David vs Goliath.
Coke vs Pepsi.
Apple vs Microsoft.
Ellen Ripley vs All the Aliens.
When it’s all said and done, DataOps vs. DevOps may not go down in history as one of THE all-time classic confrontations. But in certain circles (namely, ours), these two practices get comparatively pitted against each other quite frequently.
On the surface, this may be due to the fact that the two terms sound similar.
But the comparison may also be due to the fact that DataOps and DevOps share some important methodologies (in addition to some distinct differences).
So, let’s compare and contrast the two to ensure what they entail and how each serves a critical role in many data-driven organizations. And, along the way, let’s touch on data quality, which, despite their differences, can be seen as a mission-critical tie that binds them both together.
How we define DataOps
The term DataOps is short for Data Operations—a collaborative data management practice aimed at improving the communication, integration, and automation of data from data sources (e.g., data warehouses, APIs, etc.). This benefits data consumers, end-users like data scientists, data analysts, and decision-makers.
In principle, DataOps actually borrows from DevOps (which we will define next, promise) in addition to Agile development and lean manufacturing with the goal of keeping data use aligned with business goals.
In practice, DataOps practitioners leverage Agile approaches to designing, implementing, and maintaining distributed data architectures which in turn support a wide range of open-source tools and frameworks. Practitioners will champion continuous delivery, simplicity, responsiveness, and that which keeps data teams and consumers happy and productive.
In short, DataOps practitioners work to make Big Data make a big difference within organizations.
How we define DevOps
Unlike DataOps, DevOps is not a portmanteau (a word blending the sounds and meaning of two different words, which is also a great word to know for Scrabble). Instead, the term DevOps refers to the conceptual blend of software development and IT operations. It carries a focus on shortening the systems development lifecycle in an organization while optimally delivering features, fixes, and updates in accordance with business objectives.
In principle, DevOps emphasizes the cultural shift organizations must take to promote better communication and collaborations between software devs, operations, and other stakeholders. The goal of this cultural shift is to foster a team-oriented approach to IT service delivery, one that supports rapid IT service delivery and quality assurance.
In practice, DevOps teams leverage tools and processes like continuous integration (CI), continuous delivery (CD), and infrastructure as code (IaC) in order to build, test, and release software faster, and more reliably.
In short, DevOps engineers champion workflow and IT process optimization to build better stuff faster.
DataOps and DevOps: Shared methodologies
Let’s begin with what DataOps and DevOps have in common, despite the fact the two target different aspects of IT and business operations. As far as methodologies go, both Ops embrace:
Agile practices: DataOps and DevOps both borrow heavily from Agile principles, promoting adaptability, iterative development, and customer-centric approaches. As such, both practices encourage short, manageable cycles of development and delivery, enabling teams to react quickly to changes and feedback.
Collaboration and communication: Both practices, at their core, aim to dismantle silos within organizations—part of fostering a culture of holistic communication across departments and teams.
Automation: As a means to streamline processes, DataOps and DevOps practitioners leverage automation whenever able to reduce manual errors, improve efficiency, and enable teams to focus on high-value activities.
Continuous Integration and Continuous Delivery (CI/CD): Although they’re applied to different types of workflows respectively, DataOps and DevOps both leverage CI/CD practices to ensure the continuous integration of data sources, ongoing testing of data quality, and the consistent delivery of data insights.
Monitoring and feedback loops: The implementation of monitoring tools and feedback loops are common to DataOps and DevOps, as they ensure that performance is continually measured and all insights gained can be used to improve respective processes, products, and services.
Version control: Finally, practitioners of both DataOps and DevOps employ version control systems to manage changes. Managing changes to data schemas and analytics models (in DataOps) or code (with DevOps) facilitates better team cohesion and ensures rollbacks are possible in the case of errors or issues.
DataOps vs DevOps: Key differences
The key differences between DataOps and DevOps largely revolve around their distinct areas of focus, tools practitioners employ, and the ultimate objectives of each practice.
