DataOps and DevOps are collaborative approaches between developers and IT operations teams. The trend started with DevOps first. This communication and collaboration approach was then applied to data processing. Both methods argue that collaboration is the primary approach for application development and IT operations teams, but they target different operation areas.
DataOps is an agile method for building and implementing a data architecture that supports open-source tools and platforms in production. The goal is to extract benefits from big data. It focuses on IT operations and software development teams with data engineers, scientists, and analysts. The data scientists might collaborate to develop ways to increase desired business outcomes with their data. At the same time, other team members can point out what the company needs.
This approach utilizes several IT fields, including data creation, transformation, extraction, data quality, governance, and access control. There are no special software tools available, but frameworks and toolkits to support this methodology.
Comparison: DataOps vs DevOps
DataOps and DevOps are approaches that apply similar techniques in different fields.
In DevOps, all teams come together by sharing common goals. Both teams have similar priorities and expertise; they can more easily focus on creating high-quality products. DevOps and DataOps have a shared commitment to break up data silos and focus on inter-team communication. The latter is a subset of DevOps that includes members who deal with data, such as data scientists, engineers, and analysts. These approaches are complementary, not opposed.
The main difference between DataOps and DevOps is their maturity. DevOps has been around for over a decade, with organizations widely adopting and using this model for development. While the data version of it is a relatively new model and strategy, this field is subject to the rapidly changing nature of data.
The DataOps principles
DataOps includes both the business side and the technical side of the organization. The importance of data in the business requires almost the same audibility and governance as other business processes; therefore, greater involvement of other teams is required. These teams have different motivations, and it is essential to consider the goals of both teams. This approach enables data teams to focus on data discovery and analytics while allowing business professionals to implement appropriate governance and security protocols.
Optimizing code structures and distribution is only a part of the big data analytics puzzle. DataOps aims to shorten the end-to-end cycle time of data analytics, from the origin of ideas to creating charts, graphs, and models that add value. The data lifecycle depends on people in addition to tools. To be effective, collaboration and innovation must be managed. To this end, data operations incorporate agile development practices into data analytics so that data teams and users work together more efficiently and effectively.
What problem does DataOps solve?
DataOps is not just DevOps applied to data analytics. It promises that data analytics can achieve what software development achieved with DevOps. In other words, when data teams use new tools and methodologies, they can deliver massive improvements in quality and cycle time.
DataOps focuses on an organization’s data and getting the most out of it. The focus of this data can target anything from identifying marketing areas to optimizing business processes. Statistical process control (SPC) monitors and validates the consistency of the analytical pipeline. By doing this, SPC improves data quality by ensuring that all anomalies and errors are caught immediately. Breaking down the communication and organizational walls is not just the responsibility of one team or the other. Both teams need to work together to get more out of data with common goals.
What is a DataOps engineer?
DataOps engineers establish and maintain the data sourcing and usage cycle by defining and supporting the work processes and technologies that others employ to source, transform, communicate, and act on data.
DataOps engineers are responsible for the company’s information architecture. They’re in charge of creating an environment where data development can occur. They develop the technologies that data engineers and analysts use to build their products. Engineers also help data engineers with workflow and information pipeline design, code reviews, as well as all-new processes and workflows for extracting insights from data.
What is DataOps as a Service?
DataOps as a Service is a managed services platform that combines DataOps components with multi-cloud big data and data analytics management software. These components construct scalable, purpose-built big data platforms that adhere to stringent data privacy, security, and governance standards.
DataOps as a service entails real-time data insights. It shortens the time to develop data science applications, allowing for improved communication and collaboration across teams and team members. Increasing transparency necessitates the use of data analytics to predict all potential scenarios. This service aims for processes to be repeatable and reusable code utilized whenever feasible, resulting in improved data quality.