DevOps & DataOps in 2025: Automation, CI/CD, and the Evolution of Data Engineering
Introduction
As businesses increasingly rely on data-driven decision-making, the role of automation in software development and data management has never been more critical. In 2025, we’re witnessing the convergence of DevOps and DataOps—two practices that are reshaping how organisations build, deploy, and maintain data projects. This article explores emerging trends in DataOps and Data Engineering, dives into best practices for setting up CI/CD pipelines, and clarifies what DevOps is (and isn’t).
What Are DevOps and DataOps?
DevOps: Beyond the Buzzword
DevOps is a set of cultural philosophies, practices, and tools that increase an organisation’s ability to deliver high-velocity applications and services. Contrary to popular belief, DevOps is not merely a collection of tools (like Puppet, Chef, or Jenkins); it’s a holistic approach to software development and IT operations that emphasises collaboration, continuous improvement, and automation.
Common Misconceptions:
- Not Just Tools: While tools are essential, DevOps is fundamentally about breaking down silos between development and operations.
- Not a New Job Title: Being labelled a “DevOp” isn’t a separate technical skill—it’s about adopting a collaborative, agile mindset across your team.
DataOps: DevOps for Data
DataOps extends DevOps principles to the data analytics lifecycle. It emphasises automation, continuous integration, and continuous data pipeline deployment (CI/CD). With DataOps, data teams work closely with IT and software engineers to streamline data ingestion, processing, and analysis, ensuring that data is high-quality, secure, and readily available for decision-making.
Emerging Trends in DataOps & Automation
- Integration of CI/CD for Data Pipelines: Modern data projects increasingly adopt CI/CD practices. Automated pipelines allow data teams to continuously test, deploy, and update data workflows—ensuring changes are reliably rolled out without disrupting ongoing analytics.
- Containerisation and Orchestration: With container technologies like Docker and orchestration tools like Kubernetes, teams can deploy data services consistently across various environments. This approach improves scalability and fault tolerance for data pipelines.
- Infrastructure as Code (IaC): Tools like Terraform and Ansible enable teams to manage infrastructure through code. This automates deployments and makes the entire data architecture reproducible and easier to manage.
- Automation Platforms: Automation tools such as Puppet, Chef, and Ansible continue to evolve. They’re increasingly used to manage configuration, deploy applications, and even orchestrate complex data workflows.
- Observability and Monitoring: Continuous monitoring tools (e.g., Prometheus, Grafana) now integrate with CI/CD pipelines, providing real-time data pipeline performance and health insights. This ensures that any issues are caught early and resolved quickly.
Setting Up CI/CD Pipelines for Data Projects: Best Practices & Code Examples
Why CI/CD Matters for Data Projects
Just as in software development, a CI/CD pipeline for data projects automates data pipeline testing, integration, and deployment. Every change—from updating ETL scripts to deploying new data models—can be automatically tested and deployed with minimal manual intervention. The benefits include faster turnaround times, reduced errors, and improved reliability.
Best Practices for Data CI/CD
- Automate Testing: Include unit tests for your data transformation scripts and integration tests for your data pipelines.
- Version Control: Use Git (or similar) to manage changes in your data scripts and pipeline configurations.
- Use Containerisation: Package your data tools and dependencies using Docker to ensure consistency across environments.
- Implement Rollbacks: Ensure that your pipeline can automatically revert to the last stable version if a deployment fails.
- Monitor Continuously: Integrate monitoring tools to alert you if data quality or pipeline performance degrades.
Code Example: A Simple Jenkins Pipeline for a Data Project
Below is an example Jenkinsfile (written in Groovy) that automates the testing and deployment of a data pipeline:
pipeline {
agent any
environment {
DATA_PROJECT = 'my-data-pipeline'
DOCKER_IMAGE = 'myorg/data-pipeline:latest'
}
stages {
stage('Checkout') {
steps {
git url: 'https://github.com/myorg/data-pipeline.git', branch: 'main'
}
}
stage('Test') {
steps {
sh 'pytest tests/'
}
}
stage('Build Docker Image') {
steps {
script {
docker.build(DOCKER_IMAGE)
}
}
}
stage('Deploy') {
steps {
sh 'kubectl rollout restart deployment/data-pipeline-deployment'
}
}
}
post {
success {
echo 'Data pipeline deployed successfully!'
}
failure {
echo 'Deployment failed, rolling back!'
// Rollback logic here
}
}
}
This pipeline performs the following steps:
- Checkout: Pulls the latest code from the repository.
- Test: Runs unit tests using pytest.
- Build: Creates a Docker image for the data pipeline.
- Deploy: Uses kubectl to restart the deployment in a Kubernetes cluster, ensuring the new image is deployed.
Code Example: GitLab CI/CD YAML for a Data Project
If you’re using GitLab, here’s an example .gitlab-ci.yml for a similar setup:
stages:
- test
- build
- deploy
test:
stage: test
image: python:3.9
script:
- pip install -r requirements.txt
- pytest tests/
build:
stage: build
image: docker:latest
services:
- docker:dind
script:
- docker build -t myorg/data-pipeline:latest .
- docker push myorg/data-pipeline:latest
deploy:
stage: deploy
image: google/cloud-sdk:alpine
script:
- echo "$KUBE_CONFIG" | base64 -d > kubeconfig
- export KUBECONFIG=kubeconfig
- kubectl rollout restart deployment/data-pipeline-deployment
only:
- main
This configuration:
- Runs test in a Python environment.
- Builds and pushes a Docker image.
- Deploy the updated image to a Kubernetes cluster using Google Cloud SDK.
What DevOps Is (and Isn’t)
What It Is:
- A Culture: DevOps is about collaboration between development and operations teams, fostering a culture of shared responsibility.
- Automation-Centric: It leverages automation to reduce manual work, improve consistency, and accelerate delivery cycles.
- Continuous Improvement: DevOps encourages continuous feedback and iterative improvements in both code and processes.
What It Isn’t:
- Not Just a Toolset: While automation tools are critical, DevOps is not solely defined by the tools you use.
- Not a Magic Bullet: Implementing DevOps practices won’t solve all operational issues overnight—it requires a cultural shift and ongoing commitment.
- Not Exclusive to “Techies”: DevOps is a collaborative effort that spans multiple roles, from software engineers to system administrators and beyond. It’s not about one “DevOp” person; it’s about how teams work together.
Is the DevOps Engineer Different from Other Techies?
The role of a DevOps engineer is indeed specialised, but it doesn’t mean they possess a mysterious or exclusive skill set. What sets them apart is their focus on bridging the gap between development and operations, ensuring that systems are both reliable and rapidly deployable. They work with standard technologies and principles—but apply them in a way that promotes automation, collaboration, and continuous delivery across the entire lifecycle of an application or data project.
Conclusion
The fusion of DevOps and DataOps will revolutionise data engineering in 2025. By adopting automation techniques, embracing CI/CD pipelines, and using modern tools like Puppet, Jenkins, and Kubernetes, organisations can streamline their data processes, reduce errors, and respond swiftly to changing business needs. DevOps is not just a collection of tools or a role for a select few; it’s a cultural transformation that empowers entire teams to work collaboratively and continuously improve.
Implementing these best practices in your data projects can help you unlock new levels of efficiency and agility. As automation becomes increasingly integral to both software and data operations, staying ahead of these trends will ensure your organisation remains competitive in a fast-evolving landscape.