Firefly2025-09-292025-10-013https://postgresql.us/events/pgconfus2025/schedule/08:0001:30Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2153/Breaks09:3000:10Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2152/Session09:4000:20Forum AIn this talk, you’ll get a birds-eye view of all the Postgres work at Microsoft in 2025. This keynote will share 4 things with you: (1) our Postgres open-source work, including some of the significant contributions we’ve made to Postgres 18; (2) our investments in Azure Database for PostgreSQL to improve enterprise security, performance, and availability; (3) new AI features for developers in the Azure cloud and the new VS Code extension for PostgreSQL, and (4) our active participation in the Postgres community—which among other things includes sponsoring Postgres conferences like PGConf NYC and organizing POSETTE: An Event for Postgres.https://postgresql.us/events/pgconfus2025/schedule/session/2145/SponsorClaire Giordano10:0000:30Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2150/Breaks10:3000:50Forum AAt the time of PGConf.NYC, PostgreSQL 18 should either be released or be right around the corner - unless something unexpected comes along. This talk will give an overview of some of the many new features in this version.https://postgresql.us/events/pgconfus2025/schedule/session/2095/SessionMagnus Hagander10:3000:50Forum BWe have all heard about Postgres vacuum horror stories and tales of transaction wraparound disasters. Even if you've never been through one yourself, you may be concerned that you might someday experience it, or may know people who have avoided Postgres altogether because of it. But it doesn't have to be this way. In this talk, we will explore this topic through a review of a real-world wraparound incident, walking through the challenges and hope offered by changes in the last several Postgres releases. We'll explore the inner workings of the Postgres vacuum process, its dual purpose of managing both disk bloat and transaction IDs (XIDs), and how recent innovations have improved this process, particularly for large tables with heavy transaction loads. We'll dive deep into key features like: - What Index Deduplication has to do with minimizing vacuum workload and improving efficiency. - How On/Off Index Vacuuming gives you control over vacuuming during maintenance windows. - Why one key Autovacuum enhancement is critical for improving XID wraparound prevention. Beyond theory, we'll revisit the real-world incident and see how each new Postgres feature would have mitigated the impact. While no single change is a silver bullet, the combined effect is significant. We'll also explore how the new radix tree implementation in Postgres 17 might be the possible final piece of the puzzle for providing a manageable solution to XID wraparound risks. This talk equips DBAs with a deeper understanding of recent Postgres advancements and their practical application in managing high-transactional and/or mission-critical workloads. You'll leave with actionable insights to optimize your Postgres environment and concrete arguments to convince management that it is worth the upgrade for the potential to minimize the risk of XID wraparound incidents.https://postgresql.us/events/pgconfus2025/schedule/session/2105/SessionRobert Treat10:3000:50Hub OneCloud storage was built around the limits of old hardware. Spinning hard drives (HDDs) were slow and fragile. So, early on, cloud providers moved storage off of servers. They used network-attached disks to boost durability and scalability. But hardware has evolved. Today, you can get 2.5 million IOPS from a $600 NVMe SSD. Achieving the same throughput through network-attached storage in the cloud can cost hundreds of thousands of dollars per month. With NVMe SSDs now faster, cheaper, and more reliable, it's time to rethink PostgreSQL storage. In this talk, we’ll walk through different disk architectures and their impacts on PostgreSQL performance, how we got here, what’s changed, and why local NVMe SSDs are the future for PostgreSQL storage. We'll also share benchmarks comparing performance on different disk architectures.https://postgresql.us/events/pgconfus2025/schedule/session/2058/SessionBurak Yucesoy10:3000:50Hub TwoOracle to PostgreSQL migrations are a compelling trend. And many of you who are migrating from Oracle to PostgreSQL in the cloud are doing so to make it easier for AI adoption—and to reduce your licensed software & hence costs. However, migrating databases the traditional way is often slow, complex, and resource-intensive—presenting challenges in schema conversion, data migration, and application remediation. In this talk you’ll learn how to use the Cloud Migration Cockpit (CMC) Framework to accelerate the migration process by 4x and automate critical phases such as assessment, schema conversion, data migration, and SQL remediation for applications & interfaces—ensuring high accuracy and minimal disruption. Additionally, CMC provides a systematic and efficient approach to manage the entire migration lifecycle, including infrastructure provisioning, testing support, production cutover planning, and eventually going live with PostgreSQL databases. You’ll also learn from real-world case studies and best practices for seamless Oracle to PostgreSQL migration. Leveraging AI and automation, database migrations that once took 12 months can now be completed in just 12 weeks.https://postgresql.us/events/pgconfus2025/schedule/session/2055/SponsorKirk RoybalNeeta Goel11:3000:50Forum AI'll be presenting how Instacart redesigned its search infrastructure with PostgreSQL as the backbone for a hybrid search system. Moving away from a legacy Elasticsearch setup, the team adopted pgvector to unify vector and keyword search within Postgres, enabling significant improvements in system simplicity and developer agility. The presentation will highlight lessons learned in optimizing Postgres for real-time, large-scale retrieval as a case study for teams seeking to consolidate retrieval systems without compromising speed or relevance. Reference : https://tech.instacart.com/how-instacart-built-a-modern-search-infrastructure-on-postgres-c528fa601d54https://postgresql.us/events/pgconfus2025/schedule/session/2100/SessionAnkit Mittal11:3000:50Forum BTesting changes against a Postgres database is critical, but the common practice of replicating production databases comes with significant challenges: handling sensitive PII data, managing high costs, and dealing with excessive data volumes. These hurdles can slow down development and introduce privacy concerns. In this talk, I’ll share practical strategies for overcoming these challenges while optimising for cost and speed. First, I’ll explore techniques for anonymising production data, ensuring privacy compliance while maintaining data integrity and usefulness. For companies working with terabytes of data, I’ll discuss the importance of effective sampling to downsize datasets to manageable gigabytes, reducing complexity and costs. Drawing from our experience managing hundreds of thousands of PostgreSQL databases for our customers, we’ve found that one of their biggest challenges is maintaining development speed. One major bottleneck is developers waiting to test their pull requests against the database. By using copy-on-write branching from your staging environment, you can give every developer their own isolated database instance. This approach accelerates iteration and testing while keeping infrastructure costs under control. Note: This talk is based on our experience working with customers to create and enhance their PostgreSQL staging and development workflows.https://postgresql.us/events/pgconfus2025/schedule/session/2024/SessionMonica