Streaming database platform provider Materialize takes in $ 60 million


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Materialize, a company developing a continuous structured query language (SQL) database platform, today announced that it has raised $ 60 million in Series C funding, bringing the company’s total to more than $ 100 million. Redpoint Ventures provided the capital with participation from Kleiner Perkins, Lightspeed Venture Partners and others, and co-founder and CEO Arjun Narayan said it would be used to grow Materialize’s engineering team and scale up its service. cloud from beta to general availability.

Real-time data analysis can benefit companies in the finance, retail, e-commerce, and other industries. For example, banks can identify fraudulent transactions while minimizing false positives, and e-commerce sites can offer better personalization through referrals. But real-time data analysis often requires trade-offs between cost, speed, and functionality. For example, it’s difficult to get response times in milliseconds for requests without creating custom microservices.

Founded in 2019, Materialize – whose team includes the first employees of Dropbox, Stripe, and YouTube – offers a standard SQL interface for streaming data so businesses can build queries without the need for engineers with specialized skills. The platform incrementally calculates and maintains data as it is generated, so that query results are available when they are needed.

“Frank McSherry and I founded Materialize in February 2019 after realizing the implications of his research on timely and differential data streams to deliver ‘true’ real-time data streaming. We’ve been studying this problem for decades, and Frank in particular has spent years doing the hard science that allows developers to write complex queries to stream data using standard SQL, ”Narayan told VentureBeat by e -mail. “We have the mindset that all businesses should have access to the power of accurate streaming data without compromise. While other data streaming solutions have been around for years, each one requires some sort of compromise. “

Materialized views

McSherry and Narayan named Materialize after the database concept of “materialized views”. In databases, view materialization refers to the act of precomputing the results of a query so that they are instantly available when needed, rather than doing the work on demand and waiting. the end of the calculation.

“Materialized views that are always fresh have long been prohibitively expensive in traditional database systems, and Materialize makes them inexpensive and always ready on all a company’s streaming data,” said Narayan. “We have seen our first customers use Materialize for real-time data visualization, financial modeling, and to advance various software-as-a-service applications in marketing technologies, logistics and enterprise resource planning. “

While Materialize isn’t a machine learning or AI engine itself, Narayan notes that it may play a role in the data pipelines that power machine learning models. Some companies, including Datalot, have investigated the use of Materialize as a “streaming feature store,” a class of tools used to store features commonly used in models.

Above: Materialize can be used to power AI and machine learning pipelines, as shown in this diagram.

Image credit: materialize

“Current solutions offer a linear compromise between speed and cost. If you want to go faster, you just have to pay for it, ”Narayan said. “We’re looking to break that model by delivering extremely low latency computation, but at a much more efficient scale using standard SQL. “

Materialize says that in six months its developer community has reached over 970 people and has attracted brands like Density and Kepler Cheuvreux. This month, the startup, which has nearly 60 employees, plans to open its headquarters in the old Slack office in New York.

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