PGConf.Russia 2025

Every day, thousands of engineers work tirelessly to make data more accurate, reliable, and up-to-date. But sometimes, we need to do the exact opposite—corrupt it.

Whether it’s masking or replacing sensitive information, or even generating entirely new datasets while preserving key business properties, data obfuscation is a crucial task. It’s essential for testing systems, sharing data with third parties, and more. However, given the complexity of data schemas and business logic, this is far from trivial.

In this talk, we’ll explore the challenges of working with artificial data and discuss various approaches to solving them using PostgreSQL’s built-in features and external extensions.

Where I used to work, we had pushed ElasticSearch to its breaking point. We needed an even more scalable replacement for a write-heavy, read-seldom workload. So we built one on PostgreSQL. Now, many of us are building the successor as an open source project. 

This talk goes over the design of Bagger (named after the giant mining machines), which can manage logs into tens or hundreds of petabytes. More than just a review of the architecture, this talk focuses on the whys and the tradeoffs made in the design. 

The talk is intended both to showcase how programmable and powerful PostgreSQL is, but also illustrate the fundamental tradeoffs which must be faced when pushing any technology into the big data space.

t’s well known that pg_upgrade is the go-to tool for fast PostgreSQL upgrades. However, even with this tool, there are cases where the upgrade process takes far longer than expected.

In our case, upgrading a PostgreSQL database with 350,000 tables meant either waiting 3.5 hours or finding a better approach. By digging into the pg_upgrade source code, we discovered a way to speed up the process significantly. In this talk, we’ll share how we did it.

In today’s rapidly evolving technology landscape, PostgreSQL has emerged as a popular choice for organizations seeking to leverage the power of open-source databases while maintaining scalability, performance, and flexibility. 

As enterprises migrate from proprietary database systems like Oracle to PostgreSQL, developers face the challenge of adapting existing PL/SQL codebases to PL/pgSQL. Despite the similarities between these two procedural languages, significant differences in their syntax, performance characteristics, and ecosystem features require a thoughtful approach to migration and optimization. 

This session will focus on highlighting the key differences between Oracle’s PL/SQL and PostgreSQL’s PL/pgSQL, providing actionable insights on how to efficiently navigate these differences during migration. A deep dive will be taken into critical areas including compilation methodology, Code Debugging , Exception Handling, work_mem , Bulk Load and more.