DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations. DataOps incorporates the Agile methodology to shorten the cycle time of analytics development in alignment with business goals. DevOps focuses on continuous delivery by leveraging on-demand IT resources and by automating test and deployment of software. This merging of software development and IT operations has improved velocity, quality, predictability and scale of software engineering and deployment. Borrowing methods from DevOps, DataOps seeks to bring these same improvements to data analytics. DataOps utilizes statistical process control (SPC) to monitor and control the data analytics pipeline. With SPC in place, the data flowing through an operational system is constantly monitored and verified to be working. If an anomaly occurs, the data analytics team can be notified through an automated alert. DataOps is not tied to a particular technology, architecture, tool, language or framework. Tools that support DataOps promote collaboration, orchestration, quality, security, access and ease of use. History DataOps was first introduced by Lenny Liebmann, Contributing Editor, InformationWeek, in a blog post on the IBM Big Data & Analytics Hub titled "3 reasons why DataOps is essential for big data success" on June 19, 2014. The term DataOps was later popularized by Andy Palmer of Tamr and Steph Locke. DataOps is a moniker for "Data Operations." 2017 was a significant year for DataOps with significant ecosystem development, analyst coverage, increased keyword searches, surveys, publications, and open source projects. Gartner named DataOps on the Hype Cycle for Data Management in 2018. Goals and philosophy The volume of data is forecast to grow at a rate of 32% CAGR to 180 Zettabytes by the year 2025 (Source: IDC). DataOps seeks to provide the tools, processes, and organizational structures to cope with this significant increase in data. Automation streamlines the daily demands of managing large integrated databases, freeing the data team to develop new analytics in a more efficient and effective