PGConf.SPB 2023

In modern data analysis, machine learning models are used as often as databases. Such IT giants as Google and Amazon have already combined them. Microsoft and Yandex are not far behind. Isn't it time to implement a machine learning model in PostgreSQL? In the report you will hear about the basics of machine learning, its implementation in databases and an example of realization as postres extension.

Unfortunately, ideal computer systems exist only in science fiction books. PostgreSQL is not exception and sometimes problems may occur. I would like to discuss how to correctly (and incorrectly) try to solve a problem, which way to choose, which tool to use.

The talk is of interest to both beginners and experienced users and database administrators.

Facing the restrictions on the use of Western software, our bank has developed and put into commercial operation a reliable mechanism for data replication between two DBMSs. This mechanism allowed the bank's teams without any special restrictions to replace the product from Oracle (Oracle Golden Gate was previously used in the bank for a long time). In the presentation, we will talk about the main advantages of our software and the nuances of its use.

Postgres Pro Enterprise is based on open source PostgreSQL. However, the difference is significant: the current version of Postgres Pro Enterprise has 40 more features that are not a part of PostgreSQL. 20 more mechanisms are being developed either as built-in features or separate products. This includes BiHA, DBaaS, pg_probackup, etc. In this presentation, we'll briefly discuss some of them.