The debate surrounding the capabilities of Apache Spark or Apache Flink, open-source systems for data stream and big data processing, and when to use them, continues to rage. During the Flink Forward conference, held last week in San Francisco, California, one company shared why it chose Flink for its Software as a Service application solution.
BetterCloud Inc. provides the tools that information technology administrators need to manage their SaaS applications, such as Google’s G Suite, Zendesk or Slack. The admins are given a “single pane of glass” that provides a unified view into their users’ activities, as well as a way to see which users provision which applications.
About a year ago, BetterCloud decided it wanted to go a little bit bigger and move to a streaming platform. In looking for the latest iteration of streaming technology, some of the issues BetterCloud wanted to address included managing back pressure, checkpointing, and restoring of state, as well as the distributed streaming application elements that the company had no interest in writing itself.
After looking at a few different options, Flink came out on top, and it has been in production at BetterCloud for the past six months. “We love it; we’re big fans. We love the roadmap,” said Sean Hester (pictured), software architect at BetterCloud.
Hester recently spoke to George Gilbert (@ggilbert41), co-host of theCUBE, SiliconANGLE Media’s mobile live streaming studio, during Flink Forward 2017, held in San Francisco, California. They discussed how Flink fits into BetterCloud’s architecture and reasons to add machine learning. (*Disclosure below.)
From the beginning, BetterCloud designed its roadmap knowing one of its greatest competitive advantages was how quickly they could support additional SaaS applications. Therefore, the company deliberately designed a highly configuration-driven architecture, as opposed to a hard-coded architecture, making it a great fit for Flink’s steaming technology.
Hester said that BetterCloud is looking to add machine learning to its capabilities in the near future.
“One of our big asks to core customers was, ‘I don’t know what normal is [for you]; you figure out what normal is, and then let me know when something abnormal happens.’ Which is a perfect use case for machine learning,” explained Hester.
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of Flink Forward 2017. (*Disclosure: TheCUBE is a paid media partner at Flink Forward. The conference sponsor, data Artisans, does not have editorial oversight of content on theCUBE or SiliconANGLE.)