{"id":56547,"date":"2025-12-12T08:39:17","date_gmt":"2025-12-12T08:39:17","guid":{"rendered":"https:\/\/www.devopsschool.com\/blog\/?p=56547"},"modified":"2026-02-21T08:30:10","modified_gmt":"2026-02-21T08:30:10","slug":"top-10-bioinformatics-workflow-managers-features-pros-cons-comparison","status":"publish","type":"post","link":"https:\/\/www.devopsschool.com\/blog\/top-10-bioinformatics-workflow-managers-features-pros-cons-comparison\/","title":{"rendered":"Top 10 Bioinformatics Workflow Managers: Features, Pros, Cons & Comparison"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Introduction<\/h2>\n\n\n\n

Bioinformatics Workflow Managers are specialized software platforms designed to orchestrate, automate, and manage complex biological data analysis pipelines<\/strong>. In modern life sciences, researchers rarely run a single script in isolation. Instead, they execute multi-step workflows involving sequencing data processing, quality control, alignment, variant calling, annotation, and downstream analysis. Workflow managers bring structure, reproducibility, and scalability to these processes.<\/p>\n\n\n\n

Their importance has grown rapidly with the explosion of next-generation sequencing (NGS)<\/strong>, proteomics, metagenomics, and multi-omics research. Without workflow managers, teams struggle with inconsistent results, poor documentation, manual errors, and difficulty scaling analyses from a laptop to high-performance computing (HPC) or cloud environments.<\/p>\n\n\n\n

Real-world use cases include genome and transcriptome analysis<\/strong>, clinical genomics pipelines, drug discovery research, population-scale studies, and regulated biomedical research. When choosing a Bioinformatics Workflow Manager, users should evaluate reproducibility, scalability, ease of use, language flexibility, execution environments (local, HPC, cloud), container support, security, and community maturity<\/strong>.<\/p>\n\n\n\n

Best for:<\/strong>
Bioinformatics Workflow Managers are ideal for bioinformaticians, computational biologists, data scientists, research labs, biotech startups, pharmaceutical companies, and academic institutions<\/strong> handling complex or large-scale biological data pipelines.<\/p>\n\n\n\n

Not ideal for:<\/strong>
They may be unnecessary for simple, one-off analyses<\/strong>, very small datasets, or teams without technical expertise where fully managed analysis services or point-and-click tools may be more appropriate.<\/p>\n\n\n\n

\n\n\n\n

Top 10 Bioinformatics Workflow Managers Tools<\/h2>\n\n\n\n
\n\n\n\n

1 \u2014 Nextflow<\/h3>\n\n\n\n

Short description:<\/strong>
Nextflow is a powerful workflow manager designed for scalable, reproducible bioinformatics pipelines. It is widely adopted in both academic and enterprise environments.<\/p>\n\n\n\n

Key features:<\/strong><\/p>\n\n\n\n

    \n
  • Domain-specific language optimized for bioinformatics<\/li>\n\n\n\n
  • Native support for containers (Docker, Singularity)<\/li>\n\n\n\n
  • Cloud and HPC execution support<\/li>\n\n\n\n
  • Strong pipeline modularity<\/li>\n\n\n\n
  • Versioned workflows and reproducibility<\/li>\n\n\n\n
  • Large ecosystem of prebuilt pipelines<\/li>\n<\/ul>\n\n\n\n

    Pros:<\/strong><\/p>\n\n\n\n

      \n
    • Excellent scalability from laptop to cloud<\/li>\n\n\n\n
    • Strong industry adoption and tooling ecosystem<\/li>\n<\/ul>\n\n\n\n

      Cons:<\/strong><\/p>\n\n\n\n

        \n
      • Learning curve for new users<\/li>\n\n\n\n
      • DSL can feel unfamiliar initially<\/li>\n<\/ul>\n\n\n\n

        Security & compliance:<\/strong>
        Supports container isolation, access controls, and cloud security configurations; compliance depends on deployment environment.<\/p>\n\n\n\n

        Support & community:<\/strong>
        Very strong community, extensive documentation, enterprise support available.<\/p>\n\n\n\n

