The MAKANA Python Function (MPF) accelerates and simplifies the roll-out of long-running Python scripts in Azure’s serverless compute cloud infrastructure.
Data scientists and big data developers often write scripts that take several hours and sometimes days to complete execution. These scripts might also require large compute resources like Virtual Machines (VMs), CPU and RAM which are challenging to set-up and take-down on-demand, resulting in high IT costs.
The MAKANA Python Function extends the current Azure serverless compute technology and allows you to focus on business logic and innovation by giving you the ability to execute long-running async Python workloads without having to invest the time to build the compute infrastructure that is required. You simply send an HTTP Post request with your Python script and Python environment definition to the MPF URL and MPF takes care of the rest.
1. Use your existing Python script
With MPF, you do not need to re-write your Python code to fit into the structure required by Azure Functions. You can use the exact same code that runs on your laptop or desktop and execute it on scalable cloud resources
2. Ensure scalability and stability of your code
Write only the code that truly matters to your business, MPF will take care of the scale and stability components. This allows you to build microservice-friendly applications than can easily scale with the demand on your scripts.
3. Improve your end-to-end development experience
Take advantage of a complete, end-to-end development experience – from building and debugging locally on major platforms like Windows, macOS, and Linux to deploying and monitoring in the cloud. Setup continuous integration and continuous delivery (CI/CD) for automated DevOps.
4. Take advantage of Azure’s integration programming capabilities
Using triggers and models of Azure serverless compute environment, you can respond to events and seamlessly connect to other services
5. Control your cloud costs
Using MPF to execute your Python scripts, you can benefit from the automated and flexible scaling driven by the workload volume ensuring that cloud spending is always under control