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Processing data can be a real a mess! Here at Factual we’ve felt the pain of managing data workflows for a very long time. Here are just a few of the issues: a multitude of steps, with complicated dependencies code and input can change frequently — it’s tiring and error-prone to figure out what needs to be re-built inputs scattered all over (home directories, NFS, HDFS, etc.), tough to maintain, t
Factual’s U.S. Places dataset is built from tens of billions of signals. Our raw data is stored in HDFS and processed using Hadoop. We’re big fans of the core Hadoop stack, however there is a dark side to using Hadoop. The traditional approach to building and running Hadoop jobs can be cumbersome. As our Director of Engineering once said, “there’s no such thing as an ad-hoc Hadoop job written in J
Return to Blog Thinking in Clojure for Java Programmers (Part 1 — A Gentle Intro) We do a lot of Java work at Factual. Most of our back-end data store is written in Java, and we use a ton of Java libraries and frameworks. We recently began experimenting with Clojure in certain parts of our data store. Clojure is a Lisp implementation that runs on the JVM and offers excellent Java interoperability.
FACTUAL IS PART OF FOURSQUARE Thanks for visiting. Factual joined forces with Foursquare in early 2020 - come see what we’ve been up to together.
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