UPDATED 09:00 EST / NOVEMBER 03 2021

BIG DATA

QuestDB gets $12M Series A funding amid growing interest in time-series databases

Startup QuestDB Technology Inc. is itching for a fight with more established players in the fast-growing time-series database market after closing on a new $12 million round of funding.

Today’s Series A round was led by 468 Capital and saw participation from Uncorrelated Ventures, plus a veritable who’s who of open-source technology founders, such as Tom Preston-Werner (co-founder of GitHub Inc.), Sebastien Pahl (co-founder of Docker Inc.), Alexis Ohanian (co-founder of Reddit Inc.), Mirko Novakovic (co-founder of Instana Inc.), Andrey Alekseev (co-founder of NGINX Inc.) and Tobi Knaup (chief executive of D2iQ Inc.).

QuestDB has built what it claims is the fastest open-source time-series database on the market, used to power real-time applications in a variety of spaces, ranging from financial services to DevOps monitoring, asset tracking, digital factories, geospatial analysis, autonomous vehicles and more.

A time-series database is one that’s optimized to process information chronologically, arranging data in the exact order it is created. That’s necessary for certain types of apps, such as one that tracks the heat readings from an industrial sensor, for example, so users can see how the temperature level in a specific piece of equipment oscillates over time.

QuestDB has quite a few rivals in the time-series database space, including MongoDB Inc., InfluxData Inc. and QuasarDB SAS, to name just a few. But the startup reckons its offering has some advantages, with one of the most notable being the ability to use standard Structured Query Language to query data. In contrast, those rivals require developers to learn a proprietary query language to ask questions of their data.

QuestDB co-founder and CEO Nicolas Hourcard told SiliconANGLE it is precisely these factors that are prompting many developers to move away from those alternatives.

“Our value proposition is clear: open source, performance for time-series data, and ease of use through SQL,” he said. “We built QuestDB from the ground up with performance in mind, and it took us nearly seven years of R&D to get there.”

Hourcard explained that QuestDB’s code has been optimized to extract more performance from modern data center hardware and that, thanks to its open-source nature, developers can check for themselves to validate this.

“The write and storage systems are optimized for the fastest code execution path, leading to high ingestion speeds. This is crucial because engineers should not lose data in transit due to the bottleneck of a slow data sink,” he said. “QuestDB also produces lightning-fast querying capabilities for analyzing the data in real-time. This performance joined with ease of use through a language that developers know and love, SQL, is the combination that stands us apart from other solutions in the market.”

A graduate of the Y Combinator accelerator program, QuestDB also supports geospatial data, out-of-order data ingestion, SQL extensions and accelerators for working with time-series data in multiple time zones. The QuestDB database is available in the Amazon Web Services Marketplace and can also be hosted at DigitalOcean for applications that require flexible deployment options.

QuestDB has built up quite a following already. It has an open-source community of more than 10,000 developers and fast adoption, with the number of unique deployed instances growing at a rate of 6% per week.

Asked about this growing demand, Hourcard explained time-series data is increasingly being generated almost everywhere, by servers, applications, sensors, financial exchanges, networking devices, fleets and even devices such as wearables. One of the unique characteristics of this time-series data is that its “volume-generated,” he said, and that in order to track how things evolve over time, it’s necessary to collect these enormous volumes of information in a data store without losing any of it in transit.

“Traditional relational databases are not suited to cope with such large amounts of data by design,” he said. “This requires a specific architecture focused on performance from the outset; this is where time-series databases come into play.”

Hourcard believes artificial intelligence and machine learning is also driving growth of time-series databases. “As data grows exponentially, we see more and more developers and companies relying on time-series databases to power all kinds of real-time applications to understand what is happening in their business, create insights, and make decisions based on what they can see,” he said.

One of QuestDB’s biggest fans is the multinational aerospace corporation Airbus SE, which uses it to power real-time applications that generate “hundreds of millions” of data points each day. “For us, QuestDB is an outstanding solution that meets and exceeds our performance requirements,” said Airbus software architect Oliver Pfeiffer.

With money in the bank, QuestDB says it plans to drive product innovation and further growth by building up its support and success functions. The company is also promising to develop new features for QuestDB and launch a fully managed database-as-a-service version of its platform for customers that want all of the benefits with minimal hassle.

Image: QuestDB

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