* The monitoring * Workflows * Production * Scheduling the shifts * Timeframes for specific roles and management Time differences are important because we have some overseas clients. That is why we are using Batch Impact Manager for the Control-M, which is very helpful for us. It detects potential blocks in advance, delays, and errors. That helps us to optimize the scheduling, then the batch workload processing as well. It is pretty easy to create, integrate, and automate data pipelines. It is user-friendly, not rocket science. That is what I like about the Control-M, and specifically Batch Impact Manager. You will need some orientation and need to know what you are doing if you are integrating your system, and this solution makes it easier. We use Control-M Python Client and cloud data service integrations with GCP, which is pretty hassle-free. There aren't any problems or compliance issues. It is pretty easy to retrieve the data, do conversions, etc. They are on-time, and there is not much of a delay. The engineers on our team say that it is pretty easy to build, test and promote data workflows with the data coding language integrated into Control-M through the Control-M automation API. The ease of integration is eight out of 10. Python is the main language that our database managers and data engineers are using along with some other languages. The Control-M interface is user-friendly and easy to use. Orientation-wise, it is easy for data engineers to adapt.
