days
0
9
hours
2
2
minutes
1
9
seconds
2
8
A quest begins

Metanautix unveils Quest, a data compute engine for all file types

Coman Hamilton

CEO of Metanautix speaks to JAXenter about their latest venture into big data analytics: Quest.

Theo Vassilakis, CEO and co-founder of Metanautix spoke to JaxEnter about Quest. Theo shared a little bit about the company, the problems that Quest can solve and their predictions for the world of big data.

JAXenter: Can you tell us what new features is Quest bringing to the big data analytics table?

TV: Metanautix Quest allows analysts to easily access and combine data from disparate silos into easy-to-understand tables – whether the data is records, logs, documents, audio files, images, or videos. Because it’s based on standard SQL, the solution works within an organization’s existing toolset and doesn’t require data be moved to a centralized system. Metanautix Quest simplifies and speeds-up data analysis for more effective collaboration, governance, security, privacy and compliance by putting next-generation distributed systems technology at the fingertips of any business analyst.

What kind of problems are you hoping to solve with Quest?

We are in the analytics market and we see ourselves as a Data Supply Chain company. Right now, enterprises spend a great deal of money and time navigating the complexity of a slow, cumbersome and opaque data pipelines. Narrow, specialized technologies, while best suited for a certain types of data, make it very difficult to access and combine data of different types, such as records, logs, documents, audio, images, and video. To get the job done, enterprises have to employ a team of specialized resources, leading to delays. And, ultimately, the resulting analytics are difficult for people across the organization to understand. Metanautix is addressing an enterprise’s entire data ecosystem, speeding up time to analysis, providing greater access across the organization, and encouraging collaboration.

Can you tell us a bit about the technology that Quest is based on? What would we see when look under the hood?

My co-founder and CTO Toli and I worked at Facebook and Google – for me it was Dremel (the backend to BigQuery and other systems) and for Toli it was the photo processing backend for timeline, a huge system.  But those experiences also helped us realize that many of those systems are aimed at somewhat specialized organizations.  So, we set out to build a system that works in a more traditional enterprise environment.  For example, we spent substantial time on being able to run on-premises rather than just in the cloud.
We can run on small machines and laptops, as well as big servers, and thousands of machines in data centers. And it’s the same technology inside that can analyze JSON and text alongside excel, nosql, etc. We also combined Toli’s background in images and computer graphics to ask the question: why is media data treated differently?  We can query raw images, video, and audio like ordinary tables that can be joined with traditional data.

Why SQL?

Our solution marries the high-level functionality and ease of use found with standard SQL with next generation distributed computing, re-imagining SQL for the new world of big data. People often have a somewhat restricted view of SQL: traditionally, it requires importing data into the database, then running queries. We’re re-imagining it to say: what if you didn’t have to do a difficult import step before you started querying? We make it easy to point Metanautix Quest at any data and start querying right away, no matter the size of the data or its format. Because SQL is standard, it means we can plug into standard tools like Excel, Tableau, and others through ODBC/JDBC so people who don’t actually write queries can use their standard tools. The deeper advantages of SQL are that it’s a declarative language – it helps you say what to do, not how to do it. So it gives Metanautix an opportunity to optimize your question over a large cluster of machines and schedule it. It also makes it possible to analyze the flow of information more accurately so it’s easy to trace whether the data is being used according to policy. It’s also easier to understand who is reading what data and what they are using it for. It’s also an opportunity to learn from it. 

What have your early customers like Shutterfly been able to do with Quest?

Our work with Shutterfly has been very rewarding for both sides. For an analytics platform like Metanautix, there’s nothing better than to see the systems in use in actual production applications with large companies. Shutterfly was interested in analyzing all the orders they receive through their e-commerce sites to optimize their marketing spend and maximize ROI. It turned to Metanautix Quest to help with multi-touch attribution analysis (MTA) to identify which channels and campaigns are most effective in driving revenue across millions of customers and touchpoints. While this analysis is typically complex, laborious and slow, Metanautix Quest helped them simplify and accelerate the process – reducing wait times from days to minutes. By writing the entire pipeline in SQL, it became easier for more people on the team to understand the computation and work on it.  As a consumer-oriented business with seasonal spikes in revenue, this ability to iterate models quickly is key to impacting millions in sales. Metanautix’s data compute engine enabled Shutterfly to continually refine its marketing campaigns at a speed that improves their e-commerce business. They’ll be giving a talk with us at the Tableau conference in Seattle that’s in full swing like now.

How do you imagine the field of big data analytics to change over the next years?

What’s going to happen next in analytics is going to be very exciting.  Toli + I feel like we’ve glimpsed into the future a bit because companies like Google, Facebook, Microsoft where we worked operate at such a scale, that they often have to build the future a bit to keep going.  There’s a great William Gibson quote that says something like “The future is here, it’s just not widely deployed yet”. But how things actually go is always surprising.  For instance, one of the things that I think is already happening but will become much more common is for many more people in an organization to work with data. There’s just so much more data coming in through mobile, sensors, devices, web systems, etc.  It’s no longer going to be possible to wait for data to arrive at a warehouse, or reservoir; people will need to just go get it where it is.  I think the other big changes will be in that people will become even more educated about the technology and what it enables for them. At the moment, people think of cloud, and storage, and analytics, and big data as being somewhat tied together, partly because that’s how they started. But lots of technology is going to get disaggregated so that people can use just the part they need without being locked in to the parts they don’t want.  In some sense that’s what Metanautix is doing as well.  We’re disaggregating or unbundling the execution and computation part of the database so people can use it wherever they want.  So, there will be a lot more flexibility so that enterprises and all organizations can adapt more quickly to an evolving environment.
Author
Coman Hamilton
Coman was Editor of JAXenter.com at S&S Media Group. He has a master's degree in cultural studies and has written and edited content for numerous news, tech and culture websites and magazines, as well as several ad agencies.

Comments
comments powered by Disqus