QuestDB Expands Time Series Database With $ 12 Million Funding Cycle

Open-source database startup QuestDB said on Wednesday it had raised $ 12 million in a Series A funding round to help the vendor grow its technology and go-to-market efforts.

Founded in San Francisco in 2019, QuestDB has developed an open source time series database Technology. While some time series databases have required users to learn new query languages ​​in order to use them, QuestDB has focused on using SQL as the main method to query its database.

As of today, QuestDB is available for users to deploy on their own, as well as with offers on AWS Marketplace and the Digital CloudOcean.

In this Q&A, Nicolas hourcard, CEO and co-founder of QuestDB, explains the time series database use cases and where the provider is heading.

Why are you lifting a Series A now?

Nicolas hourcard

Nicolas hourcard: We want to double the focus on wider adoption by developers. We are seeing a lot of use from all kinds of businesses in different industries and many developers are joining our community.

Raising capital allows us to create more functionality in open source products. It also helps create the fully hosted cloud that a lot of people ask for pretty much all the time because they don’t want to manage QuestDB in the cloud on their own.

What makes QuestDB different from other time series databases?

Time card: Our differentiation is a combination of performance, simplicity through the use of SQL and the fact that we are open source. In fact, we built the entire stack for ourselves and we didn’t re-use any code available there. The algorithms have been optimized to extract as much performance as possible from the hardware.

Time series data is really everywhere.

Nicolas hourcardCEO and Co-Founder, QuestDB

So the journey of my co-founder and CTO Vlad Ilyushchenko is high frequency electronic commerce. In this world you tend to do high performance code with zero garbage collection Java. The code approach is more like C ++ than Java, oddly enough, and you can actually get a lot of performance out of that kind of code.

What do you think are the use cases for a time series database?

Time card: Time series data is really everywhere. In fact, we’ve discovered more use cases over time.

So initially we came from the world of e-commerce, so financial services is an obvious use case, where we get a lot of market data at very high rates. Time series works great there because you really need performance.

Then we saw a lot of companies doing asset tracking for fleets. So all kinds of vehicles such as cars, airplanes, ships, just track position over time. We also have a geospatial element, which we released recently in order to do it very quickly.

Another use case is for sensor data from any type of machine or manufacturing in general. We’re also seeing a growing number of data science use cases involving machine learning models to predict future behaviors based on historical patterns. These are all time series data.

How do you find organizations generally starting to use the QuestDB time series database?

Time card: We are seeing developers who are already using a time series database and generally looking for alternatives because of the performance aspect of things.

Then there are other developers who could use Oracle or PostgreSQL or other traditional relational databases. For a startup, at first, it can start by using the traditional database because it understands things and doesn’t have a lot of data.

But at some point, the scale means it’s going to sort of crumble, especially if it’s time series data, because the main feature of time series data is that it is produced in big party. Usually, we see companies come and tell us that their existing setup is not working and that they are looking for alternatives.

There are also users who use QuestDB for entirely new projects involving a lot of real-time data and components.

Editor’s Note: This interview has been edited for clarity and brevity.

Maria H. Underwood