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DataHub: Popular metadata architectures explained | LinkedIn Engineering

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When I started my journey at LinkedIn ten years ago, the company was just beginning to experience extreme growth in the volume, variety, and velocity of our data. Over the next few years, my colleagues and I in LinkedIn’s data infrastructure team built out foundational technology like Espresso, Databus, and Kafka, among others, to ensure that LinkedIn would survive and thrive through the next wave of growth. A few years later, I became the tech lead for what was then a pretty small “data analytics infrastructure” team that ran and supported LinkedIn’s Hadoop usage, and also maintained a hybrid data warehouse spanning Hadoop and Teradata.

DataHub: Popular metadata architectures explained Skip to main content LinkedIn respects your privacy LinkedIn and 3rd parties use essential and non-essential cookies to provide, secure, analyze and improve our Services, and to show you relevant ads (including professional and job ads ) on and off LinkedIn. Learn more in our Cookie Policy . Select Accept to consent or Reject to decline non-essential cookies for this use. You can update your choices at any time in your settings . Accept Reject Data Management DataHub: Popular metadata architectures explained Authored by Shirshanka Das Co-founde

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