Vivek Sj

Hey. Hi. My name is Vivek. I am from my city called Harvard. Uh, it's been 5 years. I'm into software development. Uh, I started working I started my early days of career with, uh, some product based startups as I enter. I migrated myself into full time role. It's a service based industry where I worked for 2 years. I mean, tech stuff like, Uh, Python, data science, ML, front end development, and back end development. I worked with them for 2 years. And post that move to, Uh, TCS and the role of c two h. I worked again as a back end developer along with, uh, roles of data visualization and data scientist as well. So I worked with them for probably 2, 15 months, uh, then migrated myself to a start up. So where I've worked with, again, back end development and data science. Uh, along post that, I've been working with a lot of contractors on various tech sites like Python, Django, FastAPI, MongoDB, along with data science and data analysis. Uh, Yeah. For the period of 5 years, I've worked with for the period of 6 years, I've worked with, Uh, different text tags, different companies, different applications, uh, altogether put up I'm close to 6 years. Uh, my hobbies are playing cricket, watching cricket, movies. Yep. Uh, that's

So, uh, ensuring data consistent Postgres SQL or any other database, uh, when integrating various data source into an fight system. So for use transactions or transaction in databases ensures that a series of database operations, Either all succeed or done do. So this is crucial for maintaining your consistency, especially when, Uh, multiple related operations I need to perform. And the second thing, always make sure you apply constraints. Constraints like primary key, foreign key, Unique check unique check help maintain data integrity as well. This ensures that only, um, uh, valid data is entered into the database. Also, make sure of using Data type effectively choose appropriate data types for each column to ensure that only valid data types are stored in each field. Normalization normalizes a database designed to reduce Data redundancy and improve the data integrity. This involves organizing the tables, relations element duplicate data and ensure or integrity. Always have a regular audit and monitoring button. Check the database for inconsistency. Also, implement locking mechanism to table level locking or manage concurrent of data used to modes of stored procedure and trigger. Using modes of fluent can encapsulate complex systems only, ensuring data manipulations. Always have backup and recovery plans. Make sure you have version control and database of schema. Educate all kind of users and developers while doing this.

So how would you, uh, resolve issues with real time data processing? In Python? Okay. Okay. So if I can, uh, think of something so, uh, I would answer it something like this if I have to resolve issue with respect to real time data processing in Python? So, uh, I think it will announce a lot of challenges such as latency, data volume, accuracy, and reliability as well. So I would consider steps like, um, I would optimize the data injection, ensure that your data injection pipeline is efficient. Tools like Apache Kafka can handle high throughput data strip with low latency? So efficient data storage. Once you have the data injection, make sure you have efficient data storage. Usage of database, uh, optimized for, like, real time operations like RedX, Inflex DB, which can quickly read and write your operations? Parallel processing. Utilize Python. We know that Python has multi processing or multi threading capabilities to process the data in parallel. Regularity Celery can be useful for distributing tasks across multiple Also have steam, uh, processing framework. Consider using steam processing frameworks like Apache from or link, which are designed for real time data processing and can integrate with Python. Also, have data caching maintained. It can implement caching mechanism to store frequent access data in memory, reducing accessing time. Optimize the algorithms which you are using, Have a load balancing window and distribute the load evenly across the system to prevent any single point from being a bottleneck. Always have fault tolerance and redundants. Implement a system that can handle your fault, truancy, and, um, uh, failures gracefully? Always have a real time monitoring and alerts. Must scale your systems vertically as well or horizontally. I have a regular optimization update, Vincent. Compliance and security is one thing which I would take. That's different case

