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 ...
PwC Data Analyst Interview question and its answer PwC Data Analyst Interview Experience (1–3 Years) Are you preparing for a data analyst role at PwC or a similar organization? Here’s my recent experience tackling some challenging SQL and Python interview questions during the selection process for a PwC Data Analyst role. These questions test both foundational knowledge and problem-solving skills. Here's how I approached them. SQL Questions 1. How Indexing Works in SQL Indexing improves query performance by allowing faster retrieval of rows. A clustered index organizes data physically, while a non-clustered index uses pointers to rows. Choose columns frequently used in WHERE or JOIN clauses for indexing, like CustomerID in a Transactions table. 2. Identify Customers with Purchases in Consecutive Months Using window functions: sql WITH ConsecutivePurchases AS ( SELECT CustomerID, MONTH (TransactionDate) AS TransactionMonth, YEAR (TransactionD...