Meesho PySpark Interview Questions for Data Engineers in 2025 Preparing for a PySpark interview? Let’s tackle some commonly asked questions, along with practical answers and insights to ace your next Data Engineering interview at Meesho or any top-tier tech company. 1. Explain how caching and persistence work in PySpark. When would you use cache() versus persist() and what are their performance implications? Answer : Caching : Stores data in memory (default) for faster retrieval. Use cache() when you need to reuse a DataFrame or RDD multiple times in a session without specifying storage levels. Example: python df.cache() df.count() # Triggers caching Persistence : Allows you to specify storage levels (e.g., memory, disk, or a combination). Use persist() when memory is limited, and you want a fallback to disk storage. Example: python from pyspark import StorageLevel df.persist(StorageLevel.MEMORY_AND_DISK) df.count() # Triggers persistence Performance Implications : cache() is ...
My Wells Fargo Data Analyst Interview Experience (1–3 Years) CTC: 16 LPA As a data enthusiast and SQL aficionado, I recently tackled some challenging SQL and Python questions in a Wells Fargo interview for a Data Analyst position. The experience was both rewarding and insightful. Here’s how I approached these questions. SQL Questions 1. Identify Inactive Accounts To identify accounts inactive for more than 12 months: sql SELECT AccountID, CustomerID, Balance FROM Accounts WHERE LastTransactionDate < DATEADD( YEAR , -1 , GETDATE()); This query filters accounts where the LastTransactionDate is older than one year. 2. Top 3 Accounts by Transaction Volume Per Month Using ROW_NUMBER() to rank accounts by total transaction volume for each month: sql WITH MonthlyVolume AS ( SELECT AccountID, SUM (Amount) AS TotalVolume, MONTH (TransactionDate) AS TransactionMonth, YEAR (TransactionDate) AS TransactionYear FROM Transactions GROUP...