Forget in-memory — SiSense raises $10M for in-chip analytics

While the rest of the world is agog about big data and in-memory analytics, SiSense is taking a different tack. It’s rethinking business intelligence with higher-speed analysis on smaller (relatively speaking) data sets by taking advantage of multicore, 64-bit processors. The approach has been paying off with some impressive customer uptake, and on Wednesday SiSense announced a $10 million series B funding round from Battery Ventures along with Opus Capital and Genesis Partners.

Technologically, SiSense is trying to split the difference between just about everyone else doing analytics — expensive full-stack business intelligence vendors such as Oracle (s orcl), Microsoft (s msft), IBM (s ibm) and SAP(s sap); big data and data warehouse vendors pushing massive scale databases; and next-generation, visualization-centric vendors such as Tableau and QlikView (s qlik). It’s fast, it’s has its own columnar database and HTML5 visualization technologies, can scale comfortably up about 100 terabytes, and is designed for business users rather than advanced data analysts.

SiSense’s secret sauce is a processing architecture built for speed even on small machines such as laptops. According to CEO Amit Bendov, the company’s product, called Prism, can handle a terabyte of data on a machine with 8GB of RAM because it relies primarily on disk for storage. Data is only moved to RAM as necessary, and then Prism uses vectorization and optimized instructions (that do one thing only, but do it across all the data that fit the query) to handle as much work as possible in parallel on the processor.

Source: SiSense
Source: SiSense

“We say in-memory is not the future, it’s the past,” said Bendov. “We’re already two steps ahead.” Using Hadoop or Teradata for a handful of terabytes, he added, is overkill, “like driving a Humvee to the grocery store.”

Eldad Farkash, SiSense’s co-founder and CTO, uses a different analogy — that of buying beer — to explain the technology’s underlying rationale. Latency to the CPU from the processor’s L1 cache is like grabbing a beer from the refrigerator, whereas using the L2 or L3 cache is like riding a bicycle to the corner store. RAM is the equivalent of driving a car to the grocery store, and accessing data from disk is like going to the brewery itself. Prism knows it will have to go to the grocery store, but it gets as much beer as possible from the fridge and corner store first.

dashboard-imgOnce users are actually in the product and analyzing data, it’s a drag-and-drop experience to connect various data sources and points (although custom SQL is allowed, too). The actual analysis window features a canvas that can display numerous widgets (e.g. pivot tables, charts or dashboards) at once.

Bendov said SiSense’s revenue grew 520 percent in 2012 and its notable customers include Target, Merck, Samsung and Cisco. The new investment will be used primarily to bolster the company’s sales and marketing efforts — which thus far have been largely relegated to in-bound inquiries — and to support customers in different geographies (the company is based in Redwood Shores, Calif.). “Now’s the time to add oil to the fire,” he explained.

As impressive as it all sounds, though, SiSense’s biggest challenge might well be getting noticed above the fray that is the analytics space right now — especially among more well-known and arguably future-proof vendors and technologies. That said, being a low-cost option that users like and that actually works has proven remarkably effective in an era of cloud computing and bring-your-own-device, and SiSense appears to racking up users at a pretty rapid clip.

Any product that can prove its worth initially with the people who have to use it stands a good chance of sticking around and becoming a permanent part of IT budgets for years.

Feature image courtesy of Shutterstock user Iscatel.