Databases play a pivotal role in enterprise computing, yet the variety of formats and the growth of data and its complexity make extracting value from them tricky.

JavaScript Object Notation,  or JSON, is a powerful and common data representation, but it has its intricacies. Putting this representation into an elastic search or relational database can be problematic, so what businesses tend to do is to “pick and choose” what they want or just store it as a BLOB, according to Thomas Hazel (pictured, left), founder and chief technology officer of ChaosSearch Inc.

“What you really want is the power the relational analysis of that JSON data, and that’s where Chaos comes in, where we expand those data streams,” he said. “We do it in a relational way so all that tooling you’ve been built to know and love, now you can have access to it,” he said. “So, if you’re doing proprietary API as your JSON data, you’re not using Looker, you’re not using Tableau, you’re doing some type of proprietary, probably ETLing now on the backend.”

Hazel and Ed Walsh (pictured, right), chief executive officer of ChaosSearch, spoke with Dave Vellante, host of theCUBE, SiliconANGLE Media’s livestreaming studio, for a CUBE Conversation. They discussed JSON-related challenges, how ChaosSearch addresses them, and how its solutions differ from other vendors. (* Disclosure below.)

Index and data refinery tools are essential

JSON’s popularity has increased as businesses modernize their IT infrastructure and applications. Enterprises going cloud native and using Kubernetes are increasingly adopting JSON-based document databases like MongoDB, according to Walsh.

“But if you’re not keeping the data, what people are doing with data scientists is they’re just doing leveling, they’re saying, ‘I’m going to only keep the first four things,’” he explained. “If you cut it off, you’ll lose all that fidelity, all that data, so it’s really important data to have.”

Allowing enterprises to better consume this data is the goal of ChaosSearch. It discovers and catalogs any type of data found in S3, including JSON, and its indexing functionality has the ability to automatically model it. Also, are no imposed limits to the amount of data to store.

“Our philosophy is simply stream your data into your cloud storage, your data lake, and with our index technology and our data refinery, you get to create views, dynamically, instantly, whether it’s a terabyte or petabyte, and describe how you want your data to be consumed, in a relational way or an Elasticsearch way. Both are consumable through our data refinery,” Hazel concluded.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s CUBE Conversations. (* Disclosure: This segment was sponsored by ChaosSearch Inc. Neither ChaosSearch nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE