Vivek Gupta

so starting with my background, I completed my bachelor's in computer engineering in 2008. After that, I immediately started working in the IT industry. Until 2016, I worked as a back-end engineer and on things related to the data, such as the database and all other stuff. In those eight years, I spent a lot of time on back-end services, including designing high-scalable back-end APIs for clients and designing a scalable system so that we could develop the product according to quality. At that time, I worked on some frameworks, such as JavaScript for both front-end and back-end, complete UI, and full-stack development. I also used Python, SQL, and some other technologies related to data, as well as some basic machine learning algorithms. On the cloud side, I started with Azure at that time, but only in a limited capacity. From 2016 to 2018, I went for my master's and then completed it in two years in AI and machine learning. During my master's, I also published a research paper in one of the reputed journals, Taylor and Francis. After completing my master's, I again started my career in data engineering and data science. In 2008, I started my cloud data engineer work, where I first started working in 2015-16 and already took exposure to the ETL process and complete data pipeline. I started cloud data engineering in 2018 and worked on both Azure and AWS cloud. I gained experience with some orchestration tools, such as Azure Data Factory, as well as Snap Analytics and Databricks on the Azure side, using Spark features to optimize transactions, working on high-scalable loads, and working on complete end-to-end pipelines. I handled a lot of projects from 2018 to 2022 while working for a single company. In 2022, I took another switch to another company and have been working with a different company in a different domain, including e-commerce and healthcare, as well as pharmaceutical domains. Right now, in my current project, I'm working on a server utilization dashboard, where we're completely designing the system from getting the data from the API, pulling it into blob storage, making transformations, and showing it through Power BI. Right now, I'm using Spark, Databricks, Python, and SQL as part of my tech stack.

Some of the advanced SQL techniques that are beneficial for optimizing Python scripts are broadcast joins as well as some Spark optimization techniques I use. Okay. And, using common table expressions, joins effectively and windows functions, and other functions are quite useful for optimizing the ETL scripts of the complete pipeline. Basically, using SQL techniques we can optimize a lot of stuff, like indexing, which is another optimization technique we use in our ETL process. So, let's talk about other things we're using or can follow for optimizing Python ETL scripts. We can use some Spark configurations. We can optimize partitions, perform repartitioning, and all the stuff. Indexing, I already took an example of. You can say that optimize joins, avoid selecting distinct things, and use where clauses to filter out data, and highly avoid nested queries. You can say that use base CTES and the most important thing is to use optimized joins, not using joins directly; the optimization should be there.

Can you propose a method for doing incremental data load in Python? Okay, so if we talk about this case, the method is to first of all, define incremental load as an example of strategies that can be used to optimize the Python pipeline, like, to minimize resource usage. So first of all, you can say that drag the last loaded time stamp or the ID. This means that when it's loaded, we can use some of the incremental data to query the data source for records that have been added or updated since the last load using the last loaded time stamp. Then, we can use the load increment data to load the data into the pipeline, update the latest loaded time stamp, and schedule the incremental loads. We can handle data deletions if the data source has data deletion, and then optimize the queries to retrieve the incremental data with the minimum resource size. Like, we can index the columns using the filter criteria to select only the necessary columns, and use efficient joins. And then, we can monitor and tune the performances to measure the performance of the incremental load.

Strategy to handle load into SQL database. Okay, so in that case, the thing is, can you mention a strategy to handle exceptions in Python? Okay. See, the point is, yes, we can handle exceptions while managing the data and all the stuff. So the thing is, like, we can use a try-except block, which is a simple thing. We can wrap the code responsible for loading data into the database, and then this allows us to catch and handle any exceptions that occur during the data loading process. We can use the try part first. In the try block, we can write the code to connect to the database, and then using the cursor we can execute the queries. We can then handle the exceptions in the except block by printing out the errors that occur, which allows us to handle the loading of data into the database.

Paginate API request in a Python script to ETL. Okay, a way to efficiently paginate. In that case, what I'm thinking is, see, the thing is in the script we can see a strategy like first of all we have to understand pagination parameters like it supports query parameters like page or per page or offset. So first of all we have to understand how the API handles pagination and this will help us understand the request's structure. Okay, then we can set up the pagination loop like we can create a loop that iterates over the pagination results until all the data is fetched. This loop should increment the pagination parameters with each iteration to fetch the next page of data. Then we can implement rate limiting if the API has rate limits and implement it accordingly. We can write the script to avoid these limits. Then we can handle errors and exceptions to handle any error or exception that occurs during the pagination request. And after that, we can optimize the request frequency. We have to experiment with the optimal frequency of the request to balance between the minimization of the time to fetch all the results and avoiding overbilling of the API server. So that is the process we can follow to efficiently paginate API requests in a Python script.

