DataOps vs DevOps: Learn the differences & how to integrate for maximum benefit

At their foundations, DataOps focuses on data quality and availability, while DevOps empowers faster, more reliable application development and deployment. Organizations striving to streamline and optimize both their software development and data management processes are finding ways to intertwine DevOps and DataOps for maximum impact. 

As the “--Ops” categories multiply, DataOps is starting to share center stage with DevOps. The data journeys of business intelligence and AI products are growing exponentially, getting closer in methodology and actual integration with rapid-delivery application development pipelines. 

DataOps and DevOps often have a critical role in the same organization and pipelines. Yet, it's not just software organizations expanding into data-centric products and analytics that explore the crossover. Non-software data companies may have no real application development happening – they’re purely a data product. These teams can still look to DevOps practices for guidance that jumpstarts their DataOps initiatives (or can help optimize existing DataOps platforms).

Whether your organization is deeply rooted in software development or solely focused on data, understanding how DevOps principles can enhance DataOps processes (and where they might misdirect, too) is crucial for staying competitive. Integrating these methodologies can lead to enhanced operational efficiency, reduced errors, and a more cohesive pipeline that aligns both data and application development goals.

“DataOps in 2024 reminds me so much of DevOps in its early days – we see siloed teams, a lot of manual routine tasks, and an overall frustrating experience,” adds Dan Zentgraf, a Product Manager at Liquibase and organizer of DevOpsDays Austin who has been involved in the DevOps world since its inception. “If you’re shifting to DataOps, you need to take advantage of what’s been learned from the evolution of the DevOps movement,” he advises. “That gives you a headstart on the benefits of automation, platform thinking, collaboration, and pipeline visibility.”

What is DevOps?

DevOps is a cultural and operational framework that bridges the gap between software development and IT operations. It aims to create a more collaborative environment where both teams can collaborate seamlessly to deliver software more efficiently and reliably. Central to DevOps is the practice of Continuous Integration and Continuous Delivery (CI/CD), where code changes are automatically tested, integrated, and deployed. This approach significantly reduces the time needed to release new features and updates, allowing for rapid iterations and faster time-to-market. Additionally, DevOps incorporates practices like version control, containerization, and infrastructure as code (IaC) to enhance the consistency and scalability of deployments.

By breaking down traditional silos between development and operations teams, DevOps fosters enhanced communication and collaboration. More streamlined processes ultimately results in higher quality software and more responsive IT services. Continuous monitoring and logging are also crucial for DevOps, ensuring that system performance and application behavior are constantly tracked and optimized. Through these practices, DevOps improves the efficiency of software delivery and enhances IT services' overall reliability and stability.

The most common job titles associated with DevOps include DevOps Engineer, Site Reliability Engineer (SRE), Automation Architect, Release Manager, and Cloud Engineer. These roles are critical for implementing and maintaining the practices that make DevOps successful.

What is DataOps?

DataOps is a relatively new methodology that adapts principles from agile development, DevOps, and lean manufacturing to the domain of data analytics. The primary goal of DataOps is to enhance the quality, speed, and reliability of data analytics by fostering a culture of collaboration and integration among data engineers, data scientists, and other data professionals. Similar to DevOps, DataOps emphasizes the automation of processes—in this case, the automation of data pipelines. This ensures that data is efficiently and accurately processed from ingestion to delivery, supporting timely and reliable analytics.

Key practices within DataOps include data integration, continuous testing and monitoring of data flows, and the implementation of robust data governance and quality controls. By leveraging these practices, DataOps breaks down silos between various data roles, enabling better collaboration and more efficient data management. Additionally, DataOps utilizes tools for orchestration, version control, and real-time monitoring to enhance the ability to deliver actionable insights quickly and reliably. This approach not only streamlines data management but also enables organizations to respond more swiftly to changing business needs, fostering more informed, data-driven decision-making.

The most common job titles associated with DataOps include Data Engineer, DataOps Engineer, Data Analyst, Data Architect, and Machine Learning Engineer. These roles are integral to ensuring that data pipelines are robust, scalable, and capable of delivering high-quality data for business insights.

Where do DataOps and DevOps align?

DataOps originates from the data management perspective, while DevOps comes from the software development world. They both focus on improving efficiency, quality, and collaboration, though – and with the explosive rise of data-driven applications and business analytics, data and software are more intertwined than ever. 

Both practices also prioritize automation, which lends itself well to integrating across these teams for the most powerful and reliable IT ecosystem. 

Automation

DevOps hinges on automation in CI/CD pipelines, where automated testing, integration, and deployment accelerate release cycles and improve reliability. DataOps, similarly, automates data pipelines, including data ingestion, integration, and ETL/ELT (Extract, Transform, Load; Extract, Load, Transform) processes, to ensure accuracy, efficiency, and minimal manual intervention, thus enhancing overall data quality.

Continuous improvement

DevOps practices iterative development, feedback loops, and retrospectives to continuously refine development, delivery, deployment, and monitoring workflows. DataOps applies similar principles, focusing on the continuous monitoring and refinement of data processes to optimize data flow and analytics. While application code needs to avoid breaking the live environment, data needs to remain accurate and actionable while transforming to meet business and data product needs. 

Collaboration

It’s all about breaking down the silos that exist in development, data, and operations teams. DataOps and DevOps empower better communication, more productive teamwork, and stronger pipeline integration. All of that combines to deliver higher-quality data and software products. 

Version control

In DevOps, version control systems like Git are used to manage and track changes in code, so teams have a single source of truth that enables traceability and rollback capabilities. Similarly, DataOps employs version control for data processing scripts, models, and configurations, ensuring reproducibility and the ability to revert/rollback to previous versions if needed. In both arenas, version control gives teams the confidence and space to innovate, experiment, and collaborate without disrupting the rest of the system.

Observability

All this tracking, automating, collaborating, and versioning leaves a trail of metadata. DataOps and DevOps teams both leverage workflow metrics for visibility, analytics, and monitoring of the entire pipeline (ideally). DevOps uses extensive monitoring and logging to track system performance and detect issues early. DataOps adopts these practices to monitor data pipeline performance, detect data quality issues, and ensure reliability.

Agile and Lean principles

Agile and Lean principles are foundational to DevOps and DataOps, driving their focus on efficiency, adaptability, and continuous improvement. Agile methodology focuses on small, incremental changes with continuous and immediately incorporated feedback. Lean principles focus on trimming the fat – inefficiencies – from the workflow and adding value at every step. 

DevOps applies these methodologies to deliver small, incremental software changes rapidly, allowing teams to quickly adapt and minimize waste. Similarly, DataOps streamlines the data journey by pushing small changes to datasets and database schemas and integrating with downstream teams to optimize quality, relevance, and accuracy that supports the most impactful business insights and data-driven user experiences. 

Crossover with MLOps

Machine Learning Operations, MLOps, taps both DataOps and DevOps practices to manage the lifecycle of machine learning models from development to deployment, monitoring, and optimization. MLOps emphasized automation, continuous integration, and better, safer, easier collaboration – specifically in the context of deploying and maintaining ML models. Similarly, it takes a DataOps-inspired methodology for data pipeline management, data quality, and governance, as the quality and consistency of data are critical for training and deploying accurate models.

MLOps makes sure machine learning models and products operate and deploy reliably, scalably, and efficiently while maintaining data integrity. It’s all about more efficient, integrated workflows that can be continuously optimized. 

The table below distills the perspective of DataOps and DevOps teams in each category. 

DataOps & DevOps similarities comparison table

With an understanding of the DataOps/DevOps crossover, the differences between these practices becomes more clear, too. 

DataOps vs. DevOps: What’s the difference?

Diving into the difference between DataOps and DevOps begins with the starkest difference: data vs (application) development. 

It’s also about the tools, deliverables, integrations, expertise, and metrics that each practice requires. 

Domain focus: data vs development

Both practices aim to streamline and integrate operations – but one focuses on the data journey and the other on software development cycles. DevOps is centered on integrating and automating processes between software development and IT operations teams to enhance efficiency and reliability of development. It focuses on delivering applications and updates faster and with higher quality. 

DataOps is dedicated to improving the quality, speed, and reliability of data analytics and protecting data integrity throughout its entire data journey. It seeks to automate data management and integration processes to ensure that high-quality data is consistently delivered for analytics, reporting, and machine learning purposes.

Tech stack

DevOps commonly uses CI/CD pipelines, monitoring and logging tools, and collaboration platforms to streamline the software delivery process. Tools like Jenkins, Git, Docker, and Kubernetes are typical in a DevOps environment. DataOps relies on tools designed for data integration, ETL/ELT, data quality, governance, and data pipeline automation. Examples include Apache NiFi, Talend, and dbt (data build tool), which help in managing and orchestrating data flows from ingestion, to aggregation, to delivery.

Deliverables

The output of DevOps is high-quality, reliable software experiences and optimal developer experiences. DevOps delivers software applications, system updates, and improvements to infrastructure, ensuring that the development and operations processes are tightly integrated and efficient.

The output of DataOps also includes optimal employee experiences but shifts focus to delivering data sets, analytics reports, data models, and machine learning products. Still, those aspects need to be reliable, timely, and accurate for optimal data-driven decisions and experiences. 

Integrations

The thread continues – DataOps and DevOps diverge in how they iterate with the rest of the organization. DevOps integrates development tools like Integrated Development Environments (IDEs) and code repositories with deployment and monitoring tools creates a seamless pipeline from code creation to production deployment. DataOps integrates  tools for data ingestion, processing, and storage, forming a streamlined data pipeline that ensures data is consistently ready for analytics and other applications.

Expertise 

DevOps teams are typically composed of software developers, system administrators, cloud infrastructure managers, and other software-geared roles, while DataOps starts with data engineers, architects, managers, and analysts. DevOps professionals need to be proficient in scripting, configuration management, containerization, and orchestration. DataOps teams instead look for skills in data modeling, ETL/ELT processes, and familiarity with big data technologies.

Metrics

Essential DevOps metrics include:

• Deployment frequency
• Lead time for changes
• Mean time to recovery (MTTR)
• Change failure rate

And can include many more – especially when DevOps extends to the database for change management metrics.

Critical DataOps metrics include:

• Pipeline performance
• Data quality
• Delivery timeliness
• Issue resolution time

These metrics give teams a foundation for measuring and optimizing reliable, efficient, and accurate data flows. 

DataOps vs. DevOps differences comparison table

When synergized – integrated across pipelines and teams, as available – DataOps and DevOps amplify their benefits. 

Benefits of DataOps + DevOps coordination

The pipelines of application development teams get closer and more intertwined with those of data teams every day. As data becomes the product through machine learning models, these teams are even collaborating on data-driven software experiences that don’t just benefit from DataOps/DevOps synergy – they need it.  

When DataOps and DevOps unify their perspectives, they can expect benefits including:

• A more comprehensive IT strategy that ensures a seamless workflow from application development to data management, providing a holistic view of the IT landscape and business value (and risk)
• Optimal collaboration without silos – fostering shared responsibility for application and data quality, leading to more cohesive operations and more reliable deployments
• Higher system reliability thanks to frequent, reliable software updates with continuous data validation, reducing errors and ensuring consistency
• Faster time-to-market by accelerating application development and ensuring faster delivery of high-quality data for quicker decision-making and stronger data-driven products
• Better agility since both sides of the pipeline support iterative development and continuous feedback, enabling more responsive and data-driven decision
• More efficient resource optimization since they can be audited across a larger team to fine-tune operations across applications and data infrastructure while also planning for scalability
• Stronger, easier compliance with robust and integrated processes for both software and data management
• Streamlined IT management with a structured approach to handling the complexities of both applications and data in modern IT environments

Unifying DataOps and DevOps also means bringing these principles to data stores, which evolve more quickly than ever before due to the needs of both teams and their distinct audiences.

Extending DataOps & DevOps to database change management

This careful strategic integration of DevOps and DataOps teams, tech, and principles can fall apart if it's not a holistic approach. For many organizations, there’s a missing link: the database.

As applications evolve and AI platforms crave fresh and better data, the structures of databases throughout the pipeline – including standard SQL and modern NoSQL options specialized for developers, data engineers, and others – change more frequently than ever before. Much of the schema migration burden tends to be handled manually by database administrators (DBAs), and that drastically slows down an otherwise rapid pipeline.

Integrating DataOps and DevOps practices into database change management is essential for modern organizations that rely on speedy software development and high-quality data analytics. By extending CI/CD automation, version control, governance, and observability capabilities to change management workflows, teams can streamline their database change processes, ensuring that both application code and database schema updates are managed with the same rigor and efficiency.

A database DevOps platform like Liquibase functions across teams to eliminate the traditional friction between developers, data engineers, and DBAs while ensuring reliability and minimizing errors. This alignment not only accelerates development cycles but also ensures that data pipelines are robust, scalable, and capable of delivering accurate insights. 

A unified process with visibility, traceability, and control across teams means:

• Application development teams can deploy schema updates seamlessly through CI/CD pipelines and automatically communicate the changes to downstream data engineers and analysts
• DBAs oversee schema changes using automated version control and testing, ensuring alignment with application needs while preventing conflicts that could disrupt downstream data operations
• Data engineers and analysts can easily accept and request new schema changes, allowing them to quickly and safely integrate new data models and platforms
• Development, data, and database teams operate in a simplified, synchronized, automated workflow with easy collaboration, faster releases, and enhanced reliability of development, data, and database systems

By harmonizing the workflows between development and data teams, Liquibase helps organizations maintain data integrity, improve collaboration, and ultimately achieve a more cohesive IT strategy that supports both software delivery and data-driven products and decisions.