Bridging the gap, OLTP and Real-Time Analytics in a Big Data World
Ariel Weisberg
Big Data is often created by high-volume feeds of incoming state that remains mutable, possibly indefinitely. It's a poor fit for traditional OLTP databases due to the volume involved and it's a poor fit for analytic databases due to the emphasis on mutable state and point queries. Both types of databases are ultimately necessary because the ideal storage layout for a given data set is determined by the queries you need to execute efficiently.

Over the past few years, several solutions have cropped up to solve this problem either by bridging specialized systems together or creating new systems that attempt to be a one-size-fits-all solution to OLTP and analytics.

This class will give an overview of OLTP-oriented databases (Postgres, MySQL, VoltDB), hybrid OLTP and OLAP (HBase, Cassandra, MongoDB), and OLAP (Vertica, Netezza, Hadoop), and how they can be used exclusively or together to satisfy mixed OLTP/OLAP workload at "Big Data" scale.

Level : Advanced