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We’re excited to let you know that Sapphire Ventures is leading Dremio’s $135 million Series D funding round. This makes Dremio a unicorn, which is the highest level of business funding. Companies are making more data than ever before, and they are relying more and more on insights from that data to make important business decisions. As the use of the cloud grows and computing and storage become more separate, businesses need an efficient way to look at data that is stored outside of the traditional definition of a “data warehouse.”
We think that Dremio, a distributed SQL query engine, is just what the market needs to deal with these changes. The company’s technology lets data scientists and analysts use tools like Power BI and Tableau to quickly query cloud data lake storage using a self-service semantic layer. Dremio, which is run by an experienced CEO named Billy Bosworth and a co-founder and Chief Product Officer named Tomer Shiran, wants to go beyond separating computing and storage. Instead, it wants to make the data itself a separate tier from compute, so that analytics can be done directly on full data sets regardless of the vendor or format.
Here are some more reasons why we are so happy about Dremio:
Different kinds of analytics tools are needed because of changes in the way computers are used. Several important trends in the world of computers are driving demand for Dremio’s solution on the market. The amount of data that companies collect and how important it is to their success are both on the rise. IDC thinks that in 2020, more than 59 zettabytes of data were “created, captured, copied, and used.” By 2024, that number is expected to reach 140 zettabytes.
The need to store and make sense of this data has grown the nearly $22 billion data warehouse market and the $26.5 billion business intelligence and analytics market. At the same time, more and more data is moving to the cloud, which is a trend that has only gotten stronger as the pandemic has pushed organizations to speed up their digital transformation efforts.
Because of how it works and how much it costs, most data can’t be stored in a single data warehouse. At a higher level of abstraction, there is also a move toward a disaggregated software stack, where each layer (storage, compute platform, compute frameworks for batch/real-time/SQL, etc.) is built as a set of Lego blocks that can be put together in different ways. This is in contrast to monolithic software stacks that are hard to change (such as a database with its custom storage format, parser, execution engine, etc. in a vertically integrated fashion).
As more companies move to a modern, separate software stack, the divide between computing and storage functions grows. But analysis is the only way to find out what all this data is worth. We think that a lot of companies need a tool like Dremio that lets them quickly access and directly analyze data stored in open formats in the cloud, without having to pay for expensive data copying and integration.
With Dremio’s data lake engine, you can get high-performance analytics at a low cost.
By separating data from storage, Dremio gives users the most options for when and where to analyze data. It lets data scientists and analysts run quick, self-service queries directly on data lake storage like AWS S3 and Azure ADLS. Dremio can run fast, high-performance queries at the same level as cloud data warehouses, without having to spend money on copying, integrating, or storing data in a way that isn’t open source. With a vertically integrated semantic layer and a distributed SQL engine, teams can get the data insights they need faster and for less money.
Dremio’s product was made to meet the needs of enterprises in terms of performance, security, and scalability. It fits well into the modern enterprise software ecosystem and lets companies use the analytical tools and storage options they prefer. Dremio provides strong data governance through a package of data catalog and lineage. This lets its customers, which include UBS, NCR, TransUnion, and Henkel, see how data was queried, changed, and linked across sources.
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