Okay, so with the Kafka, okay, so I have just told you that we are using, we are pulling the data and transforming the data with the help of Amazon Kinesis and with the Lambda. Now with the transformation, what we are going to do is like, we are not going to pull the data, we are going to ask our source to put that data to our Kafka. So that data will be at a scale, currently might be we are receiving a billion of articles. So with the Kafka, we will be using, we will be receiving billions of articles and that we will be processing into Apache B. So how are we going to do that? Basically, when we process the data into the Lambda, AWS, it's a serverless service, basically. So when we process that data in AWS Lambda, so there is a matching that we do. For each batch that Lambda get triggered. So consider for each batch, there's like 25 articles getting processed. And for each Lambda getting executed or is getting invocated, we need to pay to the AWS. So the scale that we have, we can go to a certain because there is a certain limit on AWS accounts to go for the Lambda execution of the Lambda concurrency that we have. Okay, when it comes to scale and process the data to a faster extent, we are using, we're going to deploy our services to the Kubernetes side. That will pull or that will consume data from the Kafka. Okay, whatever the data has been pushed from the source side, let's say 1 billion articles, okay, has been pushed. So we can scale based on the load or you can say that based on the requirement or based on the certain time period, we can scale our executors at certain level. And all those executors will be working in the Kubernetes cluster as certain amount of autoscaling with the help of KEDA and Carpenter will be applied over there. So we will be autoscaling or we have, we will be keeping some query based on the Rafa and Prometheus. So we will be doing that. But the thing is that we can scale with the less amount of money and less amount of time and a less amount of configurations, basically. So we'll be having the scale in that way. Okay. Again, there is again the scale in terms of how much data we are consuming, we can easily scale the Kafka cluster wherever it is required. Let's say we have 10 machines running, let's say having each machines having 2 TB of storage. Okay. And let's say it's 10 TB to 20 TB. If ever we need that our source is going to put a lot of data, we can easily scale our MSK cluster from 10 machines to 20 machines. So easily that Kafka will be scalable and our executors are already scalable. We can scale that replication factor as and whenever it is needed. So that's how the data pipeline that we're going to scale. Thank you.
