DBLab SE - instant cloning and database branching for PostgreSQL

I have been using DBLab Engine for the last six months. We are using DBLab Engine to speed up and write PostgreSQL queries, testing, and database processes, improving our queries, PostgreSQL development, and testing in every workflow. One of the challenges was that we need to optimize every query, so we are using DBLab Engine to identify and solve that optimization. For PostgreSQL, DBLab Engine is the best tool where we can write the queries, develop the query, and also test that query.

Our product has a database, and we are writing everything on DBLab Engine, which is hosted on DBLab Engine, where we are merging, creating the branch, and cloning our website application database. Then we are addressing and completely changing data, and everything we are working on is on DBLab Engine, where we completely write the query, run the query, and test that query.

Every day we are writing our queries on DBLab Engine, and from that interface, we are using the database backup, the restore of the database, and the snapshot of the database, needing to update everyday data according to the developer side and backend side. Everything we are working on is on that, providing better schema changes, better application changes, and better optimizations of the query. This product reduces the delays in our development cycles.

We can easily write the query and easily find out the bugs, which is useful for optimization. It is easy to manage everything, such as testing, branching, cloning, and everything. Speed for the development and also the QA processes allows us to do everything with that tool continuously.

We save time, and we are working faster than before; our team is now faster. DBLab Engine has definitely saved time, and we can easily work.

As a beginner, there were some challenges we faced to learn the UI design and the interface, so making it more user-friendly and beginner-friendly would be beneficial. We can add more functionality to make it perfect.

We can support AI tools in this generation and provide more documentation so anyone can understand and learn from it easily. Adding some AI features would be the best improvement. There is a learning curve for beginners, so making beginners more aware of DBLab Engine would help.

I have been working with this solution for the last six months.

DBLab Engine is stable.

DBLab Engine is 100% scalable.

When we are raising any complaint, they definitely contact us.

When I was joining, after that, we were working on DBLab Engine, but before that, I do not know what product we were using.

We are creating a backup, taking three backups every day: full backup, differential backup, and log backup.

I have never reached out to the implementation team; it is provided by our IT department.

DBLab Engine is 100% positively related to our organization.

If you are a database developer or you need to test data related to a database, then you can try DBLab Engine.

DBLab Engine is secure. We need it to be 100% accurate and reliable on results. DBLab Engine should remain private.

During the hackathon, my main use case for DBLab Engine was quickly spinning up a full-size database clone for testing and benchmarking my application query on realistic data loads without destroying the main environment. DBLab Engine really helped me to quickly prototype my idea.

For a specific example of how I used DBLab Engine during my project, we were implementing a complex analytic feature that required running heavy, unoptimized SQL queries to benchmark performance under realistic data loads. Instead of manually setting up a messy local database or risking downtime on a shared instance, I used the database API to instantly create a thin clone. I was able to safely run multiple query optimization experiments, analyze the execution plans, and iterate on database indexes in an isolated environment. Once the test was complete, I destroyed the clone immediately, which made the testing workflow incredibly lightweight and fast.

I only used DBLab Engine for a while, but I already see many benefits. I can think of applications not only in the hackathon, but we can also probably do some black-box monitoring, set up some simulation environments, and I think it is very helpful to have this kind of automatic testing to ensure that whenever our new feature is delivered, there are no regressions.

In my experience, the absolute best feature of DBLab Engine is the thin cloning capability driven by copy-on-write technology. Being able to provide a full-size production-scale database clone in just a few seconds, regardless of whether the underlying database is tens of gigabytes or multiple terabytes, is a massive game-changer for engineering velocity. I really would to try more. The complete environment isolation is also very fantastic.

I have noticed that the thin cloning and environmental isolation of DBLab Engine have dramatically accelerated our development lifecycle and improved overall code quality, even in a hackathon. Before utilizing this, setting up a realistic test database was a major bottleneck. Developers either had to share a single staging database or spend hours trying to seed a local database with a thin, representative subset of mock data. With DBLab Engine, we achieved complete workflow interdependence, which was incredibly helpful.

One thing I noticed is that not many people know about DBLab Engine, so I think it needs more advertisement. Additionally, the initial configuration and infrastructure setup is a bit complicated, so more documentation would be a good addition to have.

I used DBLab Engine in an AI hackathon event, which was good, so I would say approximately one month, including the survey.

DBLab Engine is stable for our use case.

I believe DBLab Engine can handle scalability well, primarily because of its architectural design. It uses a containerized approach alongside a copy-on-write file system. In our use case, we did not have a chance to scale it, as we were just doing some research and experimental work in the hackathon, but I believe there are more use cases out there. I think scalability is usually not an issue.

I did not use customer support this time, but I asked AI many questions in order to set this up. If that also counts, I think there are many resources out there, as AI knows everything.

Before using DBLab Engine, we primarily relied on standard staging databases or manually providing smaller local database dumps for testing.

I believe I just had a try with DBLab Engine and cannot think of anything that needs improvement.

I have seen a clear return on investment with DBLab Engine, primarily measured in significant time saving and reduced infrastructure overhead.

I think DBLab Engine offers excellent flexibility when it comes to pricing and license because the core engine, I believe, is open-source. The initial cost to experiment with it during the hackathon was completely free, which lowered the barrier to entry significantly.

I usually use Google Cloud Spanner because that is the most common solution available in Google Cloud and also used in our products. Because it was a hackathon, I wanted to try something new on the market and see how it goes.

A piece of advice I would give to others looking into using DBLab Engine is to identify your team's database testing bottleneck before diving in. If your developers are frequently running into issues such as flaky tests or an inability to test complex query performance against realistic data, I think DBLab Engine is an incredibly effective solution. I would rate my overall experience with DBLab Engine at a rating of four out of five.