How Netflix Scales its API with GraphQL Federation (Part 2)
In our previous post and QConPlus talk, we discussed GraphQL Federation as a solution for distributing our GraphQL schema and implementation. In this post, we shift our attention to what is needed to run a federated GraphQL platform successfully — from our journey implementing it to lessons learned. Our Journey so FarOver the past year, we’ve implemented the core infrastructure pieces necessary fo
Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache Hadoop
Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache Hadoop Connecting users worldwide on our platform all day, every day requires an enormous amount of data management. When you consider the hundreds of operations and data science teams analyzing large sets of anonymous, aggregated data, using a variety of different tools to better understand and maintain
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
Uber’s Big Data Platform: 100+ Petabytes with Minute Latency
Data / ML, EngineeringUber’s Big Data Platform: 100+ Petabytes with Minute LatencyOctober 17, 2018 / Global Uber is committed to delivering safer and more reliable transportation across our global markets. To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks in our
Machine Learning Schema Community Group
This group represents a collaborative, community effort with a mission to develop, maintain, and promote standard schemas for data mining and machine learning algorithms, datasets, and experiments. Our target is a community agreed schema as a basis for ontology development projects, markup languages and data exchange standards; and an extension model for the schema in the area of data mining and m
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Data Commons – Google Research
Are you still not using Version Control for Data?
https://dl.acm.org/doi/10.1145/3318464.3380605
https://dl.acm.org/doi/10.1145/3318464.3389726
https://www.juansequeda.com/blog/2019/10/24/a-pay-as-you-go-methodology-to-design-and-build-knowledge-graphs/
Who should be responsible for your data? The knowledge scientist
Productionizing Machine Learning with Delta Lake
Open Sourcing Delta Lake
Democratizing Data at Airbnb
Democratizing Data at Airbnb
Governed Self Service Analytics at eBay
Extreme Analytics @ eBay
Schema Evolution in Process Management Systems - DBIS EPub
A model for schema evolution in object-oriented database systems.