How would you go about integrating different data sources into unified system using Python and PostgreSQL or any other database? Okay. Okay. So, uh, if I can think about the answer with respect to integrating different data source into unified system using Python or PostScript or any other database? So, like, includes, uh, data extraction from various sources, uh, transformation of this data into a consistent format, and then loading into database? So if I look at a broader picture, so we have to make sure that, uh, the data sources like, we have tried it for all the data sources, either, say, API, CSV, or any other database. Understand the data format, the structure, the frequency of updates for each sources as well? Then choose a kind of a database system. Like, select a database like PostgreSQL, MySQL, call NoSQL option based on the data nature that is between structure and unstructured and the scalability needs? So design a unified data model, design a database schema that can accommodate data from all sources in a unified manner, considering normalizing and indexing and constraints for efficient data strength develop data extraction script ETLs like requests for API calls, pandas for data manipulation, uh, Pysoc 2 for post cross connection, extract data from each use? Transform the data like, cleanse the data, aggregate and transform the data into consistent format, handle the missing data, duplicate data, and convert data type as needed. Then also look at data loading. Load the transformed data into the unified database system. Uh, schedule regular updates or implement a real time data stream if necessary. Build a data pipeline, automate the entire retail process? Uh, orchestration tools like Airflow and, uh, Logoe can help us. Have a data integrity and consistency? Always optimize your performance for a faster data retrieval. Security and compliance is one thing. Testing and validation

How to create core packaging and release system in Python that integrates with the Node. Js application. How do you create a core which integrates the notice? So if I can, uh, think of a solution here You can think of a solution here. One solution which actually comes into so when you create a core packaging with respect to Python and try to integrate it with Node. Js, So there are several steps. The goal is to ensure that the Python package is easily available, uh, installable, and maintainable that integrates seamlessly with JSON. So structure your Python code, organize your Python code into modules and packages. A typical structure includes separate directories of your code, testers, and documentation. Use setup tools like, uh, in Python to create a setup dot py file. This file includes package name, version, dependencies. Then have a version control, systems like git to manage your codebase. After that go for Python package distribution. Build a package. Create a distributed version of your package that is ville, uh, then package repository, upload your package to a repository. For public packages, PYPI is the standard. For private packages, consider solutions like Artifactory our private Py repository. Integrate with Node. Js application Python or I mentioned that Python runtime is available in the environment where the Node. Js is running actually. Uh, then, uh, you can actually, uh, automate, like yeah. You can actually, uh, in your notice, you can manage a Python dependency. Put a simple script that's explained in Python, then use IPC, decide the IPC mechanism, inter process communication between the Python options like HTTP, socket IO, gRPC, or even state database, automate releases using CICD. Uh, note make sure you have a a trial process of Node. Js to invoke a Python script's environment variables to manage. I have a documentation examples. Wanted

Discuss a real world instance where you had to apply principles of Cloud Architect to improve your code performance in Python based application. Okay. So, uh, one of the thing So, uh, I, uh, like, I was designing a Python application designed for data analytics. So the application processes large datasets, which performs complex calculations and visualizes results. Initially, it is deployed on single server but, uh, faced performance issues due to increasing data volume and user base. So the challenges were scalability, difficulty in handling increased workload and concurrent users. There are also performance issues, slow processing of larger dataset, reliability single point of failure in current setup, cost balancing the cost was cost effective and other thing. So I made sure, uh, the Cloud Architect principles applied was scalability with cloud services, elasticity, utilize cloud services like AWS EC2, uh, which can scale up or down based on the demand. I also had a load balancing window, implemented load balance to distribute new requests across the multiple servers. Then I handled, um, uh, using AWS Lambda, I made sure we have a parallel processing been integrated, uh, with Apache Spark and, uh, AWS EMR, I'll try the distributed computing done. So we also made sure we have s three bucket for Blob Storage to store large and unstructured data in a cloud based storage solutions. We also implemented casing with, um, Redis and MemChat on cloud platforms to enhance a response time. Uh, we made sure of microservice architecture, uh, making it more manageable and scalable. We also had a CICD pipeline being implemented for automated testing. And finally, monitoring and optimization

