bdtws_2020_Lineae_Discovery_INGTowards Enterprise Grade Data Discovery and Data Lineage with Apache Atlas and Amundsen
How We Improved Data Discovery for Data Scientists at Spotify - Spotify Engineering
How We Improved Data Discovery for Data Scientists at Spotify At Spotify, we believe strongly in data-informed decision making. Whether we’re considering a big shift in our product strategy or we’re making a relatively quick decision about which track to add to one of our editorially-programmed playlists, data provides a foundation for sound decision making. An insight is a conclusion drawn from d
Open Sourcing WhereHows: A Data Discovery and Lineage Portal
In modern data-driven businesses, the complexity that arises from fast-paced analytics, data mining and ETL processes makes metadata increasingly important. In this blog post, we share our own journey and a new open source effort that aims to boost productivity and data provenance. WhereHows, a project of the LinkedIn Data team, works by creating a central repository and portal for the processes,
Amundsen — Lyft’s data discovery & metadata engine
This post introduces the Amundsen project — its goals and users. You can learn more about a hosted version of Amundsen at Stemma. In order to increase the productivity of data scientists and research scientists at Lyft, we developed a data discovery application built on top of a metadata engine. Code named, Amundsen (after the Norwegian explorer, Roald Amundsen), we improve the productivity of our
Databook: Turning Big Data into Knowledge with Metadata at Uber
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more From driver and rider locations and destinations, to restaurant orders and payment transactions, every interaction on Uber’s transportation platform is driven by data. Data powers Uber’s global marketplace, enabling more reliable and seamless user experiences across our
GitHub - mitdbg/aurum-datadiscovery
Webpage version of this documentation: http://mitdbg.github.io/aurum-datadiscovery/ Aurum helps users identify relevant content among multiple data sources that may consist of tabular files, such as CSV, and relational tables. These may be stored in relational database management systems (RDBMS), file systems, and they may live in cloud services, data lakes or other on-premise repositories. Aurum
Discovery and Consumption of Analytics Data at Twitter
Insights Discovery and Consumption of Analytics Data at Twitter Introduction The Data Platform team at Twitter maintains systems to support and manage the production and consumption of data for a variety of business purposes, including publicly reported metrics (e.g., monthly or daily active users), recommendations, A/B testing, ads targeting, etc. We run some of the largest Hadoop clusters in the
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7 Things Every Data Worker Should Know About Metadata
https://dl.acm.org/citation.cfm?id=3209900.3209911
Democratizing Data at Airbnb
Democratizing Data Discovery at Airbnb - Graph Database & Analytics
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