> For the complete documentation index, see [llms.txt](https://docs.bdb.ai/data-pipeline-2/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bdb.ai/data-pipeline-2/data-pipeline/real-time-and-batch-orchestration.md).

# Real-time and Batch Orchestration

Real-time processing deals with streams of data that are captured in real-time and processed with minimal latency. These processes run continuously and stay live even if the data info has stopped.

<figure><img src="https://859511478-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FGDmsjfjJBNqow7Fo97cO%2Fuploads%2FhPrtNYnSPYN7X5B39ThM%2FScreenshot%202022-05-10%20at%201.53.05%20PM.png?alt=media&#x26;token=ac270ba4-8308-4024-bd24-6313de26dd76" alt=""><figcaption><p><strong>Real time data pipeline</strong></p></figcaption></figure>

Batch job orchestration runs the process based on a trigger. In the BDB Data Pipeline, this trigger is the input event. Anytime data is pushed to the input trigger, the job will kick start. After completing the job, the process is gracefully terminated. This process can be near real-time. Also, it allows you to effectively utilise the compute resources.​


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