        \n\n\n\n

        2 \u2014 Snakemake<\/h3>\n\n\n\n

        Short description:<\/strong>
        Snakemake is a Python-based workflow manager emphasizing simplicity and reproducibility for data-driven bioinformatics pipelines.<\/p>\n\n\n\n

        Key features:<\/strong><\/p>\n\n\n\n

          \n
        • Python-based workflow definitions<\/li>\n\n\n\n
        • Automatic dependency resolution<\/li>\n\n\n\n
        • Native HPC and cloud execution<\/li>\n\n\n\n
        • Conda and container integration<\/li>\n\n\n\n
        • Rule-based workflow structure<\/li>\n\n\n\n
        • Excellent debugging capabilities<\/li>\n<\/ul>\n\n\n\n

          Pros:<\/strong><\/p>\n\n\n\n

            \n
          • Easy for Python users<\/li>\n\n\n\n
          • Highly readable workflows<\/li>\n<\/ul>\n\n\n\n

            Cons:<\/strong><\/p>\n\n\n\n

              \n
            • Less opinionated structure for very large teams<\/li>\n\n\n\n
            • Performance tuning may require expertise<\/li>\n<\/ul>\n\n\n\n

              Security & compliance:<\/strong>
              Varies by execution environment; supports containerized execution.<\/p>\n\n\n\n

              Support & community:<\/strong>
              Strong academic community, comprehensive documentation.<\/p>\n\n\n\n

              \n\n\n\n

              3 \u2014 Cromwell (WDL)<\/h3>\n\n\n\n

              Short description:<\/strong>
              Cromwell is a workflow execution engine for WDL workflows, commonly used in clinical and large-scale genomics.<\/p>\n\n\n\n

              Key features:<\/strong><\/p>\n\n\n\n

                \n
              • Workflow Description Language (WDL)<\/li>\n\n\n\n
              • Cloud-native execution<\/li>\n\n\n\n
              • Strong focus on reproducibility<\/li>\n\n\n\n
              • Proven clinical genomics usage<\/li>\n\n\n\n
              • Parallel execution support<\/li>\n\n\n\n
              • Backend flexibility<\/li>\n<\/ul>\n\n\n\n

                Pros:<\/strong><\/p>\n\n\n\n

                  \n
                • Well-suited for regulated genomics<\/li>\n\n\n\n
                • Clear workflow syntax<\/li>\n<\/ul>\n\n\n\n

                  Cons:<\/strong><\/p>\n\n\n\n

                    \n
                  • Less flexible outside WDL<\/li>\n\n\n\n
                  • Smaller ecosystem than Nextflow<\/li>\n<\/ul>\n\n\n\n

                    Security & compliance:<\/strong>
                    Supports auditability and controlled execution; compliance depends on deployment.<\/p>\n\n\n\n

                    Support & community:<\/strong>
                    Good documentation, moderate community size.<\/p>\n\n\n\n

                    \n\n\n\n

                    4 \u2014 Galaxy<\/h3>\n\n\n\n

                    Short description:<\/strong>
                    Galaxy is a web-based workflow platform offering accessible bioinformatics analysis without extensive coding.<\/p>\n\n\n\n

                    Key features:<\/strong><\/p>\n\n\n\n

                      \n
                    • Graphical user interface<\/li>\n\n\n\n
                    • Large library of bioinformatics tools<\/li>\n\n\n\n
                    • Workflow sharing and reproducibility<\/li>\n\n\n\n
                    • Training and tutorial ecosystem<\/li>\n\n\n\n
                    • Cloud and local deployment options<\/li>\n<\/ul>\n\n\n\n

                      Pros:<\/strong><\/p>\n\n\n\n

                        \n
                      • Beginner-friendly<\/li>\n\n\n\n
                      • Ideal for collaborative research<\/li>\n<\/ul>\n\n\n\n

                        Cons:<\/strong><\/p>\n\n\n\n

                          \n
                        • Limited flexibility for custom pipelines<\/li>\n\n\n\n
                        • Performance constraints at scale<\/li>\n<\/ul>\n\n\n\n

                          Security & compliance:<\/strong>
                          Supports user management and access controls; compliance varies by deployment.<\/p>\n\n\n\n

                          Support & community:<\/strong>
                          Very large global community and training resources.<\/p>\n\n\n\n

                          \n\n\n\n

                          5 \u2014 CWL (Common Workflow Language)<\/h3>\n\n\n\n

                          Short description:<\/strong>
                          CWL is an open standard for describing analysis workflows and tools in a portable, vendor-neutral way.<\/p>\n\n\n\n

                          Key features:<\/strong><\/p>\n\n\n\n

                            \n
                          • Open, community-driven standard<\/li>\n\n\n\n
                          • Portable across execution engines<\/li>\n\n\n\n
                          • Strong container support<\/li>\n\n\n\n
                          • Explicit input\/output definitions<\/li>\n\n\n\n
                          • Emphasis on reproducibility<\/li>\n<\/ul>\n\n\n\n

                            Pros:<\/strong><\/p>\n\n\n\n

                              \n
                            • Vendor-neutral and portable<\/li>\n\n\n\n
                            • Transparent workflow definitions<\/li>\n<\/ul>\n\n\n\n

                              Cons:<\/strong><\/p>\n\n\n\n

                                \n
                              • Verbose syntax<\/li>\n\n\n\n
                              • Requires external execution engines<\/li>\n<\/ul>\n\n\n\n

                                Security & compliance:<\/strong>
                                Depends on execution platform; supports container security.<\/p>\n\n\n\n

                                Support & community:<\/strong>
                                Active standards community, solid documentation.<\/p>\n\n\n\n

                                \n\n\n\n

                                6 \u2014 Toil<\/h3>\n\n\n\n

                                Short description:<\/strong>
                                Toil is a scalable workflow engine designed for large, distributed, and cloud-based bioinformatics pipelines.<\/p>\n\n\n\n

                                Key features:<\/strong><\/p>\n\n\n\n

                                  \n
                                • Supports CWL and WDL<\/li>\n\n\n\n
                                • Distributed execution<\/li>\n\n\n\n
                                • Cloud-native design<\/li>\n\n\n\n
                                • Fault tolerance<\/li>\n\n\n\n
                                • High scalability<\/li>\n<\/ul>\n\n\n\n

                                  Pros:<\/strong><\/p>\n\n\n\n

                                    \n
                                  • Excellent for massive datasets<\/li>\n\n\n\n
                                  • Cloud-optimized architecture<\/li>\n<\/ul>\n\n\n\n

                                    Cons:<\/strong><\/p>\n\n\n\n

                                      \n
                                    • Complex setup<\/li>\n\n\n\n
                                    • Smaller user base<\/li>\n<\/ul>\n\n\n\n

                                      Security & compliance:<\/strong>
                                      Varies by deployment; supports secure cloud environments.<\/p>\n\n\n\n

                                      Support & community:<\/strong>
                                      Smaller but technically strong community.<\/p>\n\n\n\n

                                      \n\n\n\n

                                      7 \u2014 Arvados<\/h3>\n\n\n\n

                                      Short description:<\/strong>
                                      Arvados is a data management and workflow platform focused on reproducibility and data provenance.<\/p>\n\n\n\n

                                      Key features:<\/strong><\/p>\n\n\n\n

                                        \n
                                      • Integrated data management<\/li>\n\n\n\n
                                      • Strong provenance tracking<\/li>\n\n\n\n
                                      • Scalable compute support<\/li>\n\n\n\n
                                      • Secure data access controls<\/li>\n\n\n\n
                                      • Workflow execution support<\/li>\n<\/ul>\n\n\n\n

                                        Pros:<\/strong><\/p>\n\n\n\n

                                          \n
                                        • Excellent data governance<\/li>\n\n\n\n
                                        • Designed for regulated research<\/li>\n<\/ul>\n\n\n\n

                                          Cons:<\/strong><\/p>\n\n\n\n

                                            \n
                                          • Steeper learning curve<\/li>\n\n\n\n
                                          • Heavier infrastructure requirements<\/li>\n<\/ul>\n\n\n\n

                                            Security & compliance:<\/strong>
                                            Strong access control and audit capabilities; compliance depends on setup.<\/p>\n\n\n\n

