Last Friday’s webinar, “What I Wish I Knew as a New DBA: as told by veterans,” got more questions than we had time for. That’s a good sign: curious DBAs become good DBAs. So grab your coffee (or your third coffee, no judgment), and let’s work through them.
One housekeeping note: two of you asked almost exactly the same question about “everything looks normal but users are complaining.” Great minds think alike, so I’ve answered it once below.
Here’s the secret: you don’t need to challenge the person. You need to question the change. That’s much easier, and much more effective.
Start with curiosity rather than confrontation. “Help me understand how this behaves on the orders table with 400 million rows” lands far better than “This will break production.” Questions invite people to think with you, and very often the developer spots the problem themselves while answering.
Then bring evidence:
A senior engineer may overrule your opinion, but it’s much harder to overrule a query plan showing a sequential scan across the whole table.
Offer an alternative, not just a red flag.
or
turns you from a blocker into a collaborator. And always ask the question every veteran asks:
Nobody is offended by that one.
Finally, remember that protecting the database is your job. The team hired a DBA precisely so someone would raise these concerns. Speaking up isn’t overstepping; staying quiet would be.
Teams don’t lose confidence in people who say “I don’t know yet.” They lose confidence in people who go silent, guess wildly, or pretend.
The magic formula is: what we know, what we’re doing, and when you’ll hear from me next. For example:
“Checkout queries started slowing at 10:42. I’ve ruled out CPU and connection limits. I’m now checking lock contention and recent deployments. I’ll update you by 11:15, sooner if I find something.”
Notice what that message does. It shows you’re methodical, it gives people something concrete, and it promises a next update, which stops the anxious “any news?” pings. Then keep that promise, even if the update is “still investigating, here’s what I’ve ruled out.”
And escalate early. Pulling in a senior colleague after fifteen minutes isn’t weakness; it’s good incident management. Veterans don’t know everything either. They’ve just become very comfortable saying “let’s find out.”
A good rule of thumb: learn deeply whatever can lose/corrupt data or take down production. Learn on demand whatever makes things nicer, faster, or fancier.
Your “know it cold” list should include backup and recovery (including actually restoring, and point-in-time recovery), MVCC and how vacuum and autovacuum work, WAL and checkpoints, locking behaviour, reading EXPLAIN output and indexing fundamentals, roles and permissions, and replication basics. These come up at 2 a.m., when there’s no time to read the docs from scratch.
Your “learn when the situation calls for it” list includes things like specific extensions, foreign data wrappers, advanced partitioning strategies, logical replication edge cases, and the finer tuning parameters. It’s enough to know they exist and roughly what problem they solve, so you know where to look when the day comes.
Think of it as being T-shaped: broad awareness across the ecosystem, with a deep foundation in the parts that keep data safe and systems running.
Knowing PostgreSQL is knowing how the engine works. Being a good DBA is knowing how the engine fits into a business, a team, and a very bad Friday.
A great DBA tests backups before they’re needed rather than assuming they work. They stay calm when everyone else is panicking, and that calm is contagious. They communicate risk in language managers understand (“this migration could lock the payments table for about 20 minutes during business hours”) rather than burying people in jargon. They write things down: runbooks, change notes, post-incident reviews. And they’re quietly paranoid in the most useful way, always asking “what could go wrong, and how would we recover?”
Technical skill gets you through the door. Judgment, communication, and ownership are what make people say, “We need that DBA on this project.”
First, a deep breath. Nearly every veteran has a story like this, even if theirs didn’t go all the way. Now, honestly: the odds of full recovery are low, but not zero, and what you do in the next few minutes matters a lot.
If PostgreSQL is still running, do not stop or restart it. On Linux, deleted files that are still held open by a process continue to exist on disk until that process closes them. Look under /proc/<pid>/fd/ for PostgreSQL’s processes and you may see entries marked (deleted). Those can sometimes be copied out to another disk. This rarely gives you a complete, consistent cluster, but it may rescue some files. Get experienced help before relying on what you salvage.
