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 ...
Recent Deloitte Interview Question for a Power BI/Tableau Developer Role How I Would Answer: Interview Question: How would you analyze data, gather requirements, and use different tools to deliver insights? In this blog, I’ll walk you through my thought process using a practical example—a scenario where a clothing company wants to analyze why their shirt sales dropped last month. 1️⃣ Understand the Business Goal The first step is to clearly define the objective. Example: The goal is to understand the reasons behind a decline in shirt sales and recommend strategies to improve sales performance. 2️⃣ Identify Key Metrics Defining the right metrics is crucial for actionable insights. Example: Total shirt sales Customer purchases by region and day Return rates or refunds Sales trends by product category 3️⃣ Collect Relevant Data Gathering comprehensive data ensures the analysis is thorough. Example: Use SQL to query the company’s sales database for shirt sales by region, date, and pro...