Snehal Lodhe

Could you help me to understand, uh, more about your background by giving Okay. Hi. This is Nehal. Uh, I have several plus years of experience into, uh, Python full stack development. Uh, my tech stack is Python, Node. Js, uh, in SQL and NoSQL. I'm aware of both. In AWS, I have worked on various services as AWS Lambda, Kinesis, SQS. Uh, and I have also experienced in Docker, uh, Kubernetes orchestration, Amazon AWS services as well as I'm familiar with the GCP. And, uh, this is my, um, uh, tech stack. I have also worked in financial manufacturing and aerospace industry as in to a full stack

How can solid principles be effectively implemented in Python? Yes. You can, uh, implement the solid principles principles in Python by writing the around the, um, object classes. And while loading the object, you have to, res keep, um, well of the garbage collectors and as well as the object instances should be mapped and referenced. By this way, you can, um, um, effectively implement a Python

How would you resolve issue with real time data processing in Python, particularly in finance product? So for uh, so for the finance, we have, uh, the data in the DB. So if, uh, the data is SQL data, then we can have the indexes for mapping. And as well as there are other passing formats such as JSON and XML format to pass the data. As well as if we are in no SQLs, we can write the wrappers around the 12 datas and implement it as indexes to find. And, also, we can create a single handedly helper classes, um, to manage through the, uh, finance date night processing.

What are the scalability of challenges you could foresee? How would you work around it? There are various challenges. One such, um, challenge is the latency. Since, uh, it includes the operations with the back end, um, a log core logic and the database, we have to also take care of whether the services should not fail in the microservices architecture, uh, as well as in the monolithic architecture. Apart from that, we have to take care of the data coming via passing. And, And for scalability, we have, uh, there are other parameters. Let's say, rate limiting, um, request as well as there are, um, the other parameters such as, um, the alphas and betas, uh, for the rate limiters. And for the scalability challenges, we have the option of having instances map across various regions, but still, we have the availability of the and, uh, servers and the reliability of the servers is still, um, can be modified via architecture. So these are the challenges while scalability of restful APIs.

Do you ensure data consistency in Postgres, SQL, or any other databases when integrating various data sources into unified system? Okay. So for, uh, ensuring the data consistency, we have all the integrations with Okay. Um, so ensuring data consistency, PostgreSQL, or any other databases when integration various data sources into unified system. Uh, suppose, Grace or any other, uh, databases, the integrating various data sources are very important. For that, the third party libraries and wrappers are there. So the third party libraries expose the API endpoint. And via API endpoint, we can integrate it that the third party data sources and we can integrate via having the, uh, having the writing wrap or surround them or importing tables or just in integrating the third party libraries, uh, queries.

If you had to build a high performance API in Python, what are the key consideration we'll keep in mind in the design environment? If I have to build the high performing API in Python, the key points will be the latency, rate limiting, optimization, and how much paper it will take to, uh, execute an API. Those are the important points to take care while building the high performance API in Python.

Assuming we are trying to implement the singleton pattern, what changes you will recommend and why you should use the design pattern? Okay. So in this entire, uh, scenario, while creating a DB connection, um, it has the DB host, DB user, and DB password and execute query So I will make this execute query as abstract, uh, method. And, uh, since having it As an abstract method, just creating the instance of class here and extending method into the particular instances to override it completely. By this way, we can implement, uh, a single ten design pattern, and And by creating abstract methods and extending it with the client class is the solution for this design pattern.

Give now following Python function. Explain what does it point out any issues you see within it? Okay. So it has no breaking point as I can see, and it has no repeater. So function needs some iterator to fall back into. So in this entire function, no iterator is there as to iterate over the end or going through the values. And since it's a recursion function, there is no endpoint or starting point to this recursion

What Python web frameworks do you prefer for a server side logic and why? How does that ensure high responsiveness of web application? So Python framework, I prefer for the Django Flask and FastAPI. Those are frameworks, but I prefer Django for the large community support. Um, and since it has the ability, uh, to scale the entire back end, um, via less latency, and make make the entire system as highly reliable. And it does, uh, I yeah. And since the Django Since the Django framework is a very lightweight framework, it does, um, it's highly responsive for the rest fully um, operations. Um, yes. And, uh, there is other framework called as fast API frameworks. The fast frameworks, the community support is not yet there, but it's highly responsive framework to implement. The entire Instagram back end has been relied on the

Some basic practices of building and managing server side knowledge in Node. Js and web application. So the best practices come up with a design pattern, then create a blueprint of the class, create object methods, and follow the design pattern. By this way, you can have a server side logic in Node. Js and web applications.