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Web Content Analytics at Scale with Parse.ly

Using Elasticsearch, Parse.ly wrote a time series backend for its real-time content analytics product. This talk will cover time-based indices, hot/warm/cold tiers, doc values, index aliases/versioning, and other techniques to run a multi-terabyte Elasticsearch cluster to perform time series at scale.

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Using Elasticsearch, Parse.ly wrote a time series backend for its real-time content analytics product. This talk will cover time-based indices, hot/warm/cold tiers, doc values, index aliases/versioning, and other techniques to run a multi-terabyte Elasticsearch cluster to perform time series at scale.

Andrew Montalenti

Andrew is the co-founder and CTO of Parse.ly, a Python-built tech startup that helps top online publishers understand what content their audience is interested in -- and why. Prior to starting Parse.ly, Andrew was a technologist with nearly a decade of experience in finance, high tech, and online media. He earned a degree in Computer Science from NYU. A dedicated Pythonista, JavaScript hacker, and open source advocate, Andrewis also a published technical author and editor. Relevant to Elastic{ON}'s audience, he is the author of Lucene: The Good Parts and an Elastic guest blogger. He has presented at PyData, PyCon, and several other technology conferences.