Ops stands for Operationalization. It aims to align IT with business priorities and streamline the process from product development to end deliverables. It also attempts to reduce the time in whole operational processes in the software industry.

Industry has already been introduced to the facility of Ops. With the introduction of the Cloud, Big Data analytics has become far more complicated and dynamic. DevOps, MLOps, ModelOps and DataOps are adding great value there. But XOps is uniting all the Ops under one umbrella.

According to Gartner Top 10 Data and Analytics Trends of 2021, “the goal of XOps (data, machine learning, model, platform) is to achieve efficiencies and economies of scale using DevOps best practices — and to ensure reliability, reusability and repeatability while reducing the duplication of technology and processes and enabling automation”. ^[1]

Why XOps Is Taking Off

The industry is going through speedy digital and AI transformations. A variety of use cases are emerging in the field of software and Machine Learning. The backbone of these digital solutions is data. To handle Big Data, complex engineering processes are being employed to ensure efficiency and speed.

But enterprises are facing scalability and operational challenges. Systematically productionizing the pipeline has become a major problem – one for which XOps has been introduced as a reliable and scalable solution.

DataOps: Industrializing Data and Analytics

As one DataOps practitioner from a Fortune 50 company says, “DataOps consists of a stream of steps required to deliver value to the customer. We automate those steps where possible, minimize waste and redundancy, and foster a culture of continuous improvement.” ^[2]

DataOps stems from DevOps and Agile practices. It emphasizes the use of short development sprints and self-organizing teams with business involvement. It uses version control systems and code repositories to include parallel development and increase efficiency.

Data Engineers: Leading the XOps March Forward

XOps largely deals with setting up infrastructure for data ingestion and production. Data engineers are the main players in this arena. According to Inside Big Data, “data engineering was the fastest-growing job of 2019, increasing by 50% year-over-year. Fast forward to today and data engineering opportunities are continuing to outpace data scientist roles. Data engineering is at the frontier of the data revolution.” ^[3]

The growth of and need for XOps is fundamentally restructuring the role of data engineers. Creating stable and fast Cloud infrastructures is one of their main focuses.

References

1. Gartner Top 10 Data and Analytics Trends for 2021
2. Eckerson Group: Data Ops: Industrializing Data and Analytics
3. Inside Big Data: XOPs: The Rise of Smarter Tech Operations.

Authored by: Rohan Garg, Consultant at Absolutdata