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Presto the Distributed SQL Query Engine

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@ -362,7 +362,12 @@ An organized reading list for illustrating the patterns of scalable, reliable, a
* [Sharding Layer of Schemaless Datastore at Uber](https://eng.uber.com/schemaless-rewrite/)
* [Sharding & IDs at Instagram](https://instagram-engineering.com/sharding-ids-at-instagram-1cf5a71e5a5c)
* [Solr: Improving Performance for Batch Indexing at Box](https://blog.box.com/blog/solr-improving-performance-batch-indexing/)
* [Geosharded Recommendations (2 parts) at Tinder](https://medium.com/tinder-engineering/geosharded-recommendations-part-2-architecture-3396a8a7efb)
* [Geosharded Recommendations (2 parts) at Tinder](https://medium.com/tinder-engineering/geosharded-recommendations-part-2-architecture-3396a8a7efb)
* [Presto the Distributed SQL Query Engine](https://research.fb.com/wp-content/uploads/2019/03/Presto-SQL-on-Everything.pdf?)
* [Presto at Pinterest](https://medium.com/@Pinterest_Engineering/presto-at-pinterest-a8bda7515e52)
* [Presto at Grab](https://engineering.grab.com/scaling-like-a-boss-with-presto)
* [Engineering Data Analytics with Presto and Apache Parquet at Uber](https://eng.uber.com/presto/)
* [Data Wrangling at Slack](https://slack.engineering/data-wrangling-at-slack-f2e0ff633b69)
* [NoSQL Databases](https://www.thoughtworks.com/insights/blog/nosql-databases-overview)
* [Key-Value Databases (DynamoDB)](http://www.cs.ucsb.edu/~agrawal/fall2009/dynamo.pdf)
* [DynamoDB at Nike](https://medium.com/nikeengineering/becoming-a-nimble-giant-how-dynamo-db-serves-nike-at-scale-4cc375dbb18e)