Stop all writes to that filesystem immediately. Every new write can overwrite the blocks where your deleted data still physically sits. If possible, unmount it or remount it read-only, and never write recovery output back onto the same disk.
Try filesystem-level recovery. Results vary a lot by filesystem; XFS in particular is much harder. Work from a disk image rather than the original if you can.
And once the dust settles, turn this into the most valuable lesson of your career: set up automated backups with a tool like pgBackRest or Barman, archive your WAL, and, most importantly, practise restoring regularly. A backup you’ve never restored is just a hopeful rumour.
Ah, the classic mystery! Averages are wonderful liars. Your dashboard says “fine” while one in fifty users is having a miserable time.
Start by getting specific about the complaint. Which users? Which screen or feature? Since when? All the time or at certain hours? “The app is slow” is a feeling; “saving an invoice takes 12 seconds since Monday afternoon” is a lead.
Then look at what’s waiting rather than what’s working. A system can have low CPU while sessions sit blocked. Check pg_stat_activity for wait_event values and sessions stuck in idle in transaction, and look at pg_locks for blocking chains. One forgotten open transaction can quietly hold up everything behind it.
Look at tail latency, not just averages. In pg_stat_statements, compare mean_exec_time with max_exec_time and stddev_exec_time. A query that’s usually 5 ms but sometimes 8 seconds tells a story. A plan change after an ANALYZE or a data growth spurt is a common culprit, and auto_explain can catch it in the act.
Check what sits between the user and the database. If you use PgBouncer, clients may be queueing for a connection (look at cl_waiting in SHOW POOLS) while the database itself looks relaxed. Network latency, DNS, load balancers, and the application’s own connection pool are all fair game.
Check storage latency rather than just throughput. iostat -x showing high await times can make everything sluggish while CPU stays calm. Also look for checkpoint spikes, heavy temp file usage, table bloat, and autovacuum struggling to keep up. If some reads go to a replica, check replication lag too; users may be seeing stale data and calling it “broken.”
And sometimes, after all that, the answer is: it isn’t the database. That’s a perfectly good finding. Showing evidence that the database is healthy helps the team look in the right place, which is a real contribution.
This one is as much about people as it is about PostgreSQL, and it’s worth getting good at.
Start by understanding what’s behind the resistance. Often it isn’t stubbornness but a concern nobody has addressed: cost, downtime, a bad past experience, or fear of change. Ask what worries them about the recommendation, then listen properly.
Speak their language. Instead of “we need to upgrade from an end-of-life version,” try “the current version no longer receives security fixes, which puts customer data at risk and may matter for your compliance audit.” Tie technical recommendations to things they already care about.
Offer options rather than ultimatums. “Here’s the ideal fix, here’s a lower-cost middle ground, and here are the risks of doing nothing” gives them control, and people accept decisions more readily when they’ve had a hand in shaping them.
Finally, if they still decide against your advice, respect that it’s their call, but document it clearly and politely: what you recommended, what risks you identified, and what was decided. That protects everyone, and it often means that when the risk does show up, they’ll come back to you with a lot more trust.
Thank you to everyone who joined and asked such thoughtful questions. If there’s one thread running through all of these answers, it’s this: great DBAs aren’t the ones who never make mistakes or always know the answer. They’re the ones who stay curious, communicate clearly, protect the data, and keep learning.
Got a follow-up, a war story, or a question we didn’t cover? Do let us know. We’d love to keep the conversation going. And please, before you do anything else today, go check that your backups are actually restored.
Talk Title: PostgresML: Revolutionizing Machine Learning with SQL
In today’s data-driven world, organizations often struggle with complex machine learning infrastructures and data movement challenges. This talk introduces PostgresML, a game-changing PostgreSQL extension that brings machine learning capabilities directly into your database. We’ll explore how PostgresML enables developers and data teams to perform sophisticated ML operations using familiar SQL commands, eliminating the need for separate ML systems. Through live demonstrations, we’ll showcase practical implementations of model training, real-time predictions, and GPU acceleration features. Whether you’re a database engineer, ML practitioner, or technical lead, you’ll learn how to leverage PostgresML to simplify your ML pipeline, enhance security, and accelerate deployment. Join us to discover how this innovative tool is bridging the gap between traditional database operations and modern machine learning workflows.