Sarbu11:3000:50Hub OnePgBouncer is an open source connection pooler that is loved by the PostgreSQL community for its speed and lightweight design. Yet, it has always worked in a single thread, which means users must run and manage multiple PgBouncer instances to raise throughput — an administrative headache. We've re-engineered PgBouncer to be fully multi-threaded, doubling its efficiency while keeping its trademark light footprint. In this talk, you'll hear about how we made PgBouncer multi-threaded, the testing and benchmarking we performed on multi-threaded PgBouncer, how you can use multi-threaded PgBouncer, and the roadmap for what's coming next.https://postgresql.us/events/pgconfus2025/schedule/session/1994/SessionBeihao ZhouGuanqun Yang11:3000:50Hub TwoPostgreSQL's success isn’t just the result of its powerful core database—it depends on the rich ecosystem of tools and extensions that support it. Features like backup management, high availability, and extensibility through plugins are critical to its widespread adoption and real-world reliability. This talk offers a behind-the-scenes look at how various ecosystem projects collaborate with core PostgreSQL development. We'll highlight how Barman was developed in tandem with PostgreSQL 17’s new incremental backup feature. We'll cover how the extension_control_path patch for PostgreSQL 18 introduces a cloud-native way to make extensions available in Postgres deployments, particularly in Kubernetes. We'll also discuss ongoing efforts that aim to unlock a new wave of ecosystem capabilities—particularly around advanced index types. Attendees will gain insight into how coordinated development across projects strengthens PostgreSQL as a platform and what's coming next from the EDB side of things.https://postgresql.us/events/pgconfus2025/schedule/session/2143/SponsorMartín Marqués12:2001:10Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2147/Breaks13:3000:50Forum ALet’s explore 6 Postgres database design patterns for an efficient and scalable multi-tenant database for use with a web application. Each pattern will be demonstrated as runnable SQL via psql on a local instance using a narrated and interactive style. Additionally, the code will be available on GitHub so that attendees can run it themselves and provide feedback. Because we’re using community Postgres and no third-party extensions, the design patterns run anywhere Postgres runs, from cloud providers to self-hosted instances. The patterns we’ll look at are: scaling a single large instance, using composite primary keys, logging tenant DML, tracking tenant resource consumption, adding row level security (RLS), and using partitioned tables for multitenancy use cases. These patterns will help teams design their own Multi-tenant databases so they can maximize cost efficiency by consolidating to fewer Postgres instances. As query volume and data sizes grow, teams may wonder whether these patterns will scale. Recommendations on the limitations of each pattern will be presented as well as alternative solutions, so that attendees gain a broader context on managing growth.https://postgresql.us/events/pgconfus2025/schedule/session/1972/SessionAndrew Atkinson13:3000:50Forum BOver the past ten years, we collaborated with many PostgreSQL users and also built several managed PostgreSQL solutions. From users who run their own Postgres deployments, we often hear the question, “I can put Postgres in a container and deploy it. Could you help me with taking backups and HA?” Unfortunately, taking backups isn't as straightforward as using an off-the-shelf backup solution, and setting up high availability involves much more than just creating a hot standby. Running PostgreSQL for others exposed us to a diverse set of lesser-known operational challenges, and we thought this was a good opportunity to talk about some of our learnings. In this talk, we’ll cover lesser-known challenges of not only in backups and high availability but also in security, provisioning, and more. Join me to learn how to make your PostgreSQL deployment truly resilient.https://postgresql.us/events/pgconfus2025/schedule/session/2059/SessionBurak Yucesoy13:3000:50Hub OneWorking on extensions since their inception in version 9.1, they have basically built my career in the PostgreSQL community. From critical CVEs to quick hacks, this talk will go over both the major and minor lessons learned in over a decade of development milestones. Some topic highlights include: * Why even make an extension? * Managing extension upgrades * Managing code organization * Managing multiple PG version support * Managing shortcomings in core PG * Why use an extension to help manage monitoring? * What's still missing?https://postgresql.us/events/pgconfus2025/schedule/session/1989/SessionKeith Fiske13:3000:50Hub TwoPostgreSQL’s extensibility makes it one of the most versatile platforms for modern application development. In this talk, we’ll explore select set of innovations from the past year that take it even further. We’ll start with deep dive into planner and executor improvements that bring significant performance gains for OLTP workloads. Then we’ll dive into fast analytics using pg_duckdb and how we’ve integrated it into Azure Database for PostgreSQL. Next, we’ll cover new GenAI capabilities - including how to build knowledge graphs using semantic operators and the Apache AGE graph extension. Finally, we’ll demo the new features of the VS Code extension for PostgreSQL, including support for graphs and performance analysis and tuning powered by Copilot.https://postgresql.us/events/pgconfus2025/schedule/session/2166/SponsorDivya BhargovMaxim Lukiyanov14:3000:50Forum AMigrating a large fleet of PostgreSQL databases to a managed service is a journey filled with opportunities and potential pitfalls. This is especially true when dealing with numerous multi-terabyte databases and particular cases such as migrating from an old version, using inheritance, PostGIS, moving to GCP Service accounts, and changing database assignment to instances. This session shares the real-world experience of migrating more than 100 PostgreSQL databases, ranging in size from gigabytes to multiple terabytes, from traditional virtual machines to a managed cloud service, specifically Google Cloud SQL for PostgreSQL. We will share lessons learned, covering planning, execution, and problems encountered, with practical advice to save you time, resources, and headaches during a similar migration. Key discussion areas will include tooling selection, pre-migration assessments, choosing the right instance type, optimizing the migration process for large datasets, strategies for minimizing downtime, and essential post-migration validation and performance tuning. This session is a must-attend for anyone considering or currently executing a large-scale PostgreSQL migration to a managed service and wants to learn from real-world successes and challenges.https://postgresql.us/events/pgconfus2025/schedule/session/2020/SessionNelson Calero14:3000:50Forum BLogical Replication has reached a milestone in PostgreSQL, it's more than just for upgrades. Whereas beforehand best practice was to implement High Availability architectures using a PRIMARY->STANDBY replication cluster, one can now implement active-active replication. But with this approach comes along new issues that can stall and even break your replication between multiple read-write hosts. This session covers numerous kinds of replication stalls and their mitigation. And by following a step by step approach, conflict resolution can be achieved as you to identify and resolve potential bottlenecks.https://postgresql.us/events/pgconfus2025/schedule/session/2041/SessionRobert