Control-M for Big Data

As the owner for big data initiatives in your organization you are constantly working to ensure your team has the tools needed to ensure not only implementation success but also analytical value to drive strategic direction and opportunities for competitive advantage. Delivering on schedule and on budget determines the project’s success.

Control-M for Hadoop can help you:

• Justify continued investment, reducing the dependence on costly storage
• Right-size the skilled staff required to deliver big data
• Standardize processes while allowing your team to work with the tools most suited to them

Setting the technical direction and design for your Hadoop initiative is easier said than done. New solutions hit the open source market daily and sorting through what will provide the greatest chance of success in the shortest amount of time can be daunting. Keep your project moving forward with the visibility, accessibility, and security your big data project requires.

Control-M for Hadoop can help you:

• Deliver your Hadoop project faster and with more accuracy, seamlessly connecting to any application and any database across the enterprise
• Automate batch workflow development ensuring fewer complications and the need for revisions by developers
• Enhance the stability and security of your enterprise-grade Hadoop environment

Overseeing the day-to-day development of Hadoop comes with its own challenges. From the infrastructure to the domain expertise, it’s up to you to ensure the end result answers everyone’s questions. The speed and quality of the Hadoop code your team delivers is just one key component for success.

Learn how Control-M for Hadoop can:

• Build and deploy big data pipelines with Jobs-as-Code and seamlessly inject the job workflows in your continuous integration and continuous delivery cycles
• Convert existing workflows to Control-M—scripted, Oozie, or another enterprise scheduler—to maintain continuity and reduce lost cycles
• Ease the handoff of code to Operations, driving a continuous delivery model