Shuyi Chen, Senior Software Engineer, Uber, sits down with George Gilbert at Flink Forward 2018 in San Francisco
#FlinkForward #theCUBE
https://siliconangle.com/2018/04/20/uber-building-real-time-platforms-using-stream-processing-flinkforward/
Uber building real-time platforms using stream processing
As the ride-sharing economy evolves to encompass food delivery and courrier services, Uber Technologies Inc. leverages real-time data analysis to support the broadening ecosystem of restaurants and businesses tapping into Uber’s network of drivers. The effort requires a blend of open source tools, including Apache Flink and Apache Calcite to enable Structured Query Language API and Table API, to create a business intelligence interface.
“People would love to use stream-processing technology to actually replace some of the normal backhand service applications to compose the stream processing logic,” said Shuyi Chen, senior software engineer at Uber.
Chen spoke with George Gilbert (@ggilbert41), co-host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, at the Flink Forward event in San Francisco. They discussed how Uber is making real-time platforms that are implementing stream-processing technologies like Apache Flink. (* Disclosure below.)
Uber’s real-time platforms
Uber has built two platforms using stream processing. One is called AthenaX, which uses Flink SQL to compose stream processing logic. It has developed a user interface to help the company deploy stream processing in production.
“So for example, in our Uber Eats team, they use the SQL API … as the stream-processing tool to build their Restaurant Manager Dashboard,” Chen explained. “And also non-engineers in general can actually use that UI to compose stream processing lock jobs.”
Uber has also developed what they call a “Flink as a service platform,” according Chen, because SQL actually cannot satisfy all the advanced need in Uber to build stream processing. “It’s actually using the platform to do online model update, machinery model update, using Flink,” he said.
Data engineers are the ones that continually train and update models for features that get fed in. “Basically, it’s a online model update,” Chen concluded.
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the Flink Forward 2018 event. (* Disclosure: TheCUBE is a paid media partner for Flink Forward 2018. Neither data Artisans GmbH, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
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Shuyi Chen, Uber | Flink Forward 2018
Shuyi Chen, Senior Software Engineer, Uber, sits down with George Gilbert at Flink Forward 2018 in San Francisco
#FlinkForward #theCUBE
https://siliconangle.com/2018/04/20/uber-building-real-time-platforms-using-stream-processing-flinkforward/
Uber building real-time platforms using stream processing
As the ride-sharing economy evolves to encompass food delivery and courrier services, Uber Technologies Inc. leverages real-time data analysis to support the broadening ecosystem of restaurants and businesses tapping into Uber’s network of drivers. The effort requires a blend of open source tools, including Apache Flink and Apache Calcite to enable Structured Query Language API and Table API, to create a business intelligence interface.
“People would love to use stream-processing technology to actually replace some of the normal backhand service applications to compose the stream processing logic,” said Shuyi Chen, senior software engineer at Uber.
Chen spoke with George Gilbert (@ggilbert41), co-host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, at the Flink Forward event in San Francisco. They discussed how Uber is making real-time platforms that are implementing stream-processing technologies like Apache Flink. (* Disclosure below.)
Uber’s real-time platforms
Uber has built two platforms using stream processing. One is called AthenaX, which uses Flink SQL to compose stream processing logic. It has developed a user interface to help the company deploy stream processing in production.
“So for example, in our Uber Eats team, they use the SQL API … as the stream-processing tool to build their Restaurant Manager Dashboard,” Chen explained. “And also non-engineers in general can actually use that UI to compose stream processing lock jobs.”
Uber has also developed what they call a “Flink as a service platform,” according Chen, because SQL actually cannot satisfy all the advanced need in Uber to build stream processing. “It’s actually using the platform to do online model update, machinery model update, using Flink,” he said.
Data engineers are the ones that continually train and update models for features that get fed in. “Basically, it’s a online model update,” Chen concluded.
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the Flink Forward 2018 event. (* Disclosure: TheCUBE is a paid media partner for Flink Forward 2018. Neither data Artisans GmbH, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