Talk Title: Developers are decision-makers now. DevRel gets you there faster
DevRel as a role has existed since the 1990s, yet it remains one of the least understood roles in tech. Whether due to changing definitions, role titles, or evolving industries, DevRel has transformed significantly over the past few years—yet it continues to shape the devtool landscape. Since 2023, we’ve seen explosive AI growth alongside a surge in tech companies and technical talent. But who reaches these developers? Developers distrust traditional marketing. Who builds the samples, docs, tutorials, and SDKs they rely on? DevRel has become more critical than ever, especially as developers increasingly become decision-makers. In this talk, we’ll explore what DevRel is, how it drives impact, and how you can build an effective DevRel program.
Talk Title: DPDPA(Digital Personal Data Protection Act) Unleashed – Why It Matters for Women in Data
India’s Digital Personal Data Protection Act (DPDPA) is reshaping how organisations collect, store and use personal data, with a phased, 18‑month rollout. This presentation explores what’s in policy and law, then dives into what it unlocks for careers in data, security and consulting—especially for women. As data architect ,designing database architectures, will try connect legal constructs (Data Principals, Fiduciaries, Consent Managers, the Board) to real-world data and database practices, and show how DPDPA can be a powerful career accelerator, not just a compliance requirement.
Talk Title: Where Technology Meets Customer Needs: Lessons from a Newbie Solutions Engineer
When I stepped into the world of open-source databases as a Solutions Engineer, I expected to feel overwhelmed, but I found a role that made surprising sense. In this talk, I’ll share my journey navigating PostgreSQL with the help of modern cloud platforms like Aiven and DigitalOcean, tuning tools like DBtune, and migration partners like Hexacluster. This isn’t a deep-dive into internals, it’s a practical, beginner-friendly session to reducing the friction of managing PostgreSQL in real-world environments. Along the way, I’ll highlight the often-overlooked role of a Solutions Engineer: the human bridge between customer needs and engineering solutions. If you’re a student, a DBA, a DevOps engineer or just Postgres-curious, you’ll walk away with not only tools to explore, but also a career path to consider.

Talk Title: Architecting Ethical and Responsible AI with PostgreSQL 18
Have you ever developed an Agentic AI application using an agentic framework such as langGraph and pgai extension and noticed you don’t get good results during testing or the results are biased towards a demographic. You don’t know what to do. Organizations developing Agentic AI applications using an agentic framework such as LangGraph and pgai extension often encounter issues during implementation and testing, including suboptimal performance or bias in results such as demographic bias. Identifying the root causes of these issues can be difficult without proper tools and methodologies. This session addresses these challenges by introducing Responsible AI interpretability and explainability techniques. Participants will learn how to understand and trace the model’s decision-making process, enabling them to identify why specific results are generated. These capabilities are essential for meeting compliance requirements in regulated sectors, including banking and insurance. Attendees will gain practical knowledge on building Agentic AI applications that incorporate Responsible AI principles, ensuring transparent, accountable, and fair outcomes.
Rumi ![]()
Talk Title: New features of PostgreSQL 18
PostgreSQL 18 continues the PostgreSQL project’s long-standing focus on performance, scalability, reliability, and developer productivity, building incrementally on the improvements delivered in PostgreSQL 15–17.
Rather than introducing disruptive changes, PostgreSQL 18 is expected to emphasize refinement and maturity across core subsystems such as query execution, indexing, concurrency, replication, and observability, making PostgreSQL even more suitable for enterprise-scale and cloud-native workloads.
Talk Title: Platform Engineering Unpacked: Architecture, Evolution, and Hard-Won Lessons
The way engineering teams build and deliver software has changed dramatically. We’ve moved from manual server setups to automated pipelines, from ticket-based operations to self-service workflows, and from siloed teams to platform-driven organisations. This shift gave rise to Platform Engineering, a discipline focused on creating the internal systems, golden paths, and tooling that empower developers to move faster with less friction.
