Postgres Architectures in Production

When using PostgreSQL in production it is important to implement a strategy for High Availability. With a database service, the HA properties apply to both the service itself and of course to the data set.

In this talk we learn how to think about specific HA needs of your production environment and how to achieve your requirements with Open Source tooling when it comes to your database of choice, PostgreSQL. In particular, we dive in many options that could be implemented for Postgres to evolve its offering from being a toolset to being “batteries included”. What does it mean in the context of HA? How to achieve it?

Python for PostgreSQL: how to use it, how to be good at it

Let's see, with practical examples, how to make Python and PostgreSQL talk to each other seamlessly: how to connect to a server, how to exchange data, manage transactions, passing parameters in a safe and expressive way, how to manage notifications.

We will cover psycopg2, the most used PostgreSQL adapter for Python, but also look and the up and coming psycopg3: what will remain the same, what will change, how to better organise a Python program to make the most of PostgreSQL.

Shardman - the native approach to sharding in PostgreSQL

The amount of data that is handled today by Enterprises and Web companies is constantly growing. At the same time, it becomes increasingly difficult to have and synchronize several copies of data in different systems. As a result there is a demand to work with large amounts of data directly in a transactional DBMS. This requirement is often imposed by the logic of applications that need real-time results. In this talk we will consider what a universal distributed transactional DBMS can be. We will analyze such aspects as the types of load and their prioritization, dynamic resource allocation and the level of consistency. What tools in PostgreSQL can be used to build such system, what we have already done and what is still missing.

Postgres and the Artificial Intelligence Landscape

Artificial intelligence, machine learning, and deep learning are intertwined capabilities that attempt to solve problems that defy traditional computational solutions — problems include fraud detection, voice recognition, and search result recommendations. While they defy simple computation, they are computationally expensive, involving computation of perhaps millions of probabilities and weights. While these computations can be done outside of the database, there are specific advantages of doing machine learning inside the database, close to where the data is stored. This presentation explains how to do machine learning inside the Postgres database.