Building a Versatile Analytics Pipeline on Top of Apache Spark

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It is common for consumer Internet companies to start off with popular third-party tools for analytics needs. Then, when the user base and the company grows, they end up building their own analytics data pipeline and query engine to cope with their data scale, satisfy custom data enrichment and reporting needs and achieve high quality of their data. That’s exactly the path that was taken at Grammarly, the popular online proofreading service.
In this session, Grammarly will share how they improved business and marketing analytics, previously done with Mixpanel, by building their own in-house analytics engine and application on top of Apache Spark. Chernetsov wil touch upon several Spark tweaks and gotchas that they experienced along the way:

– Outputting data to several storages in a single Spark job
– Dealing with Spark memory model, building a custom spillable data-structure for your data traversal
– Implementing a custom query language with parser combinators on top of Spark sql parser
– Custom query optimizer and analyzer when you want not exactly sql
– Flexible-schema storage and query against multi-schema data with schema conflicts
– Custom aggregation functions in Spark SQL

Session hashtag: #SFexp18

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