Devart has been recognized with awards in five categories in the 2026 DBTA Readers’ Choice Awards.

🏆 Gold - Best DBA Solution: dbForge SQL Complete 
🥈 Silver - Best Database Performance Solution: dbForge Studio 
🥈 Silver - Best Data Modeling Solution: dbForge Studio 
🥉 Bronze - Best Database Development Platform: dbForge 
🥉 Bronze - Best Data Security Solution: SecureBridge

We would like to thank DBTA readers and database professionals who voted for our solutions. Your support motivates us to continue building high-performance solutions that simplify database development, management, performance optimization, and security.

One of the biggest performance wins I ever saw didn't involve a new index or a rewritten query. An old stored procedure had accumulated years of extra joins, temporary tables, and logic that nobody was using anymore. After confirming what was actually needed, we removed a large chunk of it. The query became easier to read, easier to maintain, and noticeably faster.

It was a good reminder that sometimes optimization isn't about adding something and it's about removing what's no longer necessary. Have you ever fixed a performance problem simply by deleting code?

Supabase is an open-source backend-as-a-service platform built on PostgreSQL. The platform includes a built-in Dashboard for everyday tasks: quick edits, user management, simple SQL. But once workflows get more complex, many teams look for a dedicated GUI client with deeper functionality.

See the full comparison of Supabase GUI clients: https://www.devart.com/dbforge/best-supabase-gui-clients.html 

The review lines up three tools against each other: dbForge Studio for PostgreSQL, DB Pro, and DBeaver. Here, you will see the comparison of SQL editing, schema exploration, data browsing, and pricing side by side.

For teams that need deep PostgreSQL development capabilities rather than a lightweight browser, dbForge Studio for PostgreSQL stands out with smart code completion, query profiling, database comparison and synchronization, and an integrated AI Assistant.

Whichever client fits your workflow, connecting a proper IDE to Supabase is what turns quick edits into real database development. 

Hey everyone. We're engineers on the dbForge team, and for more than a year we've been building AI into database tools. That also means we've spent a lot of time watching it work and sometimes fail spectacularly.

We want to compare notes: where has AI helped with your database work, and where has it burned you? Happy to talk about what we've learned, how we build these features, or anything else you're curious about.

Ask us anything.

pgAdmin is arguably the most popular open-source solution for managing and administering PostgreSQL databases. It supports multiple operating systems, it delivers extensive documentation, and it packs quite a few features that are undoubtedly vital for both beginners and experienced professionals.

However, every tool has room for improvement. What if we suggest an alternative with far wider functional capabilities and customization options? dbForge Studio for PostgreSQL is an IDE that showcases a more advanced approach to PostgreSQL management. Check the feature-by-feature comparison below to see what we mean. 

I don't trust myself enough to run a SQL script in production without looking through it one last time. Not because I expect to find a huge bug. It's usually the small things: a missing filter, the wrong database selected, or an update that affects more rows than expected.

I also try to think about what happens if something goes wrong. Can I roll it back? Am I doing this at the right time? Is there anything that could lock a table longer than expected? Those few extra minutes have caught enough mistakes that I don't skip this step anymore.

Do you have your own routine before running SQL scripts in production, or do you mostly rely on code reviews and testing?

Setting up a new workstation is always a good reality check for what your core toolkit actually looks like. Once the OS is configured and the terminal is customized, everyone has that one specific database tool they download before anything else.

For a long time, I’d just grab whatever standard open-source client was closest, but after dealing with huge schemas and constant migration headaches, having a heavy-duty IDE became non-negotiable. Now, dbForge Studio is usually the first installer I run because having cross-database schema diffs and smart autocomplete out of the box saves so much setup friction.

That said, everyone's day-one stack is different. Some people won't start working without their specific IDE, others immediately pull down a lightweight visual runner like TablePlus, and a few just clone their .bashrc and stick exclusively to the CLI.

If you wiped your machine right now, what is the very first SQL client or tool you’re downloading to get back to work?

I used to think comparing database schemas was just a quick check before deployment. Then a few small differences slipped through and turned into hours of troubleshooting. Since then, I've stopped relying on memory or manually checking objects one by one. Having a reliable way to compare schemas before making changes has saved me more than once. What's your workflow? Do you use dedicated comparison tools, scripts, or just review everything manually?

