Data analytics startup Imply nabs $70M to grow cloud service

Imply, a startup developing a real-time analytics platform, today announced that it raised $70 million in series C funding led by Bessemer Venture Partners, valuing the company at $700 million post-money. CEO Fangjin Yang says that the proceeds will help to support Imply’s product and go-to-market efforts, including its cloud service and its prebuilt visualization application Imply Pivot, as well as investments in people and technology that will help the company to “rapidly grow business.”

Data analytics is the science of analyzing raw data to extract meaningful insights. Businesses that use big data increase their profits by an average of 8% according to a survey conducted by research firm BARC. A range of organizations can use data to boost their marketing strategies, increase their bottom line, personalize their content, and better understand their customers. But only 4% of companies say they have the people, tools, data, and intent to draw meaningful insights from that data and to act on them, according to a Bain report.

Above: Imply’s founding staff.

Image Credit: Imply

Burlingame, California-based Imply provides a suite of tools to run large-scale analytic workloads. It’s built on Apache Druid, a column-oriented, open source distributed data store written in Java that’s designed to quickly ingest massive quantities of event data and provide low-latency queries on top of that data. Druid was started in 2011 to power analytics products at Metamarkets, a programmatic advertising startup Snap acquired in 2017, and it was moved to an Apache license in 2015.

Imply was founded by Yang, Gian Merlino, and Vadim Ogievetsky, the lead engineers of the original team that created Druid.  Their goal with the Druid project was to enable users to arbitrarily explore and aggregate data, and to have visualizations update as fast as users could navigate through the data. This required a data store that could support interactivity “at scale” as well as offer complete flexibility in how data could be explored.

“Druid came into existence to fill a gap in the data world, and was architected with a few key attributes in mind: sub-second queries so users can explore data without breaking their workflow, streaming data ingestion so events can be explored immediately after they occur. After Druid was open sourced, people began using it for everything from analyzing ad tech, network traffic, website usage, finance, and sensor data,” Yang told VentureBeat via email. “We’ve come a long way over the past few years in terms of both adoption and scale. Existing Druid clusters now scale to petabytes of raw data, trillions of events, and millions of daily queries.”

The Imply platform

Yang asserts that Imply is one of the few analytics solution providers with a true full-stack solution for “analytics-in-motion” use cases. Leveraging the power of Druid, the platform allows customers to create data analytics queries on the fly and receive answers immediately — or almost immediately.

Imply is built to scale with technologies including large data volumes and high search concurrency, Yang says, as well as low-latency infrastructure. And unlike several rival products, Imply can compare present data with past data as well as data in motion.

“Some people refer to real-time analytics as analytics based on real-time data, but they don’t mention that questions need to be predetermined; that the data is only historical data; that the solution doesn’t scale beyond a certain number of records; and that the solution is prohibitively expensive. They also don’t mention that they provide only an engine, not also a UI or app,” Yang explained. “This is a huge handcuff as you have to wait hours or days to iterate and get to the ‘a-ha moment.’ Moreover, you can’t know what’s happening now or compare what’s happening now to the past, or account for data volumes that grow exponentially every year.”

To illustrate the ostensible benefits of Imply, Yang gave the example of Sift, a fraud detection startup using the platform for AI and machine learning workloads. Sift is tapping Imply’s analytics engine for real-time data ingestion, allowing the company to aggregate data by a range of dimensions from thousands of servers and query across a moving time window with on-demand analysis and visualization.

Above: Imply’s web dashboard.

Image Credit: Imply

“The pandemic has accelerated the rate of digital transformation in our customers, and analytics is at the forefront of their initiatives,” Yang said. “[Our over 100 customers, including Plaid and Cisco ThousandEyes,] leverage analytics-in-motion as the analytics engine behind applications centered around security, operational, and business insights, and as a complete solution to solve internal business intelligence and operational analytics use cases such as operational analytics, security analytics, advertising analytics, behavioral analytics, and more.”

Imply, which has around 132 employees, isn’t disclosing annual recurring revenue just yet.  But the market opportunity for big data analytics is large and growing, as evidenced by the funding rounds closed by competitors like Firebolt, Leadspace, Dremio, and Noogata. Market Research Future predicts that the global data analytics market will be valued at over $132 billion by 2026.

Imply’s latest funding round brings its total raised to $116 million to date, and comes as the company hires a new chief marketing officer — ex-Confluent product marketing leader Praveen Rangnath — and VP of customer success — former Fivetran customer success head Kevin Hodgkins. In addition to Bessemer, the round saw participation from Tiger Global Management, A16z, Khosla Ventures, and Geodesic Capital.