                                            Support & community:<\/strong>
                                            Enterprise-focused support, smaller open community.<\/p>\n\n\n\n

                                            \n\n\n\n

                                            8 \u2014 Luigi<\/h3>\n\n\n\n

                                            Short description:<\/strong>
                                            Luigi is a Python-based workflow engine originally developed for complex batch pipelines.<\/p>\n\n\n\n

                                            Key features:<\/strong><\/p>\n\n\n\n

                                              \n
                                            • Task-based pipeline design<\/li>\n\n\n\n
                                            • Dependency management<\/li>\n\n\n\n
                                            • Python-native implementation<\/li>\n\n\n\n
                                            • Visualization dashboard<\/li>\n\n\n\n
                                            • Flexible execution environments<\/li>\n<\/ul>\n\n\n\n

                                              Pros:<\/strong><\/p>\n\n\n\n

                                                \n
                                              • Flexible and extensible<\/li>\n\n\n\n
                                              • Suitable for custom pipelines<\/li>\n<\/ul>\n\n\n\n

                                                Cons:<\/strong><\/p>\n\n\n\n

                                                  \n
                                                • Not bioinformatics-specific<\/li>\n\n\n\n
                                                • Requires more manual configuration<\/li>\n<\/ul>\n\n\n\n

                                                  Security & compliance:<\/strong>
                                                  N\/A \u2013 depends on infrastructure.<\/p>\n\n\n\n

                                                  Support & community:<\/strong>
                                                  Active Python community, moderate documentation.<\/p>\n\n\n\n

                                                  \n\n\n\n

                                                  9 \u2014 Airflow (Bioinformatics Use)<\/h3>\n\n\n\n

                                                  Short description:<\/strong>
                                                  Apache Airflow is a general-purpose workflow orchestration platform adapted by some teams for bioinformatics pipelines.<\/p>\n\n\n\n

                                                  Key features:<\/strong><\/p>\n\n\n\n

                                                    \n
                                                  • DAG-based workflows<\/li>\n\n\n\n
                                                  • Extensive scheduling capabilities<\/li>\n\n\n\n
                                                  • Scalable execution<\/li>\n\n\n\n
                                                  • Rich monitoring tools<\/li>\n\n\n\n
                                                  • Plugin ecosystem<\/li>\n<\/ul>\n\n\n\n

                                                    Pros:<\/strong><\/p>\n\n\n\n

                                                      \n
                                                    • Excellent scheduling and monitoring<\/li>\n\n\n\n
                                                    • Enterprise-grade reliability<\/li>\n<\/ul>\n\n\n\n

                                                      Cons:<\/strong><\/p>\n\n\n\n

                                                        \n
                                                      • Not designed specifically for bioinformatics<\/li>\n\n\n\n
                                                      • More overhead for scientific workflows<\/li>\n<\/ul>\n\n\n\n

                                                        Security & compliance:<\/strong>
                                                        Supports enterprise-grade authentication and auditing.<\/p>\n\n\n\n

                                                        Support & community:<\/strong>
                                                        Very large global community and enterprise support.<\/p>\n\n\n\n

                                                        \n\n\n\n

                                                        10 \u2014 Pachyderm<\/h3>\n\n\n\n

                                                        Short description:<\/strong>
                                                        Pachyderm combines containerized workflows with data versioning for reproducible bioinformatics pipelines.<\/p>\n\n\n\n

                                                        Key features:<\/strong><\/p>\n\n\n\n

                                                          \n
                                                        • Data versioning built-in<\/li>\n\n\n\n
                                                        • Container-native pipelines<\/li>\n\n\n\n
                                                        • Kubernetes-based execution<\/li>\n\n\n\n
                                                        • Incremental processing<\/li>\n\n\n\n
                                                        • Strong reproducibility model<\/li>\n<\/ul>\n\n\n\n

                                                          Pros:<\/strong><\/p>\n\n\n\n

                                                            \n
                                                          • Excellent data lineage tracking<\/li>\n\n\n\n
                                                          • Cloud-native design<\/li>\n<\/ul>\n\n\n\n

                                                            Cons:<\/strong><\/p>\n\n\n\n

                                                              \n
                                                            • Kubernetes dependency<\/li>\n\n\n\n
                                                            • Operational complexity<\/li>\n<\/ul>\n\n\n\n

