{"id":40521,"date":"2026-03-31T11:37:19","date_gmt":"2026-03-31T09:37:19","guid":{"rendered":"https:\/\/www.huwise.com\/?post_type=glossary&#038;p=40521"},"modified":"2026-03-31T11:37:37","modified_gmt":"2026-03-31T09:37:37","slug":"devops","status":"publish","type":"glossary","link":"https:\/\/www.huwise.com\/en\/glossary\/devops\/","title":{"rendered":"DevOps"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">In the context of data management, DevOps principles have given rise to <\/span><b>DataOps<\/b><span style=\"font-weight: 400;\">, the application of DevOps practices to data pipelines and analytics workflows, making DevOps fluency increasingly important for <\/span><a href=\"https:\/\/www.huwise.com\/en\/glossary\/data-architecture\/\"><span style=\"font-weight: 400;\">data engineers<\/span><\/a><span style=\"font-weight: 400;\"> and data platform teams.<\/span><\/p>\n<h2><b>Core Principles of DevOps<\/b><\/h2>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Continuous integration (CI): <\/b><span style=\"font-weight: 400;\">Developers merge code changes into a shared repository frequently, with automated builds and tests run on every commit, catching integration issues early.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Continuous delivery (CD): <\/b><span style=\"font-weight: 400;\">Code changes are automatically built, tested, and prepared for release, reducing the time and risk involved in deployment.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Infrastructure-as-code (IaC): <\/b><span style=\"font-weight: 400;\">Infrastructure is provisioned and managed through code rather than manual processes, enabling version control, repeatability, and audit trails.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Monitoring &amp; observability: <\/b><span style=\"font-weight: 400;\">Systems are continuously monitored for performance, availability, and errors, mirroring the principles of data observability applied to data pipelines.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Collaboration &amp; shared responsibility: <\/b><span style=\"font-weight: 400;\">Development, operations, and, in data contexts, data teams share ownership of the systems they build and the outcomes they produce.<\/span><\/li>\n<\/ul>\n<h2><b>DevOps in the Data Context: DataOps<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">As data pipelines have grown in complexity, the DevOps toolkit has been adapted for data workflows:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Version-controlled data pipelines: <\/b><a href=\"https:\/\/www.huwise.com\/en\/glossary\/data-pipeline\/\"><span style=\"font-weight: 400;\">Data pipelines<\/span><\/a><span style=\"font-weight: 400;\"> are treated as code, tested, version-controlled, and deployed with CI\/CD automation.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Pipeline testing: <\/b><span style=\"font-weight: 400;\">Automated data quality tests are embedded in the <\/span><a href=\"https:\/\/www.huwise.com\/en\/glossary\/extract-transform-load-etl\/\"><span style=\"font-weight: 400;\">ETL<\/span><\/a><span style=\"font-weight: 400;\"> process, catching data issues before they reach consumers.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Environment management: <\/b><span style=\"font-weight: 400;\">Dev, staging, and production data environments are managed programmatically, reducing the risk of manual configuration errors.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Orchestration: <\/b><span style=\"font-weight: 400;\">Tools like Apache Airflow or dbt manage workflow dependencies, scheduling, and failure handling at scale.<\/span><\/li>\n<\/ul>\n<h2><b>DevOps Skills in Enterprise Data Teams<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Modern <\/span><a href=\"https:\/\/www.huwise.com\/en\/glossary\/data-architecture\/\"><span style=\"font-weight: 400;\">data engineers<\/span><\/a><span style=\"font-weight: 400;\"> are increasingly expected to bring DevOps competencies, containerization (Docker, Kubernetes), CI\/CD pipeline design, infrastructure automation, and <\/span><a href=\"https:\/\/www.huwise.com\/en\/glossary\/cloud-computing\/\"><span style=\"font-weight: 400;\">cloud engineering<\/span><\/a><span style=\"font-weight: 400;\"> skills, to their data infrastructure work. This convergence makes the boundary between cloud engineer, data engineer, and DevOps engineer increasingly fluid.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For organizations investing in <\/span><a href=\"https:\/\/www.huwise.com\/en\/glossary\/modern-data-stack\/\"><span style=\"font-weight: 400;\">modern data stacks<\/span><\/a><span style=\"font-weight: 400;\"> and <\/span><a href=\"https:\/\/www.huwise.com\/en\/glossary\/data-marketplace\/\"><span style=\"font-weight: 400;\">data marketplace<\/span><\/a><span style=\"font-weight: 400;\"> platforms, DevOps practices provide the reliability and velocity needed to deploy, iterate, and scale data infrastructure in alignment with fast-moving business requirements.<\/span><\/p>\n","protected":false},"featured_media":0,"parent":0,"template":"","meta":{"_acf_changed":false,"inline_featured_image":false},"tags":[],"letter":[351],"class_list":["post-40521","glossary","type-glossary","status-publish","hentry","letter-d"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - 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