Innovation Workload Automation Blog

Introducing Control-M Python Client and Integrations

2 minute read
Joe Goldberg

Every business wants to be data-driven. With the staggering amount of data available to organizations that want to make informed business decisions, those that don’t properly utilize it will be quickly left behind. With companies around the world harnessing the power of data to drive their business forward, becoming a data-driven business is critical to digital transformation.

As an application and data workflow automation and orchestration platform, Control-M has a long history of supporting customers through their digital transformations. That’s why I’m excited to make two announcements that will continue to help Control-M customers with those initiatives.

Control-M Python Client

Many companies struggle to operationalize their data-centric applications. The data scientists and data engineers responsible for these applications are forced spend too much of their time trying to wrangle data from multiple sources with disconnected tools. When those applications get to production, IT operations (ITOps) teams must manage those same disconnected tools to support the new business service.

This is an inefficient and time-consuming approach. Even if it seems to be working okay now, consider the impact to your time-to-market when you need to really scale application updates in production. Why not empower your data teams with Control-M, which is already part of your toolkit?

The new Control-M Python Client allows data engineers and data scientists to leverage Python programming to seamlessly interact with Control-M. They can easily build, test, and promote data workflows with the data-coding language integrated into Control-M through the Control-M Automation API. By putting Control-M to work for both data and operations teams, you can ensure visibility, improve service level agreements (SLAs), and deliver data-driven outcomes faster—at scale—across hybrid and multi-cloud environments.

Data Ecosystem and Cloud Services Integrations

With the increasing adoption of cloud services, cloud providers have made a significant investment in expanding their capabilities to ensure that cloud environments can support their users’ growing data processing needs. That said, connecting the data that resides across multiple clouds and on-premises data storage requires more tools than any single cloud provider can support. To properly orchestrate application and data workflows for all that data, organizations must use a platform that seamlessly supports multiple cloud vendors and enables end-to-end observability.

BMC can help. We’re extending our support for cloud services with four new Control-M cloud integrations that simplify workflow orchestration across platform-as-a-service (PaaS) offerings from the three leading cloud platforms—Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform. The latest integrations include:

In addition, we’re releasing other data ecosystem integrations, including:

These integrations are available as open-source downloads from GitHub. AWS Glue and Azure Data Factory integrations are BMC-supported and others are community-supported.

And that’s only the beginning. Considering the constantly evolving and changing cloud and data space, we plan to continue a strong release cadence for additional integrations. Look for many more updates and announcements in the future.

For more information about these new integrations, check out our updated Control-M Cloud Datasheet or visit bmc.com/it-solutions/control-m-integrations.

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These postings are my own and do not necessarily represent BMC's position, strategies, or opinion.

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About the author

Joe Goldberg

Joe is an IT professional with several decades of experience in the design, development, implementation, sales and marketing of enterprise solutions to Global 2000 organizations. Joe has been active in helping BMC products leverage new technology to deliver market-leading solutions with a focus on Workload Automation, Big Data, Cloud and DevOps.