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MapD Raises $25M Series B to Drive Adoption of GPU-Powered Analytics

MapD Raises $25M Series B to Drive Adoption of GPU-Powered Analytics

Today I’m proud to announce that MapD Technologies has secured $25M in funding in a Series B round lead by New Enterprise Associates (NEA) with participation from NVIDIA, Vanedge Capital, and Verizon Ventures.

This new investment will allow MapD to scale up its engineering, sales, and marketing teams to accelerate product development and deepen customer adoption. It will help us move faster toward our vision of making GPU-powered analytics ubiquitous in the enterprise.

There are clear signs this is already happening. Look no further than the recent launch of GPU-powered instances by all three major cloud providers, the tripling of data center revenue NVIDIA has seen over the last year, or the rapid increase in adoption of GPU-powered deep learning to see the beginnings of a transformation in enterprise computing.

The need for a new compute paradigm is clear. CPU performance is only increasing at a tepid 10-20% per year, significantly lagging the growth of data. This compute gap is forcing organizations to adopt awkward workarounds such as downsampling, pre-aggregation, or massive scale-out, all of which have painful downsides.

GPUs provide a way forward. By leveraging the parallelism of thousands of cores, they offer significant performance gains over CPUs, and are getting significantly faster each generation. GPUs are already widely used for HPC and machine learning, however their use in database and visual analytics workloads has lagged.

This is what we intend to change. By leveraging the parallelism of GPUs, we can run standard SQL workloads orders of magnitude faster than CPU solutions. And since we have the native graphics pipeline of the GPU, we can render query results in stunning detail, providing granular data visualization that traditional client-server BI solutions cannot match.

The speed of the MapD analytics platform is a game changer for our customers. Verizon Wireless found that the MapD platform gives them unprecedented visibility into high-volume cellular phone log data. In-Q-Tel introduced MapD to users in the US federal government after other systems were found incapable of querying and visualizing large geospatial data in real-time. And one of the world’s largest hedge funds now uses the MapD analytics platform to extract insights from its vast datasets, finding even a single MapD server significantly faster than a hundred-node Hadoop cluster running Impala.

Looking back, I am sometimes astonished that what started started as my final project for a MIT database course has evolved to bring real and tangible benefit to customers doing impressive things in so many different markets. However, as exciting as these success stories are, it is also clear that we are just at the beginning of a long journey toward the goal of making GPUs ubiquitous in enterprise analytics. After all, disrupting (I use the word unapologetically) an established space is no easy feat.

Two factors, in addition to the technological trends described above, give me confidence about the path ahead.

The first is the caliber of our team. MapD is a group of passionate and brilliant people striving to push our products and company forward. I am honored to lead a team that stays up late worrying about how to make our code run faster, working to ship a new feature, or helping a customer with a complex analytics problem.

The second is our amazing backers. Greg Papadopoulos and Forest Baskett at NEA grasped the disruptive potential of GPU-powered analytics from our first meeting. Moe Kermani at Vanedge Capital bought into our vision, leading our Series A round and helping us every step of the way as we’ve taken our product to market. Jeff Herbst at NVIDIA has been with the company all along, from when MapD won the NVIDIA Early Stage Challenge three years ago, and through all three of our funding rounds. Mark Smith at Verizon and George Hoyem at In-Q-Tel have provided invaluable perspective on critical product and business decisions.

And so while building a successful startup is never an easy task, I am heartened by our fantastic team and investors backing our company as we work to drive adoption of GPU-powered analytics. We look forward to the next steps of the journey.

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