Okay, so the reason could be the lagging our data processing, real-time data processing. First might be the case of failure of the data or the data that incoming data is too huge. Okay, so we need to identify those things first. First thing will be like, let's take an example, if we are getting any failures, okay, let's say your data is getting filled and it's been pushed to the BLQ, for sure that is consuming, that will be taking a lot of time to for the failures to come back to your executor or you need to change, if that has been handled correctly or not, okay, again, the issue in the same way will be like, we might be enhancing some of the data with some API with some endpoints or we will be pushing the data to the certain endpoints, okay, on certain database clusters. So we need to check that, are those taking an optimal time or are those taking a required time or what, the time duration, we can check that, okay, from logs, we can definitely get that. Again, there will be case that we might be receiving a lot of data from our source side that the data has been pushed a lot in the last hour. So from, if we want that data is getting lagged and we want that to be processed, we can increase our executors, parallel executors from Kafka, whatever the executors we are working might be a PySpark or might be the Apache Beam or Apache Flink, that we can increase the executors at a time to process the data faster, okay, but that needs to be identified. So verbally, we can only guess that if any failure, any time duration, latency between our services that we need to identify and also the data that is coming in, is it in the right format, is it in the right size and the number of data that we are receiving at a certain level of time, we need to check all those things. So, yeah.