Differences: Primary focus
DataOps:
- Concentrates on data analytics and data management
- Focuses on optimizing the entire data lifecycle
- Optimizes the reliability, speed, and quality of data analytics due to its emphasis on automation, integration, and collaboration
DevOps:
- Centers on software development and IT operations
- Optimizes the speed, reliability, and quality of software delivery by dismantling silos between dev and operations teams
- Promotes collaboration and continuous improvement through feedback loops
Differences: Objectives
DataOps:
- Employs principles to streamline the data delivery process
- Emphasizes automating repetitive tasks, providing monitoring and feedback in real-time
- Seeks to improve business outcomes through faster time-to-insight, enhanced agility, and better collaboration between teams
DevOps:
- Works to improve software delivery through synergizing software development and IT operations
- Employs automation and continuous feedback to increase time-to-market
- Improves customer satisfaction by establishing and maintaining optimal software quality
Differences: Tools and technology
DataOps:
- Uses tools and tech related to data analytics, processing, governance, and security
- Places a priority on metadata management, data cataloging, and ensuring high-quality data for analytics, dashboards, and data-driven decision-making
DevOps:
- Focuses on tools for CI/CD, version control, and code management
- Emphasizes security applications and infrastructure management
- Seeks to improve efficiency and reliability by automating the software development lifecycle
Differences: Roles
DataOps:
- Builds trust and collaboration between data providers (data engineers, IT operations) and data consumers (business users, data scientists, analysts)
- Promotes data self-sufficiency within an organization
DevOps:
- Brings data engineers, developers, and IT operations teams to actively and sustainably reduce product dev costs
- Aims to decrease the duration of release cycles to better meet business demands
Differences: Outcomes
DataOps:
- Aims to bolster data-driven decision-making by enabling quick and consistent delivery of high-quality actionable insights. In doing so, DataOps stresses the importance of data governance, quality, and security.
DevOps:
- Maximizes customer satisfaction by enhancing product quality while accelerating overall software delivery. DevOps focuses on reducing the time and costs associated with development and operations while increasing software reliability.
Why data quality is the tie that binds
Data quality is crucial to both DataOps and DevOps because it directly impacts the reliability, efficiency, and effectiveness of data analytics and software development processes. While DataOps focuses on data management and analytics, and DevOps centers on software development and IT operations, both disciplines rely on high-quality data to achieve their objectives.
Additionally, data contracts play a significant role in ensuring data quality. By defining and enforcing the schema, format, and usage of data, they facilitate better collaboration and reduce errors in both DataOps and DevOps environments.
Data Quality in DataOps
In DataOps, data quality is fundamental because it ensures that the data used for analytics and decision-making is accurate, complete, consistent, timely, relevant, and valid. High-quality data is essential for:
Decision making: Accurate and reliable data enables businesses to make informed decisions that can lead to improved business outcomes.
Ensuring efficiency: By automating data quality checks and integrating them into continuous data pipelines, DataOps practices help identify and rectify data issues early, thus reducing the time and resources spent on correcting data-related problems.
Building trust: High-quality data builds trust among data consumers, including data scientists, analysts, and business users, ensuring that the insights derived from the data are reliable.
Data Quality in DevOps
In DevOps, although the primary focus is on software development and operations, data quality is indirectly crucial for:
Ensuring quality: High-quality test data is essential for effective testing and quality assurance of software products. Data quality issues in test data can lead to inaccurate test results, masking defects in the software.
Operational efficiency: DevOps practices such as continuous integration and delivery (CI/CD) can benefit from high-quality data by ensuring the software development process is based on accurate and reliable data, leading to more efficient operations and reduced errors.
DataOps vs. DevOps vs. Data Quality: Ensuring optimal excellence
While DataOps and DevOps operate on different frequencies within an organization, both promote and seek to optimize data quality in their own ways. As the role of data within organizations grows increasingly important, both Data and DevOps can increasingly benefit from the drafting and enforcement of robust data contracts.
Data Contracts as Dev and DataOps enhancement
As agreements that define the schema, format, and usage of data between data producers and consumers. This makes data contracts beneficial to both practices for several reasons:
Ensuring data quality: The data contract drafting process involves defining clear expectations for data between all relevant stakeholders. As such, they become instrumental in maintaining the quality of data as it moves through an organization’s processes and systems.
Preventing data issues: When implemented and enforced correctly, data contracts prevent a multitude of common issues from occurring. Issues relevant to both DataOps and DevOps include schema changes and data format incompatibilities that can easily disrupt data pipelines or the software development process.
Facilitating internal collaboration: The alignment that data contracts facilitate between organizational stakeholders fosters an ongoing common understanding of data requirements among different teams, such as software developers, data engineers, and data analysts. And, as a rule, this understanding facilitates better collaboration and reduces misunderstandings and errors.
Better collaboration, better innovation: One thing DataOps and DevOps definitely agree on
As organizations mature digitally, having each of the major pieces of the digitally transformative puzzle is no longer enough. Ultimately, being truly innovative and competitive long-term depends on how well those pieces work together.
In this sense, DataOps and DevOps practitioners both benefit from operational frameworks and cultures that promote robust collaboration. But that’s often easier said than done. We break down why in our article, Data Collaboration: What Working Better Together Really Entails.
This article is part of our blog at Gable.ai — where our goal is to create a shared data culture of collaboration, accountability, quality, and governance.
Currently, our industry-first approach to drafting and implementing data contracts is in private Beta.
Curious to learn more? Join the product waitlist to be notified when we launch.