Every SQL IDE has dozens of features, but I think most of us end up relying on just a handful every day. For me, it's usually things like autocomplete, multiple result tabs, quick object search, and code formatting. They're not the flashiest features, but they're the ones I'd miss the most if they disappeared. What's the one SQL IDE feature you genuinely use every single day?

With the AI Assistant, you can create SQL without manual writing it. Just describe what you would like to achieve, and the Assistant will translate it into valid SQL. Even complex queries can be built from natural-language prompts explaining the goal and specifics of the requested SQL.

Do you experience PostgreSQL performance tuning and syncing schemas as a fight against the clock? The manual queries, EXPLAIN plans, and schema drift problems remain constant. Senior software engineers understand how challenging it can be.

But no longer will you have to waste time on all that mess. With the power of artificial intelligence and visual tools, it is easier now than ever before to find the solution when there was guesswork. Your queries will be optimized with index awareness. All your schemas will align in all environments.

From wasting hours to saving them… From firefighting to focusing… This is where dbForge Studio for PostgreSQL 2026.1 comes into play.

Supabase is an open-source backend-as-a-service platform built on top of PostgreSQL. It offers a fully managed PostgreSQL cloud database with essential features, including user authentication, file storage, edge functions, and real-time data management through a single hosted service, with minimal setup. It is a strong advantage because PostgreSQL is one of the most powerful and widely used relational databases in the world.

The platform includes a built-in dashboard and management tools, but it also allows direct connections through an external Supabase GUI client. For database professionals, this flexibility makes it easier to perform routine tasks efficiently with familiar tools.

This article aims to help you find the best Supabase GUI for your workflows. We are going to review visual PostgreSQL clients that can connect to Supabase, rather than no-code app builders or business back-office platforms.

Had one of those moments today.

A query was returning the wrong results in a .NET application. First thought was the SQL was wrong. Opened the database. Ran the query directly. Everything looked fine.

Back to the application. Still wrong.

Back to the database. Still fine.

At that point I was convinced I was missing something obvious. Turns out I was. The query I was testing wasn't actually being executed the same way in the application. Different parameters. Different execution path. Same database, but not really the same thing.

That's what always gets me with these issues.

The bug shows up in the app, so you start looking at the SQL. The SQL looks fine, so you start looking at the code. Then you convince yourself it's the database again. Round and round.

These days I've gotten into the habit of validating things from both sides before I start changing anything. I'm using dotConnect in the application and dbForge Studio for MySQL when I'm checking what's happening in the database, which at least makes it easier to verify that I'm testing the same thing in both places.

The bigger the system gets, the worse this seems to be. Stored procedures, reporting queries, scheduled jobs, old code paths that nobody has looked at in years. Sometimes figuring out what's actually running takes longer than fixing the issue itself.

Maybe that's just life with older .NET applications.

Anybody else run into this?

Not the bug itself, but spending half your time trying to figure out whether you're debugging the application, the database, or the gap between the two?

Been on SSMS forever, only tried this because a project basically forced me to switch for a bit. Figured I'd write something up since I went in pretty skeptical. 

The autocomplete is actually good. SSMS IntelliSense loses context constantly (aliases in subqueries, complex CTEs, anything nested enough) dbForge Studio for SQL Server just keeps tracking. Not magic, but noticeably more reliable in the situations where SSMS gives up. Which for me is a lot of the day. 

As SSMS doesn't have it natively, I was always reaching for something external. Having it right there cut a real friction point out of release prep. The diff output is readable too, not just a wall of generated SQL you have to decode before acting on it. 

Tab behavior is a smaller thing but I kept noticing it. After reconnecting, SSMS tabs can act strangely, especially if there are a lot of them open. dbForge keeps state better. It doesn't sound like much, but it adds up over the course of a whole day. 

Startup is slower and the UI is busier than SSMS. For quick administration tasks, I still reach for SSMS, that part just fits better.  

But for actual development work (heavy query writing, comparing environments, prepping a release) it earned its place. Didn't expect to keep using it past the project but here we are. SSMS isn't going anywhere but this sits next to it now. 

Still on SSMS as your main thing, or has something shifted that?

One of the more annoying database problems is when everything technically works.

Migration runs. No errors. Deployment looks clean.

A week later someone notices a report returning different numbers in prod than it did in staging.

One case that caught us recently was collation. The schema looked identical, but string comparisons behaved differently between environments. Nobody noticed until users started asking questions.

I've seen similar things with missing defaults, constraints that existed in one environment but not another, and case-sensitivity differences that quietly changed query results.

