Authored byShirshanka Das Co-founder and CTO @ Acryl | Founder DataHub project | Ex-LinkedIn December 7, 2020 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 Espr
Open Source Open sourcing DataHub: LinkedIn’s metadata search and discovery platform Co-authors: Kerem Sahin, Mars Lan, and Shirshanka Das Finding the right data quickly is critical for any company that relies on big data insights to make data-driven decisions. Not only does this impact the productivity of data users (including analysts, machine learning developers, data scientists, and data engin
With the increasing number of individuals performing data science, in every organization and team, there is a proliferation of dataset versions at various stages of data analysis. More often than not, these dataset versions are stored in an ad-hoc manner in shared file systems, leading to massive redundancy and duplication, and making it impossible to keep track of and find specific versions. Orph
Samuel Madden CS Head, MIT EECS Department MIT College of Computing Distinguished Professor Email: madden AT csail DOT mit DOT edu Phone: (617)258-6643 Assistant: Alisa Cabral (acabral AT csail DOT mit DOT edu) Room 32-G938 Computer Science and Artificial Intelligence Laboratory (CSAIL) 32 Vassar St. Cambridge, MA 02139 I am the faculty director of the Systems that Learn Initiative and co-direct t
DataHub A Data Ecosystem for Individuals, Teams and People What is DataHub? For End Users: A way to store your data centrally, without having to set up your own database A way to collaborate with others A way to seamlessly share your data with friends and colleagues A suite of tools to process your data For Developers: A database-agnostic, language-agnostic, MITREid (OpenID + OAuth2) integrated pl
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