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 ...
Cracking the UST Data Analyst Interview: First-Round Questions If you’re gearing up for a data analyst interview at UST, you’ll need more than just technical know-how; clarity in explaining concepts is equally critical. Here’s how I tackled the first-round questions and prepared to ace the challenge! 📋 Top Questions and My Approach 1️⃣ Self-Introduction Your introduction sets the tone for the interview. Here’s my strategy: Start with a brief overview of your educational background. Highlight your relevant experience, focusing on roles where you leveraged data analysis, SQL, or Power BI. End with a mention of key projects, tools you’re proficient in, and what excites you about the role. Example: "Hi, I’m [Your Name], a data analyst with 3+ years of experience in leveraging SQL, Power BI, and Python to drive data-driven decisions. In my recent role, I designed dashboards to monitor KPIs, optimized queries for better performance, and collaborated with cross-functional teams to deliv...