The approach that we can use here is a highly distributed scalable system. The approach that we can use here is a highly distributed scalable system, which can leverage the feature of distributed processing, like Apache Spark, obviously, as I am also using that stuff in my current project. And then we can modularize the ETL components. Like, we can break down the ETL process into smaller tasks or you can say that components that can be independently scaled. Like, we can scale them independently. For example, we can separate the extraction, transformation, and loading steps into individual modules or services so that we can scale each component independently based on demand. We can run them independently and scale them independently. Then, obviously, we can use cloud services. We have a lot of cloud services, so we can use them for data storage and processing. We can use S3, cloud storage, data lake storage, and store and process huge amounts of data. These services offer scalability, reliability, and managed infrastructure. So, we can use them also. And then we can use some optimized data storage, like, based on the characteristics of the data. For example, if we have columnar data format, so we can use Redshift or Presto. And then we can use growing data volumes over green data volume. We can process through the incremental processing of the load. Okay. Like, we have to design a deal process to support incremental processing. You can say that only new or changed data is processed for each ETL, and then monitoring follows. We can monitor and fine-tune the performance, handle fault tolerance, and leverage caching and memorization. We can use caching and data governance.

A simple Python code block designed to send a batch of messages to these appear to be oversight. Explain the potent, could lead to error on an exception behavior. So explain the potential issue. Explain the okay. So, basically, it is using your boto3 library. And through the Lambda, we are getting, creating list of dictionary. So in that case so what I'm thinking? Okay. So in this code block, The problem is, So here the problem is So here's, like, the issue is, I think there is a issue with the invoke method of the lambda client. Like, this is this is, like, an evident or in the string pass to the function name parameter. That is, your process message function. Like, the, closing code. Explain the potential issue. It will be an oversight that could lead to the exception behavior. So it's a improper way to you can say that's string interpolation in the function name parameter, of invoke method. Especially, there is a mismatch, character code function into the string name string.

K. Look at this. K. So in this sequel query, what I'm feeling is sales lag revenue. Order by month. Okay. See, first of all, the window function written here is not valid because depending on the SQL, you can say the dialect being used. The lag function may not be supported or may require specific syntax here. K. So, first of all, we have to verify that the SQL dialect supports the lag, and then it should require the lag function to have an ordering to determine the previous month of revenue. So if the month column in the sales data table is not properly ordered, then there's a tie between the values, the result of the lag function won't be efficient. So, and the third thing, we have to check the data quality over there. Like, if we have missing null values in the revenue or in the month column or any inconsistency, like duplicate months or something like that. So this could lead to unexpected results. So, first of all, how can we debug that? We have to check the direct compatibility. Okay, like, how can we use the lag function over and then we have to inspect the data in the sales data table to ensure that the revenue and month column contain valid data, normal values, and properly formatted. And then we have to test the query or execute the query in the SQL environment. And for the best thing, we can use if the lag is not supported, we can use the base CT. We can use the CTs to execution.

To debug a Python application experiencing performance issues during complex SQL data transformations, we can see that there are several efficient ways to do so. One of the most efficient ways is to use profiling tools like cProfile or line profiler to analyze the execution time of different parts of the Python application and identify which function or section of the code is contributing to the performance issue. To identify the performance bottlenecks, we first need to pinpoint the specific area of the code or SQL query where the performance degradation is happening. We can use profiling to identify the functions and SQL statements that are causing the issue. Once we have identified the performance bottlenecks, we can optimize the Python code to improve its efficiency. We can also analyze the SQL queries and optimize them using database-specific features like explain and analyze to understand how the database engine is executing the SQL queries. Additionally, we can cache the results of computationally expensive data transformations that don't change frequently to improve performance. Testing incremental testing and benchmarking are also useful for identifying performance issues and optimizing the application.

Using React and Steamlify in Python for a front end, to have questions in answering this won't affect your overall screening score. So what I'm thinking in this approach is to optimize web application load times. First of all, we can minimize the initial code size. We can reduce the size of the JavaScript code or CSS files by finding and compressing them. We can use tools like Webpack and Parcel to handle that. Another approach that I'm also using in my project, like my team, is lazy loading of components and resources. We can load them not immediately, but as per the requirement. We can also optimize the images by compressing and optimizing them without sacrificing the quality. Additionally, we can split the code into modules or chunks and load them dynamically based on user interaction. We can minimize external dependencies. We can use server-side rendering, optimize data loading, and performance monitoring.

How do you manage state effectively? Okay, so in that case, what I'm thinking is, first of all, I'm not sure about that. We can define clear data contracts, okay. We can centralize state management so that we can use it properly and use some of the REST API services. We can implement synchronous data fetching and all. We can optimize data transforms as well, and use real-time communication.