Swapnil Powar

Hi. Uh, I'm. I'm currently working as a software engineer at the Green Oaks. Currently, I'm working as a back end developer engineer there, and, currently, I'm handling a back end task. Yeah. Uh, I do like

So the Python package, we can, uh, facilitate working with the AWS services in Python back end application. So there will be, like, two types of packages. Basically, we can go with the first one is, like, EC tool. So where, uh, and one more is like, uh, Amazon Lambda expression. So that can be provided to the, uh, handling the large amount of data. So yeah. Uh, that could be, like, you know, or the work can be handled by our multiple, uh, factors, and, uh, that can use you to the solid services, uh, of the applications. So, uh, while implementing your, uh, Python application, uh, you can, uh, take a, uh, EC two as, uh, your first application where, uh, that can provide you the, like, uh, in a maintainable strategy services where you can scale your applications and go behind that. And for the, like, Amazon, uh, so on, uh, you can work with, uh, different approaches, uh, that can scale out to your data and, uh, that can be stored into the cloud. Yes. That's it.

So, uh, while designing the restful APIs So, uh, while designing the display page in Python, we can consider, like, uh, in the state, uh, state, uh, labeling, that can be we can have, like, four types of strategies where we can, like, you know, uh, uh, get put post and delete, uh, strategies that can forward it to the to maintain the, uh, RESTful APIs. So while designing the particular, uh, RESTful APIs, so we can consider, like, if we want to create a, uh, new, uh, data, then or resources, then we can we can we can, uh, get the data from the web or, uh, whatever the provided data. So then we can, like, post, uh, post into that. If we want to, uh, do any changes into that, so we can cover multiple changes in that, whatever the requirement it is. Uh, and after that, like, you know so we can put that into the, like, upload, uh, the data, uh, in whatever the changes we have made. And after that, uh, to maintaining, uh, the, uh, like, that can be considered whatever we need to, like, particularly delete the data, so we can do that. So that is what the, like, uh, we can, uh, things to be considered while maintaining the statelessness, uh, restful APIs while designing. So apart from that, like, while you are designing the your, uh, restful APIs, you can take your, uh, project, whatever the provided the data, and you can click, uh, so you can make changes, uh, do whatever you needed. And after that, like, you can come with all these four types of, uh, resources, uh, and you can maintain your, uh, whatever the needed is important for that.

So for imposing data integrity and consistency, uh, across, uh, your distributed, uh, Amazon Web Services, uh, that can be accessed by the Python applications. First, if you get the Python applications, then you can, uh, provide the, like, AWS services. For example, if you want to change and you can put into the cloud, then you can go into the, like, you know, EC two where that can, uh, provide you the, uh, like, different consistency and you can scale it largely where the volume is going to, uh, considering, uh, large data. So while if you want to make changes or integrate something in that, then you can use a, uh, a Lambda or Python Lambda action, and that can provide you the distributed services where, uh, if you have some, uh, large volume and you can, like, you know, or you can consider, uh, a project, uh, that can, uh, gives a lambda expression will be, like, use the low latency for that, uh, integrating the, uh, applications. So, uh, this is what the needed, uh, to integrating and considering the distributed AWS services for the Python applications.

So while designing the scalable, uh, RESTful API with the Python, uh, we could integrate the AWS services storage. So first, basically, we can see how we can design the, uh, our RESTful APIs for that, uh, given, uh, resources or we can, like, consider the, uh, resources. We can get the data from whatever we have provided. We can, uh, uh, we can place that data into our, uh, or coding environment. And after that, we can integrate something. We can, like, uh, take that, uh, data from the, uh, like, air blast storage, and that can be, uh, probably, you know, they'll, like, uh, they'll be, like, uh, EC two or, uh, you can take Amazon, uh, CNN. So that CNN will basically take, and it can help to, like, scale and, uh, grow faster, uh, to the our project and further, uh, restful page, we can, like for, uh, resources, we can use, like, get post, port, uh, put and delete. So this can be, like, uh, resources where we can maintain our changes. And after that, uh, we can basically calculate, uh, this into integrating to the AWS, uh, basic storage. So that can basically induce us, uh, like, you know, uh, scalability or higher volume of the project. And that can, uh, go to the, like, large volume of data and considering to the, like, best integrating services according to AWS, uh, storage.

So, uh, like, compromising the risk principles, we can have, like, different strategies that can use to, uh, manage, uh, Python, uh, RESTful API services. So while, uh, if you have a strategy, uh, and if you want to manage a large volume of data, then you can use a post where you can update your data. Uh, so whatever we have to need to grow into the, like, scalable structure or, uh, grow large volume of data, then we can considering to update our, uh, database or our application, uh, programming interface that can considering, uh, to the different enforcement, uh, to our, uh, Python based applications where we can, uh, change everything, and we can, like, you know, change everything into the our particular, uh, uh, updation, uh, platform, and that can be forwarded to different services to the, uh, main applications. So that can go ahead and, uh, make some changes. And after that, like, it can update into our database that can be forwarded to the, a very large, uh, volumes of data. So that will be, like, uh, come up with a different, uh, principles, and that can, uh, face the principles like statefulness or stateliness. So that can use, you know, like, you have something, uh, to take down, uh, from that, uh, particular, uh, APIs and to process, uh, that data.

So, uh, in this function, if we have the user data, so that will be like, uh, getting the user data from the user ID. So where the user ID is defined by the sending user data. So why you can send the user data? So, uh, to what? Using the function of the user ID where you can get the actual data, uh, and that user data will, uh, return without anything. So where in the if statement where user data will return, uh, making the response that can be used, like, justify, uh, for the user data and 200, uh, like, uh, entities of, uh, for that user data. And, uh, uh, it can return the making response of error, user, or form. So where it will be, like, uh, making the response while, uh, it can take the user data from the, uh, send while sending the user data. So the make response, justify user data 200, and that will be the problematic here, uh, with the client regulation. So that that can be considering to the, uh, our main API principle, it will be incorrect because why you are first, like, getting the user data from the user ID. So if you are getting the user data from the user ID, then why are we sending that to the user ID? So that is one of the, like, making the response. It will be, uh, provide the incorrect in this solution.

What issue might have been calling process data? While calling the process data, also with the last. So first thing to do is, like, you are defining the process data from the, like, data, then you can get getting the result where we'll store in the list. So for item in data, so here it will be transformed with the complex transformation. So, uh, and again, you are appending from the, like, transformer data, uh, for your complex, uh, transformation to the provided items. Then you are, uh, returning the result where the result is not into the list. So so here it is c. If you are appending a transformer, uh, function from the, like, your, uh, whatever you have defined in the transform equal to, uh, complex transformation item. So the result might, uh, you have earlier, you have, like, uh, provided the result in the list where it can be a empty variable, you will get a result. But while returning a result, you're appending the transform function. So that can define, like, you know, complex star function, and that can cannot process the data with the, uh, x whatever you have provided here. But x is not, like, defined earlier in the, like, uh, function. So what will be the x? So there will be, like, if you consider the sum complex data, then it will be, like, you know, pass the function where it will be empty and the plus pass will, like, fill up the gaps where there will be, uh, no information provided in the, uh, in this function. So might there be, like, a appending, uh, transformer function. There will be a, uh, issue will arise because you have not forwarded the, uh, x, uh, or defined the x, uh, data. So that is what it is.

So for Python business system to dynamically balance the load of incoming API request between MySQL and post SQL. So databases, uh, that can provide the system for where the MySQL and, uh, post credential, these are the, like, two different databases where MySQL, like, you have structured data, uh, that can, uh, provide the, like, you know, dynamically balance, uh, your load of the incoming API request where API request will be, like, possibly come without the change without the modifying your request, uh, where in case if you have a, uh, structured data in MySQL, so that can be, like, you know, you can update your frequently generated data and the post, uh, postgreSQL, uh, database will where you have a data that can be, like, you know, provide the, like, different different sessions where you have to update your API. Uh, you need to update your frequently API, and that can request API request will be, like, generated, uh, to the, uh, session procedure where MySQL will be, like, targeted into the, uh, like, one at a time and where the post request will be targeted into, like, uh, limited time of session persistent, uh, in the, like, you know, uh, whatever the application, whatever we have dynamically balanced in Python Visible application.

So the AWS Lambda is basically will give you the, like, you know, Python display while you are implementing, uh, or integrating this AWS Lambda. So what it will do is it can be, like, anonymous anonymously, uh, it can generate, uh, the Lambda function where it can, like, you know, target the, uh, only one number of, uh, whatever where the restful API will be have, uh, different, uh, resources where, uh, that can, uh, gives you, you know, uh, like, multiple operations within the uh, short period of time. So that can give you, uh, the serverless operation while you are implementing the, uh, AWS Lambda. So for example, like, if you are implementing, uh, this Lambda function in your serverless operation, then the restful API will be, like, first check your, like, getting the, uh, resources, then it can, like, you know, uh, in case if they want to, like, modify the resources, then you can, like, uh, you can use a post and modify it. Then you can put where you can, like, uh, generate, uh, modified data. And if you want to delete after that, you can delete this, uh, Lambda function within this restful APIs where, uh, that AWS Lambda will be, like, uh, generated into, uh, the back end database. All the database, it will be changed where the, uh, host case will, uh, while implementing it. So the lambda function will be, like, taken to the, like, uh, all over back end database, and it can generally, uh, create a queries for, uh, for, like, integrating, uh, our risk pool API, and that can be crawled into the table so that go into column. So in the structured way, so that is what the, like, you know, uh, it can use a serverless operations without, like, uh, giving an issue, uh, for the work conditions.

So I don't know much of the Node JS services, but, like, while ensuring the data integrity across so AWS, uh, hosted into a post to SQL, uh, database, we can modify the, like, existing our Python, uh, services or Python codebase where it can integrate to the, like, you know, different services and that can forward the, uh, structured way, uh, to our, uh, database. And that can have, uh, good hosted on the server side where it can use the consistency and, like, uh, update our database using the post SQL. So and there's database will be like a structured way so where we can have our, uh, good, uh, AWS hosted, uh, in the cloud.