For every function in an organization, leveraging data has become the lifeline needed for optimal decision-making. But given the wide variety and sheer amount of data that enterprises collect, using all of the data is impossible. 

This is one of the biggest bottlenecks for accelerating business and being data-driven, according to Saket Saurabh (pictured), co-founder and chief executive officer of Nexla Inc.

Based on Nexla’s purpose and mission of triggering ready-to-use data in the hands of users, automated data engineering connects the dots through initiatives like collaborative workflows and auto-generated bi-directional connectors.

“Every SaaS application you use becomes the data source, every type of database, every type of user event, anything can be a source of data … it is a tremendous engineering challenge for companies to make the data usable,” Saurabh explained. “Companies just cannot have enough people to write that code to make that data engineering happen. Where we come in with a very unique value is how to start thinking about making this whole process much faster and much more automated.”

Saurabh spoke with theCUBE industry analyst Lisa Martin during the AWS Startup Showcase: “Data as Code — The Future of Enterprise Data and Analytics” event, an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how Nexla optimizes time-to-value by enhancing data usability through automated data engineering. (* Disclosure below.)

Making ready-to-use data a reality

Despite enterprises wanting to make data part of their business, the lack of ready-to-use data becomes a challenge. Nexla fills the void through automated data engineering.

“The problem for companies is that for each of the users and teams, the data is not ready for them to use as it is … there is a lot that goes on before the data can be in their hands,” Saurabh explained. “So data engineering in this case becomes taking data from different places and making it useful.”

One of the biggest challenges is connecting the data systems to attain the desired data. Data engineering plays an instrumental role in making data useful after collecting it from different places, according to Saurabh.

“It is the task of data engineering to consume data from all these different places, formats, APIs and systems, and then somehow unify all the data so that it can be used by the applications that they are building,” he added.

Nexla’s customer base ranges from mid to very large enterprises spread across different sectors like finance, media, management, healthcare, education, logistics, retail and e-commerce.

“One of the top three banks in the country is a big user of Nexla as part of their data stack … we actually sit as part of their enterprise-wide AI platform providing data to their data scientists,” Saurabh stated. “LinkedIn is by far the largest social network … their marketing team leverages Nexla to bring data from different types of systems together as well.”

There is more than meets the eye in data engineering

Data engineering boils down to linking all the data connectivity and flows so that more people can carry out tasks rather than specialized personnel. Nexla helps automate certain data engineering pieces so that teams can accelerate faster.

“Data engineering is basically all the code, the process, and the people that are basically connecting to their system,” Saurabh said. “Data engineering is quite interesting in that … it is difficult to implement the necessary sort of pieces, but it is also very repetitive at some level.”

When it comes to automating data engineering, the journey starts at the auto-generated bi-directional connectors because it entails integrating data systems, Saurabh explained.

“Connecting to data is basically about the gateway to data, the ability to read and write data from different systems … but it is extremely complex because people have to write code to connect to different systems,” he stated. “One part that we have automated is generating these connectors so that you don’t have to write code for that.”

The second phase involves auto-generated data products in the form of Nexsets that present data in a user-readable manner. The data product is packaged in a way that other people can use it easily because it involves adding other structures to the raw materials. Collectively, the data products create the data mesh framework.

“The second part is that the gateway or connector has read the data, but how do you represent it to the user so anybody can understand it?” Saurabh asked. “That’s where the concept of data product comes in … so we also look at auto generating data products. These become the common language and entity that people can understand and work with.”

The third and last phase involves collaborative workflows where the data users work together with the data engineers for optimal results, according to Saurabh.

“Third part is taking all this automation and bringing the human in the loop,” he explained. “No automation is perfect and, therefore, bringing the human in the loop means that somebody who is an expert in data, who can look at it and understand it can now do things which only data systems experts were able to do before.” 

By enabling data engineers access to the same system being utilized by users, Saurabh believes this approach enhances the time-to-value aspect.

“While an engineer will come through APIs, SDK, commands,and interfaces, a data user comes in through a nice no-code user interface,” he stated.

Watch the complete video interview below, part of SiliconANGLE’s and theCUBE’s pre-event coverage of the AWS Startup Showcase: “Data as Code — The Future of Enterprise Data and Analytics” event:

(* Disclosure: TheCUBE is a paid media partner for the AWS Startup Showcase: “Data as Code — The Future of Enterprise Data and Analytics” event. Neither Nexla Inc., a sponsor for theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE