Are these challenges good for PySpark interview preparation?

Yes. Many of the problems are designed around joins, transformations, ETL bugs, and performance tuning topics that appear in data engineering interviews.

Can beginners use the challenge arena?

Yes. The arena includes easy, medium, and hard problems across PySpark, SQL, and Python so you can grow from fundamentals to advanced problem solving.

Do I get instant feedback on my solution?

Yes. You can run code, review output, inspect tests, and retry quickly, which makes the page useful for deliberate practice.

Why does this help with data engineering jobs?

It builds hands-on skill with the same kinds of transformations, debugging tasks, and SQL reasoning used in ETL pipelines and analytics workflows.

Data Engineering Coding Challenges

Practice PySpark, SQL & Python with instant feedback, a live leaderboard, and a weekly boss battle.

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12 problems

StatusTitleAcceptanceDifficultyReward

1.Fix the Broken Pipeline PySpark DataFrame Operations68%medium+75 XP20 min

2.Top 3 Products per Category SQL Window Functions62%medium+100 XP25 min

3.Optimize the 100M-Row Join PySpark Performance12%hard+200 XP45 min

4.Word Frequency Counter Python Data Structures91%easy+50 XP10 min

5.Running Total Revenue SQL Window Functions63%medium+100 XP20 min

6.Parse Apache Logs with Regex PySpark String Processing55%medium+120 XP30 min

7.Find Duplicate Emails SQL Aggregation95%easy+50 XP10 min

8.Median Salary per Department SQL Window Functions20%hard+180 XP40 min

9.Pandas → PySpark Migration PySpark DataFrame Basics85%easy+60 XP15 min

10.Anomaly Detection (Z-Score) Python Algorithms11%hard+220 XP55 min

11.PySpark Structured Streaming PySpark Streaming15%hard+200 XP50 min

12.Second Highest Salary SQL Subqueries94%easy+50 XP10 min