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 ...
Power BI Gateways – Real-Time Insights & Interview Tips As a Power BI developer with 3 years of hands-on experience, I’ve encountered several scenarios requiring efficient use of Power BI Gateways. These tools are vital for enabling secure data transfer, especially when connecting on-premises data sources to Power BI for real-time or scheduled insights. What are Power BI Gateways? A Power BI Gateway acts as a bridge that facilitates secure data movement between on-premises sources (e.g., SQL Server, Oracle, Excel) and the Power BI service. Gateways ensure seamless connectivity and enable real-time or scheduled data refreshes, helping organizations make data-driven decisions. Real-World Use Case In one of my recent projects, I configured an On-Premises Data Gateway to provide real-time updates for a client’s sales dashboard. The dashboard sourced data from a SQL Server database hosted on the client’s internal network. This implementation enabled: Live Data Access: Sales teams cou...