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.
Software Engineer at a transportation company with 51-200 employees
Real User
Top 10
May 24, 2026
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.
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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.
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.