Rajkumar Gupta

So we're starting my career. I have started working in IS. There, I have worked on lots of projects. I worked on the DNS log analysis where I needed to parse the log, process the log, and then load it into the SQL server. So for parsing and processing, I used Python with related libraries like Scrapy and BeautifulSoup to extract the data from the log file. Then, coming to the next malware classification problem where I needed to collect the malware file data code section using the Python p library and then regenerate the fingerprint using the Angluar method. And then, the data had value and then prepared the dataset in the thread method, dataset, and then loaded the data to machine learning algorithm, random forest, and trained the model, validated the model. Next, I worked on the Mercedes Benz, where I needed to load the data using Python. And using PySpark, I needed to train the model. Validate the model. So, I needed to do the transformation, apply the transformation, and then normalize the data using Python libraries. Then next, I worked at JPMorgan where I needed to develop an API to validate subnet data and also do the automation process of the red build. So I needed to use Python with the Flask API and use the REST API to generate the API. I also used microservices to deploy our product into the system.

For developing the high-performance API, we need to first check the optimization of the code, and then apply the RESTful API to connect the servers and generate sessions to further enhance security analysis. We also need to generate an abstract class so that we can use this abstract class for our development purposes. We will use design patterns, such as the singleton pattern, to develop our application using a structure that combines behavior and then implement the behavior of this design pattern for the API. Additionally, we need to deploy our service into microservices to connect to all APIs and run them with Swagger, which we can use to perform API performance checks.

So for doing the scalable application, we need to check the resources available for use in our application. And then we need to build a pipeline where we can push our code. And from there, the cloud will take our updated code to run the code and perform the application behavior according to the application we have written.

So for real-time data processing, first, we need to use the in-memory bill. There are lots of open source cloud architectures, like this Pachyderm architecture, that we can use for real-time data processing. And then for the finance product, we need to connect to our financial data center using the file's password to get the real-time data, and we can process, transform it, and then utilize it. And then we can view it also using the Spark act on the Spark architecture with our real-time updated data.

In AWS services, I have used the Databridge S3 to S3 with the Databridge so that we can process our data using different Python commands, and apply all the visualization and transformation methods on the data to enhance our performance on the cloud and further enhance our application. We need to use different cluster nodes so that our performance will be improved.

To analyze with business needs, we first need to gather the information. Then we need to apply logical behavior. Next, we store it into the physical database. After that, we apply logical analysis on the dataset and perform application operations so that we can get a good result of the analysis.

So this is basically the recursive method of calculating the sum of n numbers. But here we have seen that n is less than or equal to 2, that's one issue if the number is greater than 2. Then, get a equal to 2, then in that case, there will also be the sum will become the 3 bit. It should not return 1. It should return 3. So this one issue is there. But, yeah, this is one recursive method, we have implemented in the

So the basically singleton method we use so that we can use our object once at a time in the entire process. Like, we are connecting the data database once, and we can use this database anywhere inside our program to perform our connection and apply operations on the database.

So, basically, I use a Python-based application in my recent project where I need to validate the subnet. So I need to write the API, then deploy it into our microservice cloud platform. And then we connect different data sources using the API with the help of a RESTful API and a Flask API written in Python. And then when we validate the subnet, whether it is available on the data frame or not. If it is not available, then we return the response code not found. Otherwise, we return the response code 200 that subnet is already exist on the different data sources.

So, basically, using the Python and PostgreSQL or any other SQL, we can connect to the database we apply. We can use the Python library like pandas or NumPy or other data frame library in Python to perform our all these operations, like unified, group aggregation method, transformation method. And then we do our data processing and then push back to the database again. And using this transformation aggregation, we can do the analysis of the data and the database, like how much data we previously used, what we need to do for future data use, to use more applications.