Introduction to using BlinkDB - Presented by : Sameer Agarwal - UC Berkeley AmpLab 2013
AMP Camp Three -- Analytics and Machine Learning at Scale was held in Berkeley California and live streamed online, August 29-30, 2013. AMP Camp 3 attendees and online viewers learned to solve big data problems using components of the Berkeley Data Analytics Stack (BDAS) including Spark, Shark, Mesos, Tachyon, MLbase as well as Hadoop. The event was held in the Chevron Auditorium in the Internati
What are the main differences between Facebook, Presto, and Amplab Shark?
Answer (1 of 2): 1. Primary Use Case: While both are intended for analytics, Shark's primary use case is providing SQL to an (extremely fast) in-memory database, with support also for on-disk (or abstract) data sources. Presto is designed to be a fast SQL engine for the latter, and does not have ...
BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data
Queries with Bounded Errors and Bounded Response Times on Very Large Data BlinkDB is a massively parallel, approximate query engine for running interactive SQL queries on large volumes of data. It allows users to trade-off query accuracy for response time, enabling interactive queries over massive data by running queries on data samples and presenting results annotated with meaningful error bars.
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GitHub - sameeragarwal/blinkdb: BlinkDB: Sub-Second Approximate Queries on Very Large Data.