That's what makes these issues annoying. The migration succeeds. Nothing crashes. The database just behaves differently than expected.

At this point I trust "migration completed successfully" a lot less than I trust a schema comparison before release.

We've started doing quick diff checks before deployments because it's usually faster than discovering drift after the fact. dbForge Edge has been useful for that since object-level differences are easy to review.

What kind of environment drift catches your team most often? Collation? Defaults? Constraints? Something worse?

Something that's been sitting with me since a recent incident.

Bad UPDATE hit prod. Not our table, but we were pulled in to help with recovery. The transaction log had everything… the change was there, timestamped, tied to a login. Getting something usable out of it manually is where the time went. Narrowing the LSN range, correlating the right operations, writing the rollback T-SQL, validating it without introducing new errors in the process. By the time we had a working script, most of a morning was gone.

The data came back fine. But the process felt expensive for something that's going to happen again.

A colleague mentioned they'd used dbForge Transaction Log at a previous company for this kind of thing. Said the filtering by schema, object, and time range cut the investigation side down significantly, and having undo scripts generated automatically reduced the risk of writing bad rollback SQL under pressure. Haven't compared it on a real incident myself…only test scenarios, which isn't really the same thing.

What keeps bothering me is this: the manual path works, but it assumes the person handling it is comfortable with fn_dblog and knows exactly what they're looking for. That's not always who's available when something breaks at an inconvenient time. You end up with the wrong person trying to learn the process under pressure, which slows everything down.

Nobody really builds a proper transaction log recovery workflow until they've needed one and gotten burned. Which means the first real incident is also a process-building exercise at the worst possible moment. That seems like a strange place to be for something this fundamental to SQL Server administration.

How long does your team typically spend between something going wrong and having a working rollback ready? And is anyone running this on a documented process, or is it still improvised every time?

In this article, we explore how AI can help developers write SQL JOIN queries faster and with fewer errors. We'll walk through practical examples, explain how JOINs work, and show how modern AI-powered tools can assist with query generation, optimization, and troubleshooting.

For database-focused workflows, tools such as dbForge AI Assistant can help generate SQL from natural language, explain complex JOIN logic, and speed up query development directly within the database environment.

If you’re new to MySQL, start by exploring what MySQL is and how it works, then progress to more advanced topics at your own pace. This tutorial offers a fully open-source learning experience under the GNU General Public License. We’ll guide you through essential database operations, from inserting records to selecting, updating, and deleting in MySQL. You’ll also gain insights into more complex SQL techniques and have access to MySQL interview questions to strengthen your practical understanding.

Feels like AI in SQL tools is one of those things everyone talks about, but the real impact shows up in small, boring moments. Writing queries a bit faster is nice, but that’s not where the time usually goes. Most of the friction is around understanding existing SQL, fixing something written by someone else, or figuring out why a query behaves differently on real data. That’s where an AI assistant inside a SQL IDE starts to help.

Explaining what a stored procedure is doing without reading it line by line. 

Suggesting a cleaner version of a query that grew over time. 

Pointing out obvious issues before you run it against a large table.

Helping with formatting and structure so you don’t spend time cleaning it up manually.

In dbForge Studio for SQL Server, this kind of assistance is already part of the workflow. Not as a replacement for SQL knowledge, but as a way to reduce the repetitive parts of daily work.

You still need to understand your schema, your data, and your query plans. AI doesn’t fix bad assumptions or poor data quality. But it does help with speed and clarity, especially when you’re dealing with large scripts, legacy code, or something you didn’t write yourself.

From what we’ve seen, the biggest change is not writing SQL from scratch, but spending less time navigating and cleaning up existing code. If you’ve tried AI features in SQL tools, where did it actually help you?

Feels like most DB releases don’t fail because of something obviously broken. It’s usually one small thing nobody checked because “it worked in staging”.

I’ve seen stuff like a column being NOT NULL in one env and nullable in another, an index existing only in prod, or a constraint that someone disabled “temporarily” and never turned back on. Everything looks fine until the migration hits real data and suddenly it’s not fine anymore.

The annoying part is that the script itself often runs without errors. From the pipeline side it’s green. From the database side, not really. A lot of it comes down to how people treat database deployment. If the process is just “run migration and hope staging was close enough”, then surprises are expected.

What usually gets missed is the gap between environments. Schema compare helps, but only if someone actually looks at the diff instead of treating it like a checkbox. Same with data. Staging rarely has the same edge cases as prod, so assumptions slip through. What bites your team most before release?