Historically PostgreSQL was intended to transactional OLTP workload. This thesis is confirmed by row-based kind of storage and impossibility (or some complication) in building distributed engine of query execution based on MPP principles. However, due to extensibility of PostgreSQL core (first of all, by using of pluggable access methods) and tolerant license policy similar to BSD there were appeared new different forks and extensions allowing effective processing of big data in analytical manner.

In current talk I'm going to review the PostgreSQL fork called Greenplum and Citus and TimescaleDB extensions from system developer's perspective by comparing their common analytical engine features: column storage, data compression, distributed query execution and so on. The results of such overview will be helpful to database architects seeking PostgreSQL-based DBMS for analytical workload.