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.

PySpark.inData Engineering

Data Engineering Coding Challenges

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Data engineering interview practice

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hands-on problems on joins, window functions, deduplication and more. Your code runs