jobsascode.io
Learn how to implement Jobs-as-Code
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What is Jobs-as-Code?
Jobs-as-Code is a DevOps approach to making workflows versionable, testable and maintainable – helping developers, engineers and SREs collaboratively define, schedule, manage and monitor application workflows in production.
Code jobs using any text editor or IDE:
Perform automated testing using existing testing framework
...all in an automated CI/CD pipeline.
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Start Developing
Jobs-as-Code Tutorials
What Can You Do With Jobs-as-Code?
Real-World Implementation
Resource Library
Jobs-as-Code Community
Developer Tools
Use your existing DevOps tools to build application automation while building your business logic, saving time and improving app quality.
Take a
Jobs-as-Code
Approach
Use your existing build tools
to build the automation
Deploy to downstream
environments
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2
1
Operational Tasks
Operational Tasks
Using CI/CD Solutions to Manage Control-M Jobs
Using CI/CD Solutions to Manage Control-M Jobs
Install/Configure Control-M Components in Clouds/Containers
Install/Configure Control-M
Components in Clouds/Containers
IDE Access to Control-M Services
IDE Access to Control-M Services
API Gateways and Control-M
Automation API
API Gateways and Control-M Automation API
JSON Samples
JSON Samples
Jobs-as-Code Tutorials
& Code Samples
Tutorials that implement real-world Jobs-as-Code use
cases, using Control-M Automation API
What Can You Do With Jobs-as-Code?
Real-World Implementation
Click on the icon to view more videos.
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View Channel
Resource Library
White Papers
White Papers
Industry Information
Industry Information
Webinars/Podcasts
Webinars/Podcasts
Jobs-as-Code
Community Examples
Jobs-as-Code
Examples
Check out Jobs-as-Code in action in the Control-M
Automation API Quickstart repo on GitHub
Share your work and check out the work
of the Jobs-as-Code community on GitHub
Developer Tools
Jobs-as-Code Tools
Jobs-as-Code Tools Documentation
Download the free Control-M workbench
and Automation API CLI.
Get technical details for the Control-M
Automation API.
Download
Read Now
Stay In Touch
jobsascode.io
Documentation
Swagger U
Community GitHub
R&D GitHub
Workbench & CLI
Jobs-as-Code Approach
What is Jobs-as-Code?
What Can You do With
Jobs-as-Code?
Real-World Implementation
Jobs-as-Code Tutorials
Resource Library
Developer Tools
Jobs-as-Code Community
Stay in Touch
Operational Tasks
Using CI/CS Solutions
to Manage Control-M Jobs
Install/Configure Control-M
Components in Clouds/Containers
IDE Access to Control-M Services
API Gateways and Control-M
Automation API
JSON Samples
Documentation | Swagger UI | Community GitHub | R&D GitHub | Workbench & CLI
Tech Notes
Tech Notes
Operational Instrumentation/DevOps
How FinServ can Leverage Control-M to Exploit Cloud Technology & DevOps Methodology
Operational Instrumentation
Even Developers Can Love
Control-M: Application Workflow Orchestration Made Simple
Four Ways Developers can Deliver Better Software Faster
Read Now
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Forrester: Making Business Workflows
First-Class Citizens
Read Now
EMA: Jobs-as-Code Shifts Focus to
Job Scheduling, Workload Automation
Read Now
How to Accelerate DevOps Cycles
And Keep Customers First
Read Now
DevOps Institute Upskilling Report 2020
Read Now
Next-level DevOps: Powered by Workload Automation, Artificial Intelligence and Machine Learning
Watch Now
Orchestrating Business Application Workflows in Kubernetes
Watch Now
View More Blogs
Malwarebytes Fights the War Against Malware with Big Data
Read Now
The Future of Dev and Ops,
According to Leading Voices in IT
Read Now
BMC Helix Control-M:
The Journey Starts Today
Read Now
Amadeus shapes the future of travel with DevOps and Cloud
Read Now
Keep Machine Learning Teams Focused on Data Science, Not Data Processing
Read Now
Control-M Provides Quality Control for BMC’s Customer Zero Data Warehouse
Read Now
Running Control-M
Workloads in Kubernetes
Read Now
Check Status of Agents in Hostgroups
Gather hostgroups, find the agents and check their status automatically
Agentless Scheduling
Define a remote host to run jobs without deploying any scheduling agent
Manipulate Workload Policies
Self-service for flexing workload volume during high-activity or system maintenance
Modify User Roles
Update users or roles based on changes in their responsibilities or HR status
Reports via Automation API
Verify status of agents within hostgroups
Order Jobs From Automation Tools
Submit or manipulate Control-M jobs from other automation tools
More Operational Tasks Tutorials
Get started with Jobs-as-Code in delivery pipeline
Use a Jobs-as-Code approach to embed Control-M artifacts in a CI/CD pipeline
Example of Gitlab CI/CD pipeline for Control-M artifacts
Operational example of a Gitlab pipeline that includes Control-M jobs and Deploy Descriptors
Folder Cleanup Utilities
Python utility for detecting and cleaning up folders in Control-M when a deployment removes folders.
