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In-Memory Analytics

Satisfying the Need for Speed

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Disruptive Analytics

Abstract

In-memory analytics” is a misnomer: all analytics run in memory and have always done so. Two things distinguish modern in-memory analytics.

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Notes

  1. 1.

    Solid state devices and flash memory present a new opportunity for systems architecture. As of 2016, use of SSD and flash in analytic datastores is an emerging technology.

  2. 2.

    http://www.phuse.eu/download.aspx?type=cms&docID=2847

  3. 3.

    http://www.computerworld.com/article/2947614/cloud-storage/mit-proves-flash-is-as-fast-as-ram-and-cheaper-for-big-data.html

  4. 4.

    http://siliconangle.com/blog/2012/05/16/sap-hana-now-the-biggest-in-memory-database-in-the-world/

  5. 5.

    https://www.forrester.com/The+Forrester+Wave+InMemory+Database+Platforms+Q3+2015/fulltext/-/E-res120222

  6. 6.

    http://techcrunch.com/2014/06/30/databricks-snags-33m-in-series-b-and-debuts-cloud-platform-for-processing-big-data/

  7. 7.

    234 packages as of mid-June 2016.

  8. 8.

    https://databricks.com/blog/2015/09/24/spark-survey-results-2015-are-now-available.html

  9. 9.

    http://spark.apache.org/releases/spark-release-1-0-0.html

  10. 10.

    https://databricks.com/blog/2014/07/01/shark-spark-sql-hive-on-spark-and-the-future-of-sql-on-spark.html

  11. 11.

    http://spark.apache.org/releases/spark-release-1-3-0.html

  12. 12.

    http://people.csail.mit.edu/matei/papers/2015/sigmod_spark_sql.pdf

  13. 13.

    https://github.com/Intel-bigdata/spark-streamingsql

  14. 14.

    http://www.slideshare.net/SparkSummit/hundreds-of-queries-in-the-time-of-one-gianmario-spacagna

  15. 15.

    http://www.slideshare.net/SparkSummit/topnotch-systematically-quality-controlling-big-data-by-david-durst

  16. 16.

    http://www.slideshare.net/SparkSummit/petabyte-scale-anomaly-detection-using-r-spark-by-sridhar-alla-and-kiran-muglurmath

  17. 17.

    http://www.slideshare.net/SparkSummit/how-spark-is-making-an-impact-at-goldman-sachs-by-vincent-saulys

  18. 18.

    http://www.slideshare.net/SparkSummit/monte-carlo-simulations-in-adlift-measurement-using-spark-by-prasad-chalasani-and-ram-sriharsha

  19. 19.

    http://www.slideshare.net/SparkSummit/distributed-time-travel-for-feature-generation-by-db-tsai-and-prasanna-padmanabhan

  20. 20.

    https://spark-summit.org/2015/events/use-of-spark-mllib-for-predicting-the-offlining-of-digital-media/

  21. 21.

    http://www.slideshare.net/SparkSummit/escaping-flatland-interactive-highdimensional-data-analysis-in-drug-discovery-using-spark-by-josh-snyder-victor-hong-and-laurent-galafassi

  22. 22.

    http://www.slideshare.net/SparkSummit/building-a-just-in-time-data-warehouse-by-dan-morris-and-jason-pohl

  23. 23.

    http://www.slideshare.net/SparkSummit/lambda-at-weather-scale-by-robbie-strickland

  24. 24.

    http://venturebeat.com/2015/09/25/apache-drill-gurus-at-dremio-raise-more-than-10m-from-redpoint-and-lightspeed/

  25. 25.

    http://www.cs.berkeley.edu/~haoyuan/papers/2013_ladis_tachyon.pdf

    http://www.cs.berkeley.edu/~haoyuan/papers/2014_EECS_tachyon.pdf

    http://www.cs.berkeley.edu/~haoyuan/papers/2014_socc_tachyon.pdf

  26. 26.

    http://www.alluxio.com/assets/uploads/2016/02/Baidu-Case-Study.pdf

  27. 27.

    https://dzone.com/articles/Accelerate-In-Memory-Processing-with-Spark-from-Hours-to-Seconds-With-Tachyon

  28. 28.

    http://blogs.wsj.com/venturecapital/2015/03/17/andreessen-horowitz-invests-7-5m-in-big-data-startup-tachyon/

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Authors and Affiliations

  1. Newton, Massachusetts, USA

    Thomas W. Dinsmore

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  1. Thomas W. Dinsmore
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© 2016 Thomas W. Dinsmore

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Dinsmore, T.W. (2016). In-Memory Analytics. In: Disruptive Analytics. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-1311-7_5

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