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      • Spark’s ability to store data in memory and rapidly run repeated queries makes it a good choice for training machine learning algorithms. Running broadly similar queries again and again, at scale, significantly reduces the time required to go through a set of possible solutions in order to find the most efficient algorithms.
      medium.com/the-ramp/spark-101-what-is-it-what-it-does-and-why-it-matters-d54b2287a8d2

      Spark 101: What Is It, What It Does, and Why It Matters

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  4. towardsdatascience.com › deep-learning-with-sparkDeep Learning with Spark in Deep Java Library in 10 minutes

    Jun 10, 2020 · In this blog, we demonstrate how users can execute deep learning workloads directly from Scala using the Deep Java Library (DJL). DJL is a framework-agnostic library developed to provide deep learning directly in Spark jobs developed with Java.

  5. www.linode.com › docs › guidesWhy You Should Use Apache Spark for Data Analytics

    Aug 19, 2023 · Why You Should Use Apache Spark for Data Analytics. Published August 19, 2023 by Jeff Novotny. Create a Linode account to try this guide. Within the growing field of data science, Apache Spark has established itself as a leading open source analytics engine.

    • Linode

  6. www.infoq.com › articles › deep-learning-apacheAccelerating Deep Learning on the JVM with Apache Spark and ...

    Jun 11, 2021 · In this article, authors discuss how to use Deep Java Learning (DJL), Apache Spark v3, and NVIDIA GPU computing to simplify deep learning pipelines while improving performance and reducing...

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  7. thenewstack.io › the-good-bad-and-ugly-apacheThe Good, Bad and Ugly: Apache Spark for Data Science Work

    Jun 26, 2018 · It’s easy to see why Apache Spark is so popular. It does in-memory, distributed and iterative computation, which is particularly useful when working with machine learning algorithms. Other tools might require writing intermediate results to disk and reading them back into memory, which can make using iterative algorithms painfully slow.

  8. www.kdnuggets.com › 2018 › 04Deep Learning With Apache Spark: Part 1 - KDnuggets

    In the spirit of Spark and Spark MLlib, it provides easy-to-use APIs that enable deep learning in very few lines of code. It focuses on ease of use and integration, without sacrificing performace. It’s build by the creators of Apache Spark (which are also the main contributors) so it’s more likely for it to be merged as an official API than ...

  9. medium.com › @shivanipanchiwala › apache-spark-aApache Spark: A Primer on Why Spark Matters and How It Works

    May 13, 2024 · 1. Speed: Apache Spark’s in-memory computation allows it to process data up to 100 times faster than traditional big data processing frameworks like Hadoop MapReduce. By caching...

  10. towardsdatascience.com › the-what-why-and-when-ofThe What, Why, and When of Apache Spark | by Allison Stafford ...

    Jan 12, 2020 · High speed data querying, analysis, and transformation with large data sets. Compared to MapReduce, Spark offers much less reading and writing to and from the disk, multi-threaded tasks (from Wikipedia: the threads share the resources of a single or multiple cores) within Java Virtual Machine (JVM) processes.

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