DevOps
DevOps is a set of cultural practices, organizational principles, and technical methods that integrate software development (Dev) and IT operations (Ops) into a unified, continuous workflow. By breaking down the traditional silos between development and operations teams, DevOps enables organizations to deliver software faster, with higher reliability, and with the ability to rapidly respond to changing business needs.
In the context of data management, DevOps principles have given rise to DataOps, the application of DevOps practices to data pipelines and analytics workflows, making DevOps fluency increasingly important for data engineers and data platform teams.
Core Principles of DevOps
- Continuous integration (CI): Developers merge code changes into a shared repository frequently, with automated builds and tests run on every commit, catching integration issues early.
- Continuous delivery (CD): Code changes are automatically built, tested, and prepared for release, reducing the time and risk involved in deployment.
- Infrastructure-as-code (IaC): Infrastructure is provisioned and managed through code rather than manual processes, enabling version control, repeatability, and audit trails.
- Monitoring & observability: Systems are continuously monitored for performance, availability, and errors, mirroring the principles of data observability applied to data pipelines.
- Collaboration & shared responsibility: Development, operations, and, in data contexts, data teams share ownership of the systems they build and the outcomes they produce.
DevOps in the Data Context: DataOps
As data pipelines have grown in complexity, the DevOps toolkit has been adapted for data workflows:
- Version-controlled data pipelines: Data pipelines are treated as code, tested, version-controlled, and deployed with CI/CD automation.
- Pipeline testing: Automated data quality tests are embedded in the ETL process, catching data issues before they reach consumers.
- Environment management: Dev, staging, and production data environments are managed programmatically, reducing the risk of manual configuration errors.
- Orchestration: Tools like Apache Airflow or dbt manage workflow dependencies, scheduling, and failure handling at scale.
DevOps Skills in Enterprise Data Teams
Modern data engineers are increasingly expected to bring DevOps competencies, containerization (Docker, Kubernetes), CI/CD pipeline design, infrastructure automation, and cloud engineering skills, to their data infrastructure work. This convergence makes the boundary between cloud engineer, data engineer, and DevOps engineer increasingly fluid.
For organizations investing in modern data stacks and data marketplace platforms, DevOps practices provide the reliability and velocity needed to deploy, iterate, and scale data infrastructure in alignment with fast-moving business requirements.
Learn more
Our latest Q&A blog explains why businesses should choose a cloud-based option for their data product marketplace in order to increase consumption, minimize administration, effectively protect data, and scale to meet growing demand.
Today’s enormous growth in data volumes brings a new challenge for businesses – how can they harness and use this data at scale? Organizations are therefore looking for solutions that can transform their data assets by making them available and useful, accelerating and improving performance to benefit the entire business.
Data products transform organizational data into value by making information understandable, consumable and accessible to AI and business users. Their creation and delivery requires collaboration between data management, business and IT teams - we explain the roles involved and how they work together.
Our latest Q&A blog explains why businesses should choose a cloud-based option for their data product marketplace in order to increase consumption, minimize administration, effectively protect data, and scale to meet growing demand.
Today’s enormous growth in data volumes brings a new challenge for businesses – how can they harness and use this data at scale? Organizations are therefore looking for solutions that can transform their data assets by making them available and useful, accelerating and improving performance to benefit the entire business.
Data products transform organizational data into value by making information understandable, consumable and accessible to AI and business users. Their creation and delivery requires collaboration between data management, business and IT teams - we explain the roles involved and how they work together.
