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 ...
How I Cracked the Data Analyst Role at Flipkart 🚀 The journey to securing a Data Analyst role at Flipkart was both challenging and rewarding. Here’s a detailed walkthrough of my experience, preparation strategy, and key takeaways. Application Process Applied Through: LinkedIn Total Number of Rounds: 5 HR Discussion: Focused on my past roles, experiences, and suitability for the position. 1st Technical Round: Covered foundational concepts in Excel, Power BI, and SQL. 2nd Technical Round: Delved into complex SQL queries and advanced Excel-based problem-solving. Managerial Round: Scenario-based questions to assess analytical thinking and problem-solving in real-world situations. Final HR Discussion: Discussed roles, responsibilities, and expectations from the role. My 3-Month Preparation Strategy 📆 Month 1: Advanced Excel, Power BI, and Data Visualization Source: Pavan Lalwani 🇮🇳 Excel for Data Analysis: Excel was the backbone of my initial preparation. I focused on the followi...