DataOps presents a holistic approach to designing, building, moving, and utilizing data within an organization. It aims to maximize the business value of data and its underlying infrastructure, both on-premises and in the cloud. DataOps is essential for digital transformation initiatives such as cloud migration, DevOps, open-source database adoption, and data governance.

However, DataOps should not be confused with data operations, which refer to the routine tasks and activities necessary for managing and maintaining an organization’s data infrastructure. Data operations are a crucial part of any data strategy, but DataOps goes beyond these basic tasks to focus on using data to drive business value through continuous improvement and automation.

By adopting a DataOps mindset and approach, organizations can improve the quality and speed of their data-driven decision-making, becoming more agile and responsive to changing business needs. Let’s take a comprehensive look at DataOps first so we can see the bigger picture.

What is DataOps?

DataOps is an iterative technique for building and managing a distributed data architecture that can run a wide variety of open-source applications. DataOps’ mission is to derive value for businesses from large data sets.

It “is a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization. The goal of DataOps is to deliver value faster by creating predictable delivery and change management of data, data models, and related artifacts. DataOps uses technology to automate the design, deployment, and management of data delivery with appropriate levels of governance, and it uses metadata to improve the usability and value of data in a dynamic environment,” according to Gartner.

Embrace SDDC and DevOps to accelerate digital transformation

The DataOps approach, which takes its cue from the DevOps paradigm shift, is focused on increasing the rate at which software is developed for use with large data processing frameworks. DataOps also encourages line-of-business stakeholders to collaborate with data engineering, data science, and analytics teams in an effort to reduce silos between IT operations and software development teams. This ensures that the organization’s data may be utilized in the most adaptable and efficient manner to provide desirable results for business operations.

DataOps integrates many facets of IT, such as data development, data transformation, data extraction, data quality, data governance, data access control, data center capacity planning, and system operations, because it encompasses so much of the data lifecycle. Typically, a company’s chief data scientist or chief analytics officer leads a DataOps team comprised of specialists like data engineers and analysts.

Frameworks and related toolsets exist to support a DataOps approach to collaboration and greater agility, but unlike DevOps, there are no software solutions dedicated to “DataOps.” Tools for this purpose include extract-transform-load (ETL) programs, log analyzers, and system monitors. In addition to open-source software that enables applications to combine structured and unstructured data, tools that support microservices architectures are also commonly connected with the DataOps movement. 

Data operations is not DataOps

With DataOps, decision-makers and decision-making software can benefit from increased cooperation and the rapid supply of data and insights. A key component of DataOps is the automation of procedures, similar to those in DevOps, that promote data sharing and transparency. The term “DataOps” is not meant to imply any sort of auxiliary hardware or software.

In contrast, data operations analyze the big picture. Data and the data pipeline are part of this picture, as are the operational requirements of data availability, integrity, and performance, as well as the hybrid infrastructure on which data lives. The purpose of data operations is to maximize the business value of both the data and the pipeline. What needs to be tested, monitored, analyzed, tuned, secured, etc., is the infrastructure within the pipeline.

How does DataOps work?

DataOps seeks to manage data in line with business objectives by integrating DevOps and Agile methodologies. If increasing lead conversion rate was the objective, for instance, DataOps would arrange data in such a way that better marketing product recommendations could be made. DevOps techniques are used to optimize code, product builds, and delivery, while Agile processes are utilized for data governance and analytics development.

DataOps isn’t just about writing new code; it’s also about streamlining and bettering the data warehouse. DataOps, which is inspired by lean manufacturing, use statistical process control (SPC) to ensure the analytics pipeline is always being monitored and validated. Using SPC, you can rest assured that your statistics are always within reasonable bounds while also improving the speed and accuracy of your data processing. With the use of SPC, data analysts can be notified instantly if an unexpected occurrence or error happens.

What does DataOps as a Service offer?

DataOps as a Service combines managed services for gathering and processing data with a multi-cloud big data/data analytics management platform. With the help of its components, it offers scalable, purpose-built big data platforms that follow best practices in data protection, security, and governance.

Understanding the significance of Data as a Service in a digital-first world

Providing real-time data insights is the definition of data operations as a service. It facilitates improved communication and teamwork between teams and team members and decreases the cycle time of data science applications. It is essential to increase transparency by employing data analytics to foresee any circumstance that could occur. Whenever feasible, processes are designed to reuse code and ensure improved data quality. A single, interoperable data hub is produced as a result of everything.

What’s the role of data operations in a business?