Alright. Okay. So for to implement a okay. To implement a singleton design pattern, right, in the provided Python code, A few changes are necessary, basically. A singleton pattern ensures that a class has only 1 instance and provides a global point of access to it. This pattern is often used in a scenario where having more than 1 instance of a class would lead to problems, such as conflicting requests or inconsistent state across instances. In database connection, it's commonly used to ensure that there's only 1 database connection shared with different parts of the application, So it can improve performance and avoid issues like connection leaks. So here are the changes which we need to implement for the singleton pattern. Create a class variable, a class variable that will Hold a singleton instance. Modify the constructor. The constructor should not be used to create multiple instances, instead use a class method to control the in an instantiation, then have a private constructor make the constructor private to prevent the creation of multiple instances directly, Then have a class method for instance creation. Implement a class method that checks if an instance already exists. And if not, create one. So I would, uh, have a kind of a anything, which would have a new, uh, as a method, which is overridden to control the creation of New instance. Then, uh, instance class, uh, variable is used to keep the track of the instance. The init method is still there, uh, for any necessary initialization. But note that with this with the implementation I am obtaining, it will be called multiple times unless guarded. Why do we use single pattern for database connections? Resource Management ensures that only 1 connection or a pool of connections is managed and shared, Graduation Overhead or Overload. Consistency. It guarantees that every part of the application uses the same data base connection state. And finally, Performance, it avoids the cost of opening and closing connections frequently, which can be significant in some applications.

And I okay. When I'm recalculate, if n is We are doing a recursion here as you're calculating n minus So, uh, whatever the given Python function is, right, is a recursive implementation of the Fibonacci series. So the Fibonacci sequence is a series of numbers where each number is a sum of 2 preceding ones, Usually, starting with 0 and 1. In this implementation, however, uh, the sequence starts with 2 ones. Yeah. It is talking 2 ones. So if I'm not on the function, if n is less than or equal to 2, the function returns 1. So this serves as the base case for the recursion and sets the first two numbers of the sequence to 1. For n greater than 2, the function returns the sum of function itself called with n minus 1 and n minus 2, thereby summing up the 2 preceding values of numbers in a sequence. So the sum issues would be performance issues Uh, with respect to the time complexity, this function has exponential time complexity because it recalculates the same value multiple times. As n increases, the number of function calls grows exponentially, leading to significant performance issues. Uh, then we have no input validation. The function does not have an invalid input, for instance, if a negative number or a non integer is passed. The function will either enter an infinite recursive loop in case of negative integer or raise a type error in case of non integer. Uh, Overflow risk, due to recursive nature and lack of termination condition. Right? For the negative n, calling the function with a larger value f n would lead to Stack Overflow error. Then, uh, inefficient base case, this function would be slightly more Effective if it is, uh, tire if it is directly returned n for the base n is equal to 0 and n is equal to 1, aligning it with standard Fibonacci sequence, Uh, and, uh, producing the number of records you call for small values of n. Basically, to improve the function, right, if we would implement, mem, uh, memo memoization to store and reuse previously computed values or use an, uh, iterative approach to calculate the Fibonacci sequence, both of which would significantly improve its efficiency. So for handling inputs, adding input validation check would be more of beneficial.

What Python frameworks do you prefer for server side logic, and why how does that ensure the high responsiveness of a web application. Okay. If I can think of this, uh, with respect to Django here. So whenever I think about Python framework for server side logic, uh, the 2 widely preferred frameworks are Django and Flask. Choice between them depends upon the specific need and the scale of the project. So Django, uh, it's a fully featured framework. Django is a high level framework that allows, uh, batteries included philosophy. It includes an ORM, object relation mapper, an admin panel, forms, authentic support, and many more features out of the box as well. Uh, ORM layer, Django allows ORM, uh, Django's ORM allow developers to interact with the database using Python objects instead of writing raw or scale queries, speeding up the development and reducing errors. Security. Django has a built in protection against many common security threats like SQL injection, Cross script scripting, CRF attacks, enhancing the security of the web application. Scalability, while Django can handle high traffic power, Proper architecture and scaling strategies like database optimization, caching, and load balancing are essential. Community and ecosystem. Being Microsoft framework Django has a large community and extensive documentation, which is beneficial for troubleshooting and finding resources on plugins. It also ensures high response since in Django. Effective database queries, uh, you can use Django's ORM effectively optimizing database queries and indexing can significantly Improve your response time. You can implement caching strategies. Django supports our asynchronous use from 3.1. Middleware optimizations can be done with third party applications.