More Using CI/CD Solutions to Manage Control-M Jobs Tutorials
Control-M services from Lambda
Invoke Control-M services from Lambda function triggered by AWS event.
Control-M Server in Kubernetes Pod
Run Control-M/Server as a Kubernetes Service
Control-M Agent in Kubernetes Pod
Run Control-M/Agent with Horizontal Pod Autoscaling
Use Case: Control-M/Agent as Kubernetes DaemonSet
Complete use case example including Python client using Kubernetes API for job submission and monitoring
Maintain and Upgrade Control-M
Install fix packs, upgrade and install optional components
Use Terraform to provision Control-M
Install a Control-M environment on AWS with Terraform
More Install/Configure Control-M Components in Clouds/Containers Tutorials
IDE and code editor integration
Invoke Control-M functions and access code snippets within various IDEs and editors
More IDE Access to Control-M Services Tutorials
Access Control-M REST API Services via an API Gateway
Considerations for accessing Control-M RESTful Web Services via an API Gateway
Apigee API Gateway quick setup
Set up Apigee gateway for use with Control-M Automation API
More API Gateways and Control-M Automation API Tutorials
Miscellaneous JSON Samples
More JSON Samples
View More Blogs
Get started with
Jobs-as-Code in delivery pipeline
Use a Jobs-as-Code approach to embed Control-M artifacts in a CI/CD pipeline
Example of Gitlab CI/CD pipeline for Control-M artifacts
Operational example of a Gitlab pipeline that includes Control-M jobs and Deploy Descriptors
Folder Cleanup Utilities
Python utility for detecting and cleaning up folders in Control-M when a deployment removes folders.
View Tutorials For Operational Tasks
View Tutorials For Using CI/CS Solutions To Manage Control-M Jobs
View Tutorials For Install/Configure Control-M Components In Clouds/Containers
View Tutorials For IDE Access
To Control-M Services
View Tutorials For API Gateways and
Control-M Automation API
Scroll down to find out what you
can do with jobs-as-code!
Scroll down to find out what you
can do with jobs-as-code!
MongoDB Workloads
with Control-M
Read Now
FMongoDB Workloads
with Control-M
Read Now
Control-M and
Kubernetes: Introduction
Read Now
Tech Notes
Orchestrating Food Delivery in Real Time
with Artificial Intelligence
Watch Now
Big Trucks Deliver Big Data at Navistar
Read Now
XebiaLabs and Control-M:
What's the Difference?
Read Now
Data Science and the Future of
Information Management Podcast
Watch Now
Malwarebytes Fights the War Against Malware with Big Data
Read Now
BMC Helix Control-M:
The Journey Starts Today
Read Now
Amadeus shapes the future of travel with DevOps and Cloud
Read Now
Control-M Provides Quality Control for BMC’s Customer Zero Data Warehouse
Read Now
Orchestrating Food Delivery in Real Time
with Artificial Intelligence
Watch Now
CWCM: Integrating Control-M Into a DevOps CI/CD Lifecycle
Use common tools such as Jenkins and git to integrate Control-M into a software development lifecycle.
Control-M and Kubernetes
Read Now
x
Face The Workflow Automation
Gap Head On
Read Now
CWCM: Pushing Control-M Jobs
Watch Now
Control-M vs Ansible:
How are they Different?
Read Now
How to Orchestrate a Data Pipeline on AWS with Control-M from BMC Software
Read Now
Workflow Orchestration vs. Continuous Integration: What’s the Difference?
Read Now
Autonomous Data and Application Workflow Orchestration
Read Now
Control-M and Kubernetes
Read Now
XebiaLabs and Control-M:
What’s the Difference?
Read Now
Workflow Orchestration vs. Continuous Integration: What’s the Difference?
Read Now
Data and Application Workflow Orchestration
Read Now
CWCM: Integrating Control-M Into a DevOps CI/CD Lifecycle
Use common tools such as Jenkins and git to integrate Control-M into a software development lifecycle.
X
Face The Workflow Automation Gap Head On
Read Now
Data Science and the Future of
Information Management
Watch Now
Control-M 20 Overview
Watch Now
Leveraging Control-M to
Exploit New DevOps Practices
Read Now
Simplify Complex Application
Workflows with Control-M 20
Watch Now
Control-M and
Kubernetes: Introduction
Read Now
Big Trucks Deliver Big Data
at Navistar
Read Now
Orchestrating Business Application Workflows in Kubernetes
Watch Now
Operational Instrumentation/DevOps
Read Now
How to Accelerate DevOps Cycles
And Keep Customers First
Read Now
Kong API Gateway
Control-M Automation API as a service behind a Kong API Gateway
The DevOps Trifecta: Combining AI,
ML and Workload Automation for Next-Level Benefits
Read Now
The Future of Dev and Ops,
According to Leading Voices in IT
Read Now
AI & ML: Driving the Next Generation of Innovation in DevOps and Workload Automation
Read Now
Control-M vs Ansible:
How are they Different?
Read Now
AI & ML: Driving the Next Generation of Innovation in DevOps and Workload Automation
Read Now