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Published : Nov 20, 2019
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
Nov 2019
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

One of the main points of friction for data scientists and analysts, in their workflow, is to locate the data they need, make sense of it and evaluate whether it's trustworthy to use it. This remains a challenge due to the missing metadata about the available data sources and lack of adequate functionality needed to search and locate data. We encourage teams who are providing analytical data sets or building data platforms to make data discoverability a first-class function of their environments; to provide the ability to easily locate available data, detect its quality, understand its structure and lineage and get access to it. Traditionally this function has been provided by bloated data cataloguing solutions. In recent years, we've seen the growth of open-source projects that are improving developer experiences for both data providers and data consumers to do one thing really well: to make data discoverable. Amundsen by Lyft and WhereHows by LinkedIn are among these tools. What we like to see is a change in providers' behavior to intentionally share the metadata that help discoverability in favor of discoverability tools that infer partial metadata information from silos of application databases.

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