HDFS

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the MAD Podcast

4 statements about HDFS, every show

MAD Assertion Not checkable as stated
Uber consolidated all log and business data into an HDFS data lake
“What we really created was, like, using HDFS, like, a data lake, where we basically copied the whole data sets from, like whatever we get from, like, analytical logs or, like, all our business data sources, too, into HDFS.”
Praveen Murugesan Sep 30, 2016 ▶ 4:28 The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)
MAD Assertion Supported
Srivas: A single Apache HDFS cluster handles roughly 100 million files
“HDFS, a single cluster, can do about a hundred million files.”
M.C. Srivas Dec 17, 2015 ▶ 8:41 A Fireside Chat with MapR CTO M.C. Srivas (Data Driven NYC / FirstMark)
MAD Assertion Supported
Stoica: Hadoop's HDFS read/write cycle crippled early iterative machine learning
“If you look at the machine learning, it's, fundamentally, it's an iterative algorithm, and every iteration is turned into a Hadoop job. So between the iteration, you write the data and read the data from HDFS, so that's why it's very slow.”
Ion Stoica Apr 2, 2015 ▶ 4:08 Ion Stoica, Databricks // Creating Apache Spark // Data Driven NYC (FirstMark Capital)
MAD Assertion Not checkable as stated
Papaioannou in 2012: HDFS will scale forever for most companies
“HDFS, which will scale forever for probably like 99.9% of the companies on the planet. They're just never going to run out of space, you know, to put data into it.”
Todd Papaioannou Dec 5, 2013 ▶ 15:08 Panel: Continuuity, Sailthru and Visual Revenue // Data Driven NYC #7 // June 2012

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