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Scaling Redshift without Scaling Costs at GIPHY

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binhnguyennus 2018-04-30 15:06:01 +08:00
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@ -241,7 +241,7 @@ An updated and curated list of selected readings to illustrate High Scalability,
* [MPH: Fast and Compact Immutable Key-Value Stores at Indeed](http://engineering.indeedblog.com/blog/2018/02/indeed-mph/)
* [zBase: High Performance, Elastic, Distributed Key-Value Store at Zynga](https://www.zynga.com/blogs/engineering/zbase-high-performance-elastic-distributed-key-value-store-2)
* [Venice: Distributed Key-Value Database at Linkedin](https://engineering.linkedin.com/blog/2017/02/building-venice-with-apache-helix)
* [Column Databases (Cassandra, HBase)](https://aws.amazon.com/nosql/columnar/)
* [Column Databases (Cassandra, HBase, Redshift)](https://aws.amazon.com/nosql/columnar/)
* [Consistent Hashing in Cassandra](https://blog.imaginea.com/consistent-hashing-in-cassandra/)
* [Understanding Gossip (Cassandra Internals)](https://www.youtube.com/watch?v=FuP1Fvrv6ZQ)
* [When NOT to use Cassandra?](https://stackoverflow.com/questions/2634955/when-not-to-use-cassandra)
@ -256,6 +256,7 @@ An updated and curated list of selected readings to illustrate High Scalability,
* [Imgur Notification: From MySQL to HBASE at Imgur](https://blog.imgur.com/2015/09/15/tech-tuesday-imgur-notifications-from-mysql-to-hbase/)
* [Improving HBase Backup Efficiency at Pinterest](https://medium.com/@Pinterest_Engineering/improving-hbase-backup-efficiency-at-pinterest-86159da4b954)
* [ClickHouse - Open Source Distributed Column Database at Yandex](https://clickhouse.yandex/)
* [Scaling Redshift without Scaling Costs at GIPHY](https://engineering.giphy.com/scaling-redshift-without-scaling-costs/)
* [Document Databases (MongoDB, SimpleDB, CouchDB)](https://msdn.microsoft.com/en-us/magazine/hh547103.aspx)
* [eBay: Building Mission-Critical Multi-Data Center Applications with MongoDB](https://www.mongodb.com/blog/post/ebay-building-mission-critical-multi-data-center-applications-with-mongodb)
* [MongoDB at Baidu: Multi-Tenant Cluster Storing 200+ Billion Documents across 160 Shards](https://www.mongodb.com/blog/post/mongodb-at-baidu-powering-100-apps-across-600-nodes-at-pb-scale)