribute information to wherever it is needed.

The enhancement to the Elektron Data Platform will initially provide access to real-time data on the secure and scalable Amazon Web Service (AWS) Cloud in North America, with plans to expand to Europe and Asia later this year. With the cloud API, data can be consumed natively on AWS, directed to applications based in other cloud environments, or to an on-premise environment.

As a simplified, conflated real-time service, the real-time in the cloud service can power up to three client applications at three updates per second across 50,000 instruments at the same time, which can be selected from the full universe of over 70 million instruments covered by the Elektron Data Platform.

DriveWealth Technologies, a digital broker-dealer for retail investors, is among the clients to be using the Elektron real-time in the cloud delivery.

Harry Temkin, Chief Information Officer of DriveWealth Technologies, comments: “Our business is using the cloud to improve our agility and being able to access real-time pricing data means we can power our business applications with trusted information from Thomson Reuters, with a simple solution that was up and running in a matter of hours.”

“The financial community is increasingly building smarter machines using the cloud to move faster, be more innovative and succeed,” says Brennan Carley, Global Head of Enterprise for the Financial & Risk business at Thomson Reuters. “Smarter machines are powered by data, which is why we are making it easier for clients to consume our comprehensive and trusted data and tools in the cloud. Combining the breadth and depth of our data with the flexibility and agility of the cloud lowers barriers to entry and helps clients be more nimble as they identify opportunities to build long-term competitive advantage. We are committed to supporting our customers in the cloud, by making our clean, normalized and linked data and tools more accessible in whichever cloud they want to use.”

Brad Bailey, Research Director at Celent, comments: “The cloud is becoming central to innovation in the front office, a key endpoint in the capital market infrastructure. This is especially true in enabling agility in market data access, usage, and, storage. Moreover, the cloud is allowing firms to leverage their data for the next wave of analytics, predictive analysis, and machine learning.”

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