Data operations play a crucial role in supporting and maintaining an organization’s data infrastructure. Some common tasks and activities that are part of data operations include:

• Data ingestion: The process of bringing data into the organization’s data pipeline or storage system.
• Data transformation: The process of cleansing, enriching, and formatting data so that it can be used effectively.
• Data storage: The process of organizing and storing data in a way that is secure, scalable, and accessible.
• Data access: The process of granting users access to data in a controlled and secure manner.
• Data backup and recovery: The process of creating copies of data for disaster recovery purposes.

By ensuring that these tasks are carried out efficiently and effectively, data operations help organizations to derive value from their data and make informed decisions. They also play a crucial role in maintaining the organization’s data infrastructure’s reliability, security, and performance.

Which problems do data operations address in a business?

Data operations can help businesses solve a variety of problems, including:

• Cloud migration issues: Data operations can help ensure that the root cause of performance problems is accurately identified, whether it is due to the cloud environment or other factors.
• Reactive mindset: Data operations can help businesses anticipate performance problems rather than reacting to them, improving user experience in business-critical applications.
• Skills gaps: Data operations can help organizations address shortages in key areas such as cloud architecture, IT planning, and orchestration and automation.
• Disruptions to the data pipeline: Data operations can help businesses ensure that data continues to flow smoothly and uninterrupted, even when facing internal systems or data ingestion issues.
• Self-service data consumption: Data operations can help organizations make it easier for line-of-business (LOB) users to locate, access, and interpret the right data from multiple sources.
• Database changes: Data operations can help organizations apply DevOps practices to make changes to their data structures more quickly and safely without causing bottlenecks or introducing risk.
• Balancing high availability and costs: Data operations can help organizations find a balance between maintaining “always on” mission-critical applications and managing costs.
• Transformation of operations teams: Data operations can help operations teams embrace change and grow from being experts in the database to be experts in the data, leveraging new technologies like autonomous databases, AI, and machine learning.

What does a data operations engineer do?

A data operations engineer is responsible for designing, deploying, and maintaining an organization’s data infrastructure. This includes tasks such as:

• Setting up and configuring data storage systems such as databases, data lakes, and data warehouses.
• Designing and implementing data pipelines to move data between different systems.
• Monitoring and troubleshooting data infrastructure to ensure it is running smoothly and efficiently.
• Implementing security measures to protect data and prevent unauthorized access.
• Collaborating with data analysts, data scientists, and other stakeholders to understand data requirements and ensure that data is being used effectively.

In addition to these technical tasks, data operations engineers may also be responsible for managing budgets, developing strategies for data management, and communicating with stakeholders about data-related issues. They may work in a variety of industries, including finance, healthcare, retail, and technology.

Data operations engineer salary

Data is the new gold and the industry demands goldsmiths. Did you know that the average gross income for a data center or operations manager in Germany is EUR 74.763 per year, or EUR 36 per hour, as reported by Salaryexpert.com? Furthermore, they receive an average bonus of 5,256 € per year. Estimated wages based on a survey of businesses in Germany and their anonymous workers. The average compensation for an entry-level data center or operations manager (1-3 years of experience) is 52.556 Euros. In contrast, the average compensation of a senior data center or operations manager (8+ years of experience) is 92.791 €.

Key takeaways

Data operations;

• Refer to the processes and systems used to manage and handle data within a business. This includes tasks such as data collection, storage, processing, analysis, and visualization.
• Are important for businesses because they enable organizations to make informed decisions based on accurate and up-to-date data. This can lead to improved efficiency, better customer service, and increased profitability.
• Require careful planning and management to ensure that data is handled in a secure and compliant manner. This includes protecting against data breaches and ensuring that data is only used for authorized purposes.
• Can be complex, especially for businesses with large amounts of data or those that operate in regulated industries. In these cases, it may be necessary to invest in specialized tools and technologies to manage data effectively.
• Are a key component of a successful data strategy. By investing in effective data operations, businesses can improve their ability to make data-driven decisions and drive business growth.

Conclusion

As data volume, velocity, and variety grow, new insight-extraction techniques and procedures are required. IDC anticipates that the volume of data created will increase to 163 zettabytes by 2025, with 36% of that data being organized. The current technologies, procedures, and organizational structures are ill-equipped to handle the tremendous growth in data inputs and the rising value expectations for data output. As a greater proportion of the workforce requires access to this data to execute their tasks, a shift in philosophy is required to break through cultural and organizational barriers to deliver scalable, repeatable, and predictable data flows.

This change is occurring due to the DataOps revolution. Companies would be urged to adopt the processes and technologies necessary to avoid data-related headaches in the future. Data operations facilitate creating scalable, repeatable, and predictable data flows for every use case. Organizations can use data operations to enable the integration, automation, and monitoring of data flows for data engineers, analysts, and business users.

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 methodology

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.