Okay, so first let me explain you the thing that we do. Okay, so what we do is like We source the data. We source the data via the EP of our sources like Consider there is a news channel. Okay, they give us the data, they are the source. So we hit their EPI put their data to our kinesis from there the trigger base lambda get invocated and transform the data so for the cost optimization for the scalability purpose and also for the faster processing of the data. We are going to transform all this pipeline into the Kafka and Apache teams Apache data pipeline. So what we are going to do is like we gonna set up a Kafka cluster We have set up a Kafka cluster basically So we gonna ask our source to put all the data to a Kafka topic in our required topic They will put their data. Okay from there the Apache team will take that data and transform that data So this is how the Kafka gets into picture. Okay, why Kafka comes into picture is because of the cost optimization, performance the scalability and all those stuffs are there for the Kafka team. Okay, so we have involved Kafka in that sense Apart from that we are going to create some dashboard for the analysis purpose for that we gonna put some analytics and consumption logs to the Kafka, how much data we are consuming What kind of a data we are consuming. All those logs will be pushed to a Kafka topic and from there we will be creating a dashboard that will help us to identify analyze the Analyze the data in a much better way. So that's where the Kafka comes into picture for us. Okay, so
