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Startups Innovating Data Science Platforms

Hadoop: The Elephant In The Room

Hadoop has been a great solution to analyzing big data, but is too much for most companies. Most don’t have petabytes of data, or a team of engineers and PhD’s to set up and maintain a cluster. So what options are left for these companies? Until recently..not many. There are a few startups looking to change this.

Zillabyte

A distributed cloud platform built independent of Hadoop, Zillabyte makes data science accessible to everyone who can program. They currently support Ruby and Python, and will support Javascript in the future. To help users get started they offer web data sets for analysis and a trial period to test the product.

SensePlatform

Currently in Beta, SensePlatform makes collaboration easier with their platform. They are building a web platform to make collaboration on projects and integrating with existing data science tools easy. They also support Hadoop integration, making it easier to use their infrastructure, but without deep knowledge of the Hadoop ecosystem. You can sign up for an invite to test their platform.

ScienceOps

ScienceOps is a cloud platform for rapidly deploying predictive models. ScienceOps supports Python and R, hitting the two most popular languages data scientists use.  They are strongly focused on model management, even making version control easier for users. You can sign up to try it on their public sandbox.

Making Data Science More Accessible

We’re seeing an explosion of interest in data science. There are new resources being posted and published, communities (like this one) have formed and help to define the field, and now platforms are catching up. All of these platforms focus on rapid deployment and on demand scalability, filling the space Hadoop leaves in the field. They are the next tools developers and data scientists will look to for their big data processing needs, and will lower the barrier for companies using data effectively.

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