                                                              Security & compliance:<\/strong>
                                                              Supports enterprise security models; compliance varies.<\/p>\n\n\n\n

                                                              Support & community:<\/strong>
                                                              Commercial support available, growing community.<\/p>\n\n\n\n

                                                              \n\n\n\n

                                                              Comparison Table<\/h2>\n\n\n\n
                                                              Tool Name<\/th>Best For<\/th>Platform(s) Supported<\/th>Standout Feature<\/th>Rating<\/th><\/tr><\/thead>
                                                              Nextflow<\/td>Large-scale genomics<\/td>Local, HPC, Cloud<\/td>Pipeline portability<\/td>N\/A<\/td><\/tr>
                                                              Snakemake<\/td>Python-based workflows<\/td>Local, HPC, Cloud<\/td>Simplicity & readability<\/td>N\/A<\/td><\/tr>
                                                              Cromwell<\/td>Clinical genomics<\/td>Cloud, HPC<\/td>WDL standard<\/td>N\/A<\/td><\/tr>
                                                              Galaxy<\/td>Non-programmers<\/td>Web, Cloud<\/td>GUI-driven workflows<\/td>N\/A<\/td><\/tr>
                                                              CWL<\/td>Standardized workflows<\/td>Multi-engine<\/td>Vendor neutrality<\/td>N\/A<\/td><\/tr>
                                                              Toil<\/td>Massive datasets<\/td>Cloud, HPC<\/td>Distributed execution<\/td>N\/A<\/td><\/tr>
                                                              Arvados<\/td>Regulated research<\/td>Cloud, On-prem<\/td>Data provenance<\/td>N\/A<\/td><\/tr>
                                                              Luigi<\/td>Custom pipelines<\/td>Local, Cloud<\/td>Python task orchestration<\/td>N\/A<\/td><\/tr>
                                                              Airflow<\/td>Enterprise scheduling<\/td>Cloud, On-prem<\/td>Monitoring & scheduling<\/td>N\/A<\/td><\/tr>
                                                              Pachyderm<\/td>Cloud-native pipelines<\/td>Kubernetes<\/td>Data versioning<\/td>N\/A<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                              \n\n\n\n

                                                              Evaluation & Scoring of Bioinformatics Workflow Managers<\/h2>\n\n\n\n
                                                              Tool<\/th>Core Features (25%)<\/th>Ease of Use (15%)<\/th>Integrations (15%)<\/th>Security (10%)<\/th>Performance (10%)<\/th>Support (10%)<\/th>Price\/Value (15%)<\/th>Total Score<\/th><\/tr><\/thead>
                                                              Nextflow<\/td>24<\/td>13<\/td>14<\/td>8<\/td>9<\/td>9<\/td>13<\/td>90<\/td><\/tr>
                                                              Snakemake<\/td>22<\/td>14<\/td>13<\/td>7<\/td>8<\/td>8<\/td>14<\/td>86<\/td><\/tr>
                                                              Cromwell<\/td>21<\/td>12<\/td>12<\/td>8<\/td>8<\/td>7<\/td>12<\/td>80<\/td><\/tr>
                                                              Galaxy<\/td>20<\/td>15<\/td>11<\/td>7<\/td>7<\/td>9<\/td>13<\/td>82<\/td><\/tr>
                                                              CWL<\/td>21<\/td>10<\/td>14<\/td>7<\/td>8<\/td>8<\/td>12<\/td>80<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                              \n\n\n\n

                                                              Which Bioinformatics Workflow Managers Tool Is Right for You?<\/h2>\n\n\n\n
                                                                \n
                                                              • Solo users:<\/strong> Snakemake or Galaxy for simplicity<\/li>\n\n\n\n
                                                              • SMBs:<\/strong> Nextflow or Snakemake for scalability<\/li>\n\n\n\n
                                                              • Mid-market:<\/strong> Nextflow, Cromwell, or Pachyderm<\/li>\n\n\n\n
                                                              • Enterprise:<\/strong> Nextflow, Arvados, or Airflow<\/li>\n<\/ul>\n\n\n\n