In this session, I’ll walk through the evolution that brought us here and why Platform Engineering has become a strategic priority across industries. I’ll share the architecture patterns that define successful platforms, how self-service emerges as a core capability, and the practical dos and don’ts learned from building real-world internal platforms.
Attendees will gain a clear understanding of:
Why DevOps wasn’t enough, and what Platform Engineering solves
The natural evolution from scripts → automation → abstractions → platforms
What makes a good platform (and what absolutely doesn’t)
How to design developer-centered systems and golden paths
My firsthand lessons from enabling engineering teams at scale
This talk gives a foundational, experience-driven view of what Platform Engineering really means today and how teams can start their journey the right way.
Our idea explores the implementation of AI-driven query optimization in PostgreSQL, addressing the limitations of traditional optimization methods in handling modern database complexities. We present an innovative approach using reinforcement learning for automated index selection and query plan optimization. Our system leverages PostgreSQL’s pg_stat_statements for collecting query metrics and employs HypoPG for index simulation, while a neural network model learns optimal indexing strategies from historical query patterns. Through comprehensive testing on various workload scenarios, we will validate the model’s ability to adapt to dynamic query patterns and complex analytical workloads. The research also examines the scalability challenges and practical considerations of implementing AI optimization in production environments.
Our findings establish a foundation for future developments in self-tuning databases while offering immediate practical benefits for PostgreSQL deployments. This work contributes to the broader evolution of database management systems, highlighting the potential of AI in creating more efficient and adaptive query optimization solutions.
This talk provides an introductory overview of Artificial Intelligence (AI) and Machine Learning (ML), exploring key concepts and their application in building intelligent systems. It will highlight the essential AI/ML techniques, such as supervised and unsupervised learning, and discuss practical use cases in modern industries. The session also focuses on how PostgreSQL, with its powerful extensions like PostgresML, TimescaleDB, and PostGIS, supports the development of AI-powered applications. By leveraging PostgreSQL’s ability to handle complex datasets and integrate machine learning models, participants will learn how to build scalable, intelligent solutions directly within the database environment.
Success is a multiplier of Action, External Factors and Destiny.
Out of these three, the only controllable aspect is our action. Again, action is the result of our EQ, IQ, SQ, and WQ (Willingness Quotient) together.
We all want to be successful and keep trying to motivate ourselves with external factors. We read inspirational books, listen to great personalities, and whenever possible upgrade ourselves with more knowledge and the list goes on.
Indeed these are excellent motivators, but in this process, we forget the most important source of energy, YOU!
We read other stories to feel inspired, thinking “I am not enough!”
But, the day we start accepting ourselves, introspect, understand, and align our life purpose with our routine, we find the internal POWER. This is a continuous source of motivation and energy which we need at down moments. When we feel, lonely, stuck and seek help, our inner voice is the greatest companion.
But, how many times do we consciously think about our “Subconscious”?
“Journey to Self” is our structured coaching program where we take back focus from the outside and delve deep inside to find our inner strength. Focusing on self-acceptance and personal growth
I believe everyone has POWER within them!
Let’s be the POWERHOUSE!
Human, AI, and Personalized User Experience for DB Observability: A Composable Approach
Database users across various technical levels are frequently frustrated by the time-consuming and inefficient process of identifying the root causes of issues. This process often involves navigating multiple systems or dashboards, leading to delays in finding solutions and potential downstream impacts on operations.
The challenge is compounded by the varying levels of expertise among users. It is essential to strike the right balance between specialized and generalized experiences. Oversimplification can result in the loss of critical information, while an overwhelming amount of data can alienate certain users.
Developers and designers are constantly navigating these trade-offs to deliver optimal user experiences. The integration of AI introduces an additional layer of complexity. While AI can provide personalized experiences within databases, it is crucial to maintain user trust and transparency in the process.
The concept of personalized composable observability offers a potential solution. By combining the strengths of human expertise, information balance, and AI-driven personalization, we can create intuitive and user-friendly experiences. This approach allows users to tailor their observability tools and workflows to their specific needs and preferences.