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Meesho PySpark Interview Questions for Data Engineers in 2025

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 ...
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How I Cracked the Data Analyst Role at Flipkart

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

  1. HR Discussion: Focused on my past roles, experiences, and suitability for the position.
  2. 1st Technical Round: Covered foundational concepts in Excel, Power BI, and SQL.
  3. 2nd Technical Round: Delved into complex SQL queries and advanced Excel-based problem-solving.
  4. Managerial Round: Scenario-based questions to assess analytical thinking and problem-solving in real-world situations.
  5. 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 following areas:

  • Data Cleaning: Managing duplicates, handling null values, and applying conditional formatting.
  • Advanced Formulas: Mastered VLOOKUP, INDEX-MATCH, SUMIF, and array formulas.
  • Pivot Tables: Used for summarizing and analyzing data efficiently.

Power BI for Data Visualization:
I transitioned to Power BI to learn dynamic reporting and dashboard creation.

  • Data Import and Transformation: Leveraged Power Query for cleaning and transforming data.
  • DAX (Data Analysis Expressions): Built calculated measures and time-based functions.
  • Interactive Dashboards: Designed user-friendly dashboards with slicers, filters, and KPIs.
  • Data Modeling: Developed an understanding of relationships between tables and applied it to real-world datasets.

⌛ Month 2: SQL for Data Extraction and Analysis

Source: Nitish Singh

SQL became my focus for handling large datasets and complex queries. Key areas of learning included:

  • Basic SQL Queries: SELECT, WHERE, JOIN, GROUP BY, and ORDER BY.
  • Advanced Queries: Subqueries, UNION, nested queries, and window functions.
  • Data Aggregation: Functions like COUNT, SUM, AVG, and DISTINCT.
  • Joins: Mastered INNER, LEFT, RIGHT, and FULL OUTER JOIN.
  • Query Optimization: Practiced writing efficient queries to manage large-scale datasets effectively.

Daily Practice on HackerRank:
HackerRank’s SQL challenges helped sharpen my speed and logic for tackling diverse query problems.

✅ Month 3: Kaggle Projects and Real-World Problem Solving

Exploratory Data Analysis (EDA):
Worked on analyzing datasets to identify patterns, relationships, and outliers.

Projects:

  • Sales Analysis: Investigated factors driving sales growth.
  • Customer Behavior: Analyzed buying patterns to understand user segments.
  • Market Research: Identified trends and anomalies in product performance.

Kaggle’s community projects provided exposure to varied datasets and analytical approaches, enhancing my problem-solving skills.

Key Lessons and Takeaways

  • Consistency Matters: Dedicated daily practice with SQL, Excel, and Power BI made all the difference.
  • Project Experience Is Crucial: Real-world datasets from Kaggle prepared me for scenario-based interview questions.
  • Scenario-Based Thinking: Managerial rounds tested my ability to think analytically under hypothetical situations.

Message to Aspiring Data Analysts:


Breaking into a role at Flipkart or any top organization demands persistence and structured preparation. Focus on mastering the tools, practicing real-world problems, and continuously improving your understanding of data.

Follow me for more insights and interview experiences!

#Flipkart #DataAnalyst #SQL #PowerBI #Excel #InterviewExperience #DataScience #CareerTips

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