Bernier14:3000:50Hub OneIn this session, we'll dive deep into optimizing time-series data management in PostgreSQL, combining partitioning strategies with automated maintenance for enterprise-scale applications. We'll explore practical techniques for designing high-performance time-series tables using declarative partitioning, demonstrating how to achieve optimal write throughput and query performance through strategic partition pruning. Key areas we'll cover include: 1/ Implementing efficient partition key selection for time-series data 2/ Automating partition management using pg_cron for creation and retention 3/ Optimizing bulk data ingestion through parallel query execution 4/ Real-world performance comparisons between partitioned and non-partitioned approaches 5/ Best practices for handling high-velocity data streams while maintaining query responsiveness Through live demonstrations and real-world examples, attendees will learn how to implement a robust, scalable time-series data architecture that can handle billions of rows while maintaining consistent performance. We'll also address common challenges such as partition cleanup, index management, and monitoring strategies specific for PostgreSQL.https://postgresql.us/events/pgconfus2025/schedule/session/1970/SessionDomenico di Salvia14:3000:50Hub TwoThe last two years in technology have been The Years of AI. AI-based tools are appearing everywhere, in everything, doing things that we all thought required human beings. Are DBAs obsolete? Can an AI improve your database performance by 10x just through tuning? Spoiler alert: No. This talk goes over some good and bad uses for AI in database management, and how to tell them apart.https://postgresql.us/events/pgconfus2025/schedule/session/2142/SponsorChristophe Pettus15:2000:40Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2148/Breaks16:0000:50Forum APostgreSQL serves as a cornerstone of OpenAI's backend infrastructure, powering many of our most critical features. In this talk, I will share how we've taken PostgreSQL to the next level to handle OpenAI's demanding workloads. I'll cover the challenges we faced, the lessons learned from outages, and the strategies we implemented to scale PostgreSQL to handle millions of queries per second (QPS). Key topics include: Connection Pooling: How we effectively manage connection pooling with PgBouncer. Read Replication: Strategies for scaling read replicas to meet increasing demand. Schema Migrations and Long-Running Queries: Approaches to seamless schema migrations and resolving performance bottlenecks caused by long-running queries. Observability: Enhancing monitoring and observability to ensure reliability and performance.https://postgresql.us/events/pgconfus2025/schedule/session/1953/SessionBohan Zhang16:0000:50Forum BThe dream of HTAP — Hybrid Transactional and Analytical Processing — promised a unified system that could power both real-time applications and large-scale analytics. But the promise remains largely unfulfilled. As existing systems forced a compromise — delivering OLTP and OLAP that were both suboptimal. - We just want postgres for OLTP. This talk introduces a new architecture for HTAP — one that doesn’t compromise either side. We’ll explore how decoupling OLTP and OLAP, and yet providing a sub-second transactionally consistent link between them, might be the right way to finally achieve the HTAP dream. And how we do it all with in postgres extension system, with pg_mooncake.https://postgresql.us/events/pgconfus2025/schedule/session/2114/SessionZhou Sun16:0000:50Hub OneWhen I first got into software engineering, I thought studying history was a waste of time. I’ve since learned that understanding how people got where they are—and what they learned along the way—is one of the fastest ways to level up. In that spirit of standing on the shoulders of those who came before us, this talk shares lessons from 10 Postgres major contributors about how they became effective Postgres hackers. But the talk isn’t just for contributors. Whether you’re an app developer, DBA, data engineer, someone who’s thinking about becoming a contributor, or just someone who uses Postgres every day, you’ll find takeaways here. This is an updated version of an illustrated talk I gave at PGConf.dev 2025. You’ll hear how these contributors first got involved, where they turned to build their database knowledge, how they learned through code, and—maybe most importantly—the invisible curriculum they had to navigate along the way. This version includes a few new stories, deeper dives, and more time to explore the not-often-discussed skills, habits, and mindset shifts that helped them grow. Even if you never plan to contribute to Postgres, these hacker stories pull back the curtain on how a handful of PG contributors got their start—and what they picked up along the way. Whether you’re early in your career or deep into it, these stories give us all something to think about.https://postgresql.us/events/pgconfus2025/schedule/session/2081/SessionClaire Giordano16:0000:50Hub TwoThere's no ignoring the popularity of terms like DevOps and Platform Engineering to describe high functioning technical organizations. From vision to action, we want our tools and communication channels in the business to reflect a product-first, self-service mentality; while support engineers want to ensure that resources are provisioned and managed appropriately for security, scalability, and accountability. Nowhere do these two ideals seem to come into conflict more often than with databases and other stateful stores. Postgres is famous for its reliability and deep set of tunable parameters, but is also known among engineers as a double-edged sword… The same rich set of parameters can lead engineers to question when and which levers to pull. It seems like tuning the database will always require specialized knowledge, and results in long turnaround times when scaling, tuning, and provisioning the database. You'll learn the core of a DevOps philosophy, and how you can embrace it to reduce barriers between the business and the engineers responsible for maintaining and supporting the data layer. And we'll discuss how and why reality sometimes falls short of the best intentions.https://postgresql.us/events/pgconfus2025/schedule/session/2165/SponsorPhil Vacca16:5001:10Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2149/Breaks08:3001:00Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2154/Breaks09:3000:10Forum ALearn about the United States PostgreSQL Association (PgUS) and how you can get involved. We'll also be awarding the PgUS Volunteer of the Year!https://postgresql.us/events/pgconfus2025/schedule/session/2176/SessionElizabeth Christensen09:4000:20Forum APostgres has proven to be a great building block for the next generation of databases systems at AWS, from forming the core of Aurora DSQL, to being a key part of how we’re thinking about data for agentic applications (and applications built by agents).https://postgresql.us/events/pgconfus2025/schedule/session/2168/SessionMarc Brooker10:0000:30Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2155/Breaks10:3000:50Forum ADatadog operates hundreds of Postgres clusters spread across different cloud providers. Our Postgres usage continues to increase in breadth to more and more individual clusters and in depth as certain clusters grow larger and have more intensive performance needs. Over time, all the little differences between cloud provider offerings developed into major pains for application developers and for database operators. Datadog built a self-managed Postgres platform on Kubernetes in order to alleviate these pains. Our