                                                                Budget-conscious:<\/strong> Open-source tools like Snakemake, CWL
                                                                Premium solutions:<\/strong> Pachyderm, enterprise Nextflow
                                                                Ease of use:<\/strong> Galaxy
                                                                Deep customization:<\/strong> Nextflow, Snakemake
                                                                Security & compliance:<\/strong> Cromwell, Arvados<\/p>\n\n\n\n

                                                                \n\n\n\n

                                                                Frequently Asked Questions (FAQs)<\/h2>\n\n\n\n
                                                                  \n
                                                                1. What is a bioinformatics workflow manager?<\/strong>
                                                                  A tool that automates and manages multi-step biological data analysis pipelines.<\/li>\n\n\n\n
                                                                2. Do I need programming skills?<\/strong>
                                                                  Some tools require coding, while others offer graphical interfaces.<\/li>\n\n\n\n
                                                                3. Are these tools cloud-ready?<\/strong>
                                                                  Most modern workflow managers support cloud execution.<\/li>\n\n\n\n
                                                                4. Which is best for genomics pipelines?<\/strong>
                                                                  Nextflow and Cromwell are widely used in genomics.<\/li>\n\n\n\n
                                                                5. Can workflows be reused?<\/strong>
                                                                  Yes, most tools emphasize reproducibility and sharing.<\/li>\n\n\n\n
                                                                6. Are these tools secure?<\/strong>
                                                                  Security depends on deployment and infrastructure.<\/li>\n\n\n\n
                                                                7. Can they handle large datasets?<\/strong>
                                                                  Yes, many are designed for high-performance and distributed computing.<\/li>\n\n\n\n
                                                                8. Do they support containers?<\/strong>
                                                                  Most modern tools support Docker or similar technologies.<\/li>\n\n\n\n
                                                                9. Are there GUI-based options?<\/strong>
                                                                  Galaxy is the most popular GUI-based platform.<\/li>\n\n\n\n
                                                                10. What is the biggest mistake users make?<\/strong>
                                                                  Choosing a tool without considering scalability and team expertise.<\/li>\n<\/ol>\n\n\n\n
                                                                  \n\n\n\n

                                                                  Conclusion<\/h2>\n\n\n\n

                                                                  Bioinformatics Workflow Managers are essential for reproducible, scalable, and efficient biological data analysis<\/strong>. While tools like Nextflow and Snakemake dominate large-scale research, others excel in accessibility, standardization, or enterprise control.<\/p>\n\n\n\n

                                                                  There is no single \u201cbest\u201d solution for everyone. The right choice depends on team size, technical expertise, data scale, compliance needs, and infrastructure<\/strong>. By focusing on these factors, organizations can select a workflow manager that delivers long-term value and scientific confidence.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                                                  Introduction Bioinformatics Workflow Managers are specialized software platforms designed to orchestrate, automate, and manage complex biological data analysis pipelines. In modern life sciences, researchers rarely run a single script in… <\/p>\n","protected":false},"author":58,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_joinchat":[],"footnotes":""},"categories":[11138],"tags":[18142,18147,18141,18149,18144,18152,18126,18151,18150,18143,18148,18146,18153,18145],"class_list":["post-56547","post","type-post","status-publish","format-standard","hentry","category-best-tools","tag-bioinformatics-pipeline-automation","tag-bioinformatics-pipeline-tools","tag-bioinformatics-workflow-managers","tag-cloud-bioinformatics-workflows","tag-computational-biology-workflows","tag-containerized-bioinformatics-workflows","tag-genomics-workflow-management","tag-hpc-bioinformatics-pipelines","tag-multi-omics-workflow-management","tag-ngs-data-analysis-pipelines","tag-reproducible-bioinformatics-analysis","tag-scalable-bioinformatics-pipelines","tag-scientific-workflow-management-systems","tag-workflow-orchestration-for-bioinformatics"],"_links":{"self":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/56547","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/comments?post=56547"}],"version-history":[{"count":4,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/56547\/revisions"}],"predecessor-version":[{"id":59922,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/56547\/revisions\/59922"}],"wp:attachment":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=56547"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=56547"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=56547"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}