Snehal Lodhe

Hi. This is Nehal. I have several plus years of experience in Python full stack development. My tech stack includes Python, Node.js, SQL, and NoSQL. I'm aware of both. In AWS, I have worked on various services such as AWS Lambda, Kinesis, and SQS. I have also experienced in Docker, Kubernetes orchestration, and Amazon AWS services. Additionally, I'm familiar with the GCP. This is my tech stack. I have also worked in the financial, manufacturing, and aerospace industries as a full stack developer.

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

How would you resolve issue with real time data processing in Python, particularly in finance product? So for, so for the finance, we have, the data in the DB. So if, 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, to manage through the, finance date night processing.

What are the scalability challenges you could foresee? There are various challenges. One such challenge is the latency. Since it includes operations with the back end, core logic, and the database, we have to take care of whether services should not fail in the microservices architecture as well as in the monolithic architecture. Apart from that, we have to take care of the data coming via passing. And for scalability, we have several parameters. Let's say, rate limiting, requests, as well as other parameters such as alphas and betas 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 servers and the reliability of servers is still modified via architecture.

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

If you had to build a high-performance API in Python, the key considerations we'll keep in mind in the design environment are the latency, rate limiting, optimization, and how much paper it will take to 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, scenario, while creating a DB connection, it has the DB host, DB user, and DB password and execute query So I will make this execute query as abstract, method. And, 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, a single ten design pattern, 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, I prefer Django Flask and FastAPI. Those are frameworks, but I prefer Django for the large community support. And since it has the ability to scale the entire back end via less latency, making the entire system highly reliable. And it does, since the Django framework is a very lightweight framework, it's highly responsive for all operations. And, there is another framework called FastAPI. The FastAPI framework has highly responsive implementation, but the community support is not yet there. The entire Instagram backend 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.