An Azure service that stores unstructured data in the cloud as blobs.
I would first investigate the storage usage to understand what is causing the 40 TB consumption in the mbgdpdatalakeprod storage account.
A good starting point would be to review the storage account metrics and identify which containers and directories are using the most space. From there, check the data growth pattern and the last modified dates of the files to determine whether this is expected growth or unexpected data accumulation.
I would also review the workloads that write data into this Data Lake, such as Azure Data Factory, Synapse Analytics, Databricks, or other applications. In many cases, the root cause is a pipeline continuously generating new files, duplicate data being stored, or temporary/staging data that is not being cleaned up.
Once the source of the growth is identified, the remediation depends on the data type. For example, older data that still needs to be retained can be moved to Cool or Archive storage using Lifecycle Management policies, while unnecessary temporary or obsolete data can be removed based on the retention requirements.
To prevent similar situations in the future, I would recommend configuring Azure Monitor alerts for storage growth, reviewing retention policies, and ensuring that each dataset has a defined owner and lifecycle.
The main objective is to identify which process is generating the data and apply the appropriate retention and monitoring controls rather than only increasing storage capacity.