Nemo: Data discovery at Facebook
Large-scale companies serve millions or even billions of people who depend on the services these companies provide for their everyday needs. To keep these services running and delivering meaningful experiences, the teams behind them need to find the most relevant and accurate information quickly so that they can make informed decisions and take action. Finding the right information can be hard for
Presto Infrastructure at Lyft
OverviewEarly in 2017 we started exploring Presto for OLAP use cases and we realized the potential of this amazing query engine. It started as an adhoc querying tool for data engineers and analysts to run SQL in a faster way to prototype their queries, when compared to Apache Hive. A lot of internal dashboards were powered by AWS-Redshift back then and it had data storage and compute coupled toget
Data Engineering at SmartNews
2019年3月8日のMachine Learning Casual Talks #9で話した資料です https://mlct.connpass.com/event/120451/
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
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