self-managed platform is now our default way of running Postgres and gives us a single foundation to make it scalable with automation, to make it reliable with architecture, and to make it cost effective. In this talk, we’ll cover: * What is good and bad about using cloud-managed Postgres offerings * What does our Postgres on Kubernetes offering look like * How we moved an entire region from 100% cloud-managed to 100% self-managed * What is good and bad about using our self-managed Postgres offering * What is on our platform roadmaphttps://postgresql.us/events/pgconfus2025/schedule/session/2064/SessionNick Canzoneri10:3000:50Forum BHardcoding database credentials in applications or configuration files is a dangerously insecure practice in today's dynamic environments, creating a large attack surface and increasing the risk of credential compromise. It doesn’t matter if they are encrypted, they are still hardcoded in some form. This is further complicated by the need to provide temporary database access to developers and on-call engineers. The increasing frequency of data breaches highlights the urgent need for a more secure solution. This presentation demonstrates how HashiCorp Vault's database secrets engine can secure PostgreSQL credentials. We'll cover generating short-lived, dynamic credentials for applications and release pipelines, and creating temporary, limited-permission accounts for developers and on-call engineers. The talk will also cover configuring PostgreSQL in Vault, creating Vault roles, integrating applications with Vault, and implementing credential rotation and revocation. Real-world examples and best practices will be shared. Implementing this solution offers several key advantages. Primarily, it enhances security by significantly reducing the risk of data breaches through the elimination of long-lived credentials and the implementation of the principle of least privilege. It also helps organizations meet regulatory compliance requirements related to data security and access control. Furthermore, the solution streamlines operations by automating credential management, making it easier to manage, rotate, and audit credentials. This leads to increased developer productivity by providing secure and controlled access to database resources when needed without hindering development workflows. Finally, it reduces operational overhead by simplifying the process of managing database access for both applications and personnel. Note: this is a demonstration of a technical solution, not a sales pitch for HashiCorphttps://postgresql.us/events/pgconfus2025/schedule/session/1957/SessionDwarka Rao10:3000:50Hub OneWhen JSON_TABLES released I thought this would be the greatest thing ever! Several months in, I'm not seeing the hype that I through it would get around the web. Even AI doesn't seem to care about it. I'm hoping to get the most out of my local content based heavily on the metadata stored as JSON. This talk embarks on a journey of learning to accept JSON_TABLES for what they are, powerful but not quite the next big thing. I also highlight the feelings that came from this and exploration and how things could be sweeter.https://postgresql.us/events/pgconfus2025/schedule/session/1987/SessionJay Miller10:3000:50Hub TwoIn the first half of this session, we will take a look at the recently released pg_stat_plans 2.0 extension, which functions similarly to pg_stat_statements, where it shows how often a query executes, but also captures how often a particular type of query plan got executed for a given query. The new pg_stat_plans is similar in spirit to pg_store_plans and the original pg_stat_plans, but offers a low-overhead design by using plan tree hashes (plan IDs) for tracking historic plan use for all executed queries, as well as enabling tracking of plans for queries currently running on Postgres 18. In the second half, we will dive deeper into specific plan problems, like going through the steps necessary to resolve plan regressions (or bad query plans) when they are found, and how tools like pganalyze Query Advisor can suggest better query plans by rewriting query text, tuning settings, or using planner hints.https://postgresql.us/events/pgconfus2025/schedule/session/2163/SponsorLukas Fittl11:3000:50Forum APostgreSQL 18 gained support for using asynchronous I/O (AIO) for reads. This talk will discuss the limitations of AIO in PG 18, the workloads in which it can and can't help,. and by how much it can help. After that we will look ahead to AIO support beyond PG 18.https://postgresql.us/events/pgconfus2025/schedule/session/2079/SessionAndres Freund11:3000:50Forum BToday global regulations around data privacy and sovereignty are getting tighter by the minute, organizations are increasingly required to control not just how data is handled, but where it resides. This presentation explores the critical concepts of data sovereignty and how it can impact the database architecture and compliance with a focus on PostgreSQLhttps://postgresql.us/events/pgconfus2025/schedule/session/2019/SessionTim Steward11:3000:50Hub OneHave you heard the expression, Losing the Forest Through the Trees? In a nutshell it means being so focused on details that we miss the bigger picture. Often in geospatial we are guilty as charged. It is about this tool or that platform vs the important work at hand. Building efficient workflows are an important part of building capacity to hold bigger stories. A powerful workflow I share when building data stories includes multi-faceted postgreSQL. Specifically QGIS and open source integration bringing a series of interconnected issues—including deforestation, pollution, habitat destruction, and biodiversity loss—representing a detachment from ecological systems, exacerbated by not only the energy requirements of exponential AI implementation but global industrial practices that allow environmental harm to be displaced geographically and socially. Quantitative Storytelling portrays an intricate dance between objective reality—Using the city as an operational unit we will build schema from multiple data sources and learn to tell stories about urban form and morphometrics learning from social demographics and the vulnerabilities encountered with climate risk and change.https://postgresql.us/events/pgconfus2025/schedule/session/2004/Sessionbonny p mcclain11:3000:50Hub TwoIn this talk, Maxim Sobolevskiy is going to show what DataGrip is all about, from the basics to more complicated things like AI for SQL, databases in version control, and scripted mechanisms for data export. What will be covered: – Various types of IntelliSense, code snippets, expanding wildcards, and subqueries. – AI Assistant: Text-to-SQL, Fix with AI, and AI-based export. – Data editor: Editable result-sets, bulk and multiple submit, and transposing data. – Data import/export: Import/export from CSV, JSON, Excel, and INSERT statements, and export to other databases. – Navigation: Searching for database objects, data, SQL code, files, settings, and more. – Additional tools: Diagrams, user-defined parameters, and a powerful diff tool. DataGrip is built with one purpose in mind: to save developers from repetitive work. Join this session to find out how well it fulfills that purpose.https://postgresql.us/events/pgconfus2025/schedule/session/2144/SponsorMaxim Sobolevskiy12:2001:10Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2156/Breaks13:3000:50Forum ATable partitioning in PostgreSQL databases offers significant performance and operational advantages, particularly for large datasets. By enabling partition pruning, queries can target only the relevant subsets of data, substantially improving execution times. Maintenance operations such as vacuuming, reindexing, and backups become more efficient, as they can be performed incrementally on individual partitions rather than the entire table. Partitioning also simplifies data lifecycle management by allowing old data to be archived or dropped quickly without costly delete operations. In this session, you will learn how to apply table partitioning to maximize performance of your Postgres database through a real-world example of how we maintain large alternative datasets at Bloomberg.https://postgresql.us/events/pgconfus2025/schedule/session/2013/SessionIvan SayapinYu Lung Law13:3000:50Forum BIf you've ever had an application that needed hundreds (or thousands) (or tens of thousands) of connections to postgres, then you've probably needed a connection pooler. And if you've ever used PgBouncer as your connection pooler, you may have run into some challenges, or problem, or confusing behaviors, or all of the above. In this talk, we will cover what can go wrong, how to fix problems, and how to monitor to keep your database, clients, and DBAs happy.https://postgresql.us/events/pgconfus2025/schedule/session/2139/SessionNicholas Meyer13:3000:50Hub OneYou already know your Postgres, but you're being asked to work with this Kubernetes thing. Do you want to focus your Kubernetes learning on only the bits which are relevant to databases? Then this is the talk for you! Skip the usual 'why run databases on Kubernetes' discussion and dive straight into what you need to know to talk Postgres on Kubernetes. Using a Postgres database hosted on Kubernetes as an example, I will cover the components of Kubernetes involved in day-to-day activities - including network connectivity, storage, and the automation of restarts and upgrades. Join me for an overview of the few, core terms and concepts among the many, many pieces of the Kubernetes ecosystem you need to learn about first, to start your Data on Kubernetes journey.https://postgresql.us/events/pgconfus2025/schedule/session/2065/SessionAlastair Turner13:3000:50Hub TwoAmazon Aurora DSQL is a new serverless, distributed SQL database that uses decades of Amazon innovation and operational excellence. Join Marc Brooker, AWS VP and Distinguished Engineer, as he dives into the design and key innovative technologies that make Aurora DSQL the ideal choice for new applications and applications which require the highest level of resiliency. Learn how active-active architecture in Aurora DSQL delivers single- and multi-Region resiliency and virtually unlimited scale.https://postgresql.us/events/pgconfus2025/schedule/session/2173/SponsorMarc Brooker14:3000:50Forum AWith the power of PostgreSQL, modern data transformation can happen inside the database rather than with external tools. This is known as the extract, load, and transform (ELT) pattern using built-in functions and SQL features to work with data regardless of the form. Once the data is loaded, knowing how to utilize Common Table Expressions (CTEs), recursive CTEs, and CROSS JOINs can significantly improve your data transformation tasks. In this session, I’ll explain and demonstrate how each of these features can be used together to build powerful queries that utilize additional PostgreSQL features and functions. Transforming JSON, text, and even emoji into sources of data-driven insights. By the end of this session, you will understand how to approach data challenges differently, using the power of PostgreSQL and SQL to work more effectively.https://postgresql.us/events/pgconfus2025/schedule/session/2127/SessionRyan Booz14:3000:50Forum BSQL Server to PostgreSQL migrations present a complex array of technical differences that can challenge even experienced DBAs - we've seen this firsthand across hundreds of hours spent with customers managing databases from tens of GiBs to several terabytes. Successfully navigating these migrations requires deep understanding of how critical components like backups, collations, and case sensitivity differ between platforms. In this talk, we'll compress our experience from 500+ hours of migration consulting into a practical guide that helps database administrators (DBAs), developers, and IT professionals understand and overcome these core challenges. We'll focus on four critical areas that consistently emerge as migration roadblocks: * Architectural Differences * Data Storage and Access * Backup Strategy Differences * Collation and Character Set Challenges Target Audience: DBAs and developers planning PostgreSQL migrations. Basic knowledge of both databases helpful but not required. This talk distills our extensive experience into practical, actionable guidance, focusing exclusively on open-source solutions that attendees can implement immediately.https://postgresql.us/events/pgconfus2025/schedule/session/2137/SessionSAMEER KUMAR14:3000:50Hub OnePostgreSQL performance measurement is complex, with metrics like average query runtime often hiding significant variability, particularly when analyzing short time windows or different times of the day. This talk explores the limitations of traditional performance indicators and introduces a novel workload fingerprint method designed for more reliable performance assessment, particularly in live production environments. This fingerprint approach provides a granular view of database activity, aiding both human database administrators and AI-powered tuning systems in understanding and effectively addressing performance tuning. Attendees will gain insights into effective tuning strategies and monitoring techniques, engaging in discussions that span both the technical implementation and philosophical considerations of database optimization, ensuring a comprehensive understanding of the challenges and solutions in maximizing PostgreSQL efficiency.https://postgresql.us/events/pgconfus2025/schedule/session/2120/SessionLuigi Nardi14:3000:50Hub TwoThe PostgreSQL community is known for being fully committed to open-source development and ideology - for good reason. It's brought over 35 years of success to the project, resulting in Postgres being chosen as the ‘Most Desired & Admired Database’ (StackOverflow's Developer Survey) and in 1 out of 3 choose for customer-facing mission critical applications (CIO Postgres High Availability Survey). pgEdge believes in the power of PostgreSQL and everything that has contributed to it. That's why we've provided pgEdge Distributed PostgreSQL entirely under the standard PostgreSQL MIT license and releasing pgEdge Enterprise Postgres, to bring you 100% open source, standard PostgreSQL fully optimized for high availability (for both distributed and non-distributed workloads). What else is included? Hardened logical replication Bundled Kubernetes operator pgEdge Distributed components (such as Spock and related PostgreSQL extensions) All available in native package format for VMs and bare metal, with containers for Kubernetes, OpenShift, and Docker/Docker Swarm, as well as a managed service within pgEdge Cloud (compatible with major cloud providers). Scale from a single instance of Postgres, easily implement high availability with read replicas and Patroni, and ultimately grow to globally distributed PostgreSQL architecture across multiple geographies for extreme high availability and low latency requirements. Move with freedom knowing your infrastructure will grow easily with your organization. We'll be here to support you along the way. Want to know more? Attend this session to learn how to leverage pgEdge Enterprise PostgreSQL to support your operations and scale from any point, whether you're running a single-instance or already hosting a fleet of deployments.https://postgresql.us/events/pgconfus2025/schedule/session/2164/SponsorPhillip Merrick15:2000:40Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2157/Breaks16:0000:50Forum AThe Role of Database Sharding in Advancing Research: As users of the open-source Greenplum database, we faced a significant challenge when Broadcom decided to close the software in May 2024. This decision necessitated finding alternative solutions to replace our Greenplum clusters, which store terabytes of Universe object information essential for ongoing scientific research and discoveries. Our evaluation process focused on two potential replacements: Citus and EDB WarehousePG. This talk presents the comprehensive evaluation and benchmarking results of these alternatives.https://postgresql.us/events/pgconfus2025/schedule/session/2049/SessionJoaquim OliveiraPilar de Teodoro16:0000:50Forum BManaging users, roles, and authentication in Postgres can quickly become complex—especially at scale. In this talk, we’ll explore how we addressed this challenge by integrating Postgres authentication into our platform through a custom proxy with passwordless authentication. We’ll share our motivations, architectural decisions, and implementation strategy—including how we streamlined role management using external identity providers, mapped service and user identities to granular Postgres roles, and eliminated password management entirely for internal users. Whether you're building a multi-tenant SaaS or looking to simplify your authentication model, this talk will offer practical insights and reusable patterns to bring back to your own systems.https://postgresql.us/events/pgconfus2025/schedule/session/1992/SessionJonathan Hinds16:0000:50Hub Onepg_duckdb is a new open-source Postgres extension that makes it easy and fast to run analytics queries straight from within Postgres. You can often use pg_duckdb to speed up your slowest Postgres queries significantly without having to make any changes to your query, data or schema! And if you want to speed up your queries even more that's possible too: pg_duckdb makes it very easy to store some of your data remotely in columnar format in S3, Azure blob storage, MotherDuck and more. In this talk you'll learn: 1. How you can use pg_duckdb to speed up your Postgres queries 2. How pg_duckdb can be so much faster than Postgres for analytics queries 3. How you can utilize remote columnar data storage to make your queries even fasterhttps://postgresql.us/events/pgconfus2025/schedule/session/2088/SessionJelte Fennema-Nio16:0000:50Hub TwoWe'll explore how open source managed PostgreSQL can deliver both superior performance and true portability. By using local NVMe storage instead of network-attached disks, Ubicloud PostgreSQL eliminates the I/O bottlenecks that limit performance in services like RDS, delivering up to 5X better throughput. The deep dive covers how Ubicloud PostgreSQL runs on EC2 instances with automated backups, recovery, high availability, and more, while demonstrating strong performance and uptime. Importantly, we'll demonstrate how the same service maintains compatibility with bare metal installations, enabling easy migration without vendor lock-in. You'll see the architecture that makes this possible and leave with practical insights for improving both PostgreSQL performance and deployment flexibility.https://postgresql.us/events/pgconfus2025/schedule/session/2146/SponsorUmur Cubukcu17:0001:00Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2158/Session08:3001:00Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2159/Breaks09:3000:50Forum AIn the world of relational databases, indexes are crucial for optimizing query performance. PostgreSQL, a powerful open-source database system, offers a variety of index types beyond the standard B-tree. This presentation delves into the different index types available in PostgreSQL, exploring their strengths, weaknesses, and ideal use cases. Specifically, we will cover and show use cases (using actual explain plan output) for the following types of indexes: - B-tree Indexes - Hash Indexes - GIN /GiST Indexes - BRIN Indexes - BLOOM - Vector Indexeshttps://postgresql.us/events/pgconfus2025/schedule/session/2082/SessionShane Borden09:3000:50Forum BAI applications are changing how we use databases, and vector data is at the center of this change. By enabling semantic search within domain datasets, vectors allow for more intelligent and nuanced queries. But managing this data effectively requires understanding the challenges of vector size and its impact on storage and performance. This presentation explores practical strategies for storing, indexing, and querying vector data in operational databases and different factors impacting performance and management. Learn how to optimize your database for AI-powered applications and unlock the full potential of vector search.https://postgresql.us/events/pgconfus2025/schedule/session/2053/SessionGleb Otochkin09:3000:50Hub OnePostgreSQL has a very sophisticated role privilege system that can control access to database objects on a very granular level… if you use it properly! This talk is an introduction to the user and role system in PostgreSQL, how to configure it, how to set up secure defaults, and how to use it for both database login and granular control over access once a user is logged in.https://postgresql.us/events/pgconfus2025/schedule/session/1973/SessionChristophe Pettus09:3000:50Hub TwoAiven is home to several Open Source data solutions, and choosing the right tool for the job is often the biggest challenge for startups. With our upcoming startup program, we're diving deeper into the question: "Is PostgreSQL your 'one stop shop', or should you use dedicated tools for dedicated solutions?" In this talk, we'll explore the benefits and tradeoffs of an "all PostgreSQL" solution as told through demos, workshops, and customer stories.https://postgresql.us/events/pgconfus2025/schedule/session/2169/SponsorJay Miller10:2000:40Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2160/Breaks11:0000:50Forum AAt Okta/Auth0 we have a wide range of different state and persistence dedicated systems. These range from regular key-value stores like Redis, a large fleet of NoSQL databases of the likes of DynamoDB and MongoDB, and ever larger and more predominant usage of PostgreSQL for a wide majority of new and old use cases. This talk is all about the operational challenges of managing a fleet of 650+ Postgres database clusters, spread across different cloud providers and regions, and in a variety of diverse use cases, with different operational profiles to each of them. We will uncover a range of operational challenges such as: * Database operational procedures: backups/restore, version management, data retention and observability for 650+ clusters * Sizing: when and how to scale up individual clusters * Same database, different use cases: full text search vs basic CRUD vs spike traffic management are handled from a database perspective and what issues/challenges we encounter dealing with non-predictable problems. * Same database engine, different deployments: tactics and deployment practices on how to handle multi and single tenant deployments, the performance and operational implications of such scenarios. * Database Topology: how our clusters are designed per client and resiliency needs in mind. What I would like the attendee to get from this session: * A good set of live production examples and lessons learned on managing a large postgres fleet on a variety of different constraints. * How different use cases, combined with single vs multi-tenant deployments can drive your database optimisation efforts * What and how to handle performance issues at scale due to unexpected load or cascading effects of "bad" releases. * How to build for failure and where databases take an important role on such an architecture. This is an operational focussed talk were we will cover on a lot ground on lessons learned, where a natural comparison between different database engines will be used for highlighting the how and why we are using PostgreSQL and where we are heading in terms of broader adoption of the technology @auth0https://postgresql.us/events/pgconfus2025/schedule/session/1947/SessionNorberto Leite11:0000:50Forum BThis talk uses the recently committed eager scanning feature as a case study to walk through the Postgres feature development process. It’s meant to give prospective contributors a realistic look at what it takes to get a feature committed and to help users better understand how Postgres evolves and how they can benefit from these changes. Anti-wraparound vacuums are one of the most consistent Postgres user complaints. This talk explains how freeze debt accumulates, how it varies based on table access patterns, and how Postgres attempts to manage it. Eager scanning started as a proposal on pgsql-hackers to preemptively freeze modified pages using per-table access pattern statistics. Over years of discussion and experimentation (including evaluating and discarding many empirical models and approaches), the shape of both the problem and solutions evolved. This talk dives into what made designing an adaptive feature in Postgres so difficult: extreme constraints, second-order effects, code complexity, and the sheer variety of workloads Postgres must support. The final committed feature is simpler than the original idea: it eagerly scans all-visible but not all-frozen pages to amortize the cost of aggressive vacuums. It doesn’t try to predict access patterns or freeze lifespan, but it’s effective, maintainable, and is a step toward addressing the core problem. Finally, the talk will explain how users can expect this change to reduce the frequency and impact of anti-wraparound vacuums in their own workloads.https://postgresql.us/events/pgconfus2025/schedule/session/2080/SessionMelanie Plageman11:0000:50Hub OneThis comprehensive guide explores PostgreSQL's built-in statistics collection system, focusing on the powerful pg_stat views. Learn how to leverage these views to gain deep insights into database performance, including I/O patterns, query execution statistics, and resource utilization. This talk will also cover PostgreSQL 18's upcoming features and recent enhancements to the statistics system, providing database administrators and developers with essential tools for maintaining optimal database performance. We will cover about enhancements to pg_stat_statements, pg_stat_checkpointer, etc.https://postgresql.us/events/pgconfus2025/schedule/session/2051/SessionChirag DaveSami Imseih11:0000:50Hub TwoPostgreSQL has earned its place as a powerhouse relational database, but as application demands increase—especially in real-time analytics, high-throughput systems, and hybrid transactional/analytical workloads—traditional scaling approaches can hit cost and complexity walls. This talk introduces a practical, non-invasive strategy to scale PostgreSQL horizontally and reduce query latency using GridGain as an in-memory computing layer. We’ll explore how GridGain integrates with PostgreSQL to act as a distributed SQL engine and caching layer, enabling low-latency access to hot data and high-performance distributed joins—without replacing your database. Attendees will learn how to offload expensive queries, perform real-time analytics across large datasets, and scale out read-heavy workloads without resorting to sharding or complex replication schemes. Through architecture diagrams, performance benchmarks, and real-world use cases, this session equips PostgreSQL users with a modern, JVM-based approach to meet growing performance demands—while keeping PostgreSQL at the core of their stack. Target Audience Developers, DBAs, architects, and platform engineers working with PostgreSQL who are exploring strategies to scale read-intensive workloads, enable real-time analytics, or integrate in-memory processing into their systems.https://postgresql.us/events/pgconfus2025/schedule/session/2167/SponsorOscar M Herrera12:0000:50Forum AUpgrading a 200TB PostgreSQL database? Let's just say it's an adventure! In this session, I'll walk you through a real-world upgrade of a massive PostgreSQL setup. It wasn't always smooth sailing, and we definitely learned some hard lessons along the way. We'll dive into the nitty-gritty of moving from PostgreSQL 13 to 17, focusing on how we kept downtime to a minimum – because 24/7 is the name of the game, right? Data integrity was paramount, and we'll share the strategies we used to ensure everything stayed rock solid. Expect to hear about the real-world challenges we wrestled with, from wrangling huge data migrations to fine-tuning performance and battling extension compatibility. Come to this session to get the inside scoop on: * Planning a PostgreSQL upgrade when "large-scale" is an understatement. * Practical, battle-tested techniques for cutting downtime. * Performance tricks and optimization for truly massive databases. * Testing and validation methods that actually work under pressure. * The key takeaways – the stuff we wish we knew before we started – that you can use for your own upgrades, no matter the size. If you're a DBA, architect, or anyone who wrestles with keeping large PostgreSQL environments running smoothly through upgrades, this session is for you. Especially if "large datasets" and "always-on" are part of your daily vocabulary.https://postgresql.us/events/pgconfus2025/schedule/session/1958/SessionVibhor Kumar12:0000:50Forum BAlloyDB Omni is a Google provided version of PostgreSQL designed to be run “awywhere”. Even though it’s provided by Google, it can be run on-premises, in any cloud, or in a cloud-hybrid configuration. But what does this flavor of PostgreSQL add? What special features and functionality has Google included that aren’t available in any other PostgreSQL distribution? And what are the limitations that DBAs and Developers should be aware of? This technical presentation covers all of the details that Developers and DBAs need to get started with AlloyDB Omni. Including required prerequisites, how it is installed, and how it is managed. And of course, an overview of how you can move your data in and out and which backup, replication, and HA/DR tools are compatible.https://postgresql.us/events/pgconfus2025/schedule/session/1999/SessionSimon Pane12:0000:50Hub OneWe’ve moved from buzzing pagers to polished SRE dashboards, yet incident response is still largely *reactive*: collect signals, page a human, hope they’re sharp at 3 a.m. In this talk, I’ll explore what happens when we flip that script. Shifting from reactive alerts, to proactive self-healing in the form of an open-source agent for PostgreSQL. - **Signals → Playbooks.** `pg_stat_statements`, cloud metadata and more flow into an LLM-driven engine that selects an executable playbook. Each playbook defines diagnostics, safe-change limits, rollback steps, and can be tuned to your business needs. - **Pluggable tooling.** Need `git` history, Sentry error context, a read-only peek at another Postgres cluster, or live Datadog metrics? Plug in the matching MCP server—or one you build yourself—and the agent can call it instantly. - **Customizable evals.** Built-in evaluation hooks spin up a shadow workload or staging clone so every `CREATE INDEX`, `VACUUM`, or config tweak proves its worth before rolling out to production. - **Trust & approval.** The agent can run arbitrary SQL, but approval gates let you decide which actions are fully automated and which still need a human click. As the head of product at Xata, I’ll highlight real-world examples across Xata’s fleet of serverless Postgres instances and map out where we think agents like this will go next. Bring your skepticism and leave with a PostgreSQL agent you can POC on Monday.https://postgresql.us/events/pgconfus2025/schedule/session/2050/SessionAlex Francoeur12:0000:50Hub TwoIn today's fast-paced, data-driven world, organizations must quickly and efficiently unlock insights from their data to stay competitive. This is where Google Cloud's powerful combination of databases and AI comes in. Explore how businesses can leverage industry-leading capabilities across Vertex AI, the Gemini foundation models, and AlloyDB AI to bring powerful generative AI directly to their data. Discover how AlloyDB's advanced vector search features, which are natively integrated with PostgreSQL, simplify the development of sophisticated AI applications. Learn how to unlock the full potential of your data and drive innovation by unifying your transactional, analytical, and AI workloads on a single, high-performance platform.https://postgresql.us/events/pgconfus2025/schedule/session/2171/SponsorCody Fincher12:5001:10Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2161/Breaks14:0000:50Forum AAmazon Relational Database Service (Amazon RDS) operates a variety of large, very-high-throughput PostgreSQL databases in unusual architectures. Apart from performance challenges over the years, we've been investing into disaster recovery capabilities of our Postgres databases and would like to share our experience. In this technical session, we'll examine our comprehensive approach to disaster prevention and recovery, sharing insights from managing business-critical database operations at a high scale. This session covers: * Recovery strategies from logical unavailability caused by database overload (monitoring and resource management) * Recovery from storage, hardware, or networking failures (topics: MAZ, read-replicas) * Addressing logical data corruption (topics: PiTR, delayed replicas, change-log, pg_dump and backups) * Patterns that enhance resilience and prevent disasters from happening (topics: preventive triggers, access control, db row-level signatures) * Developing and maintaining operational readiness in the team (automated testing and DR trainings)https://postgresql.us/events/pgconfus2025/schedule/session/2063/SessionAlisdair OwensAndrei Dukhounik14:0000:50Forum BIn late 2023, the Java community started a challenge to find the most efficient way to process a file with 1 billion rows of data. Unsurprisingly, many database communities quickly took on the same challenge with varying results. Postgres, in many cases, performed the worst without close attention to settings and efficient resource utilization. But, with a little more effort, could it compete head-to-head? In this session, we’ll look at the original challenge and how to approach it with vanilla Postgres beyond the basics. Next, we’ll explore how the increasingly popular in-memory analytics database, DuckDB, handles the same challenge. Finally, we’ll explore recent opportunities to integrate the two databases together to provide a powerful analytical engine with Postgres for the best of both worlds.https://postgresql.us/events/pgconfus2025/schedule/session/2125/SessionRyan Booz14:0000:50Hub OneMy presentation "Explaining the Postgres Query Optimizer" covers the details of query optimization, optimizer statistics, joins, and indexes. This talk covers 40 other operations the optimizer can choose to handle complex queries, large data sets, and to enhance performance. These include merge append, gather, memoize, and hash aggregate. It explains their purpose and shows queries that can generate these operations.https://postgresql.us/events/pgconfus2025/schedule/session/1948/SessionBruce Momjian14:0000:50Hub TwoThis talk will explore the ins and outs of Transparent Data Encryption (TDE) and discuss various reasons, technical and non-technical, to implement TDE. Kyle will demonstrate solutions to problems that may arise.https://postgresql.us/events/pgconfus2025/schedule/session/2174/SponsorKyle Avants15:0000:50Forum ALogical replication in PostgreSQL is rapidly transforming, opening up new possibilities for data management and distribution. This talk will explore the anticipated future direction of this crucial feature, emphasizing how upcoming capabilities will empower users in their daily workloads. We will also provide a concise overview of logical replication's evolution and highlight significant features from recent releases, illustrating its growing power and flexibility.https://postgresql.us/events/pgconfus2025/schedule/session/2015/SessionAmit Kapila15:0000:50Forum BVery early in Jazz music history, New York became the center of its development, shaping its own style. Nowadays, the Jazz scene is still alive and swinging. Let us now imagine that you have to develop an application where you need to store information about Jazz history and the current events around the New York Jazz scene. How would you store the data? How would you organize it? Which data types would you use? And most importantly, which types of questions will your application be able to answer from that data? In this talk we will go through schema modeling having a problem as a starting point. We will study the guarantees that the database can provide, and what is better done at the application level. We will not only discuss data types but also normalization and some interesting extensions.https://postgresql.us/events/pgconfus2025/schedule/session/2132/SessionBoriss Mejias15:0000:50Hub OneRelational databases are typically designed for clarity, scale and maintainability. However, oftentimes databases are accessed from a single application. Over the course of observing how the primary application accesses the database, a common set of patterns can emerge to the DBA. For example, the “location” table is an insert only table, or the airport table is frequently referenced but rarely modified. Given such observations can the database be significantly optimized in such a way that takes advantage of these access patterns? It turns out in Postgres the answer is yes. Such optimizations are possible through Table Access Methods (TAMs) and Index Access Methods (IAMs). TAMs are in effect the answer to what other open source databases refer to as storage engines. The ability to introduce TAMs and IAMs to a Postgres Database has been continually evolving and becoming more understood over the years. With Postgres 18 there is a new level of plugability that extension developers can take advantage of in the Index Access Method API. This talk will cover the specifics of this extensibility and identify what is possible. Some open source TAMs and some commercially available TAMs will be presented in this talk. The concept of optimizing the database based on application access patterns is not new. Some history of how this has been accomplished in the past will be discussed. In addition, proposals for how Postgres could evolve around this concept will be presented.https://postgresql.us/events/pgconfus2025/schedule/session/2094/SessionTom Kincaid15:0000:50Hub TwoDiscover how Redgate’s free tools, pgNow, pgCompare Community, and Flyway Community, help Postgres users monitor performance, compare schemas, and deploy changes with confidence. This fast-paced demo shows how you can streamline workflows, reduce risk, and bring DevOps discipline to your database development.https://postgresql.us/events/pgconfus2025/schedule/session/2172/SponsorAdam Britt15:5000:10Otherhttps://postgresql.us/events/pgconfus2025/schedule/session/2162/Session