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Quilt Business

Quilt Data | 1.1.1

Reviews from AWS Marketplace

2 AWS reviews
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  • 4 star
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  • 2 star
  • 1 star

    A computational biologist

A necessity for every data-driven company

  • May 11, 2020
  • Review verified by AWS Marketplace

Quilt is an indispensable tool for anyone that wants to properly manage their data in AWS. A key element to Quilt is that the programmatic interface is intuitive and flexible, offering multiple ways to integrate it into the data analysis workflow (python, R, command line). As only a handful of Quilt functions provide a majority of core functionality, there is not an overwhelming learning curve to get started, but many additional features improve the usability (e.g., reading data directly into memory, single file installation). Beyond the programmatic functionality, the Quilt web-based interface is extremely useful for browsing files and packages and switching between the different versions. I would highly recommend integrating Quilt into your data science workflow.

    Grzegorz M.

Missing tool in Data Science pipeline

  • October 05, 2019
  • Review verified by AWS Marketplace

Quilt simplified our flow in data maintenance and versioning. Now, it became extremely easy to keep track of changes in a dataset and refer in a reproducible manner a specific revision without worrying if someone overwrites the data. We have it already integrated into our flow, so the dataset updates interfere with model building no more.
Quilt team provides us with ongoing support. Bugs happen in every software, but in the case of small bug we found, we received a fixup in no time, so we could smoothly continue our work.
We spotted some drawbacks in Quilt Teams some time ago. These are mostly resolved here, and remaining "wishes" are on the roadmap. It's really nice that devs listen to our needs!
What we love most about Quilt, is the caching feature. We reduced data transfer costs while keeping low complexity of scripts.
Overall grade is 5/5 since that tool was missing heavily in the flow we had for Machine Learning. At this moment we use it also for versioning models (especially that we generate models in a bunch of formats each time) and Jupyter Notebooks (for which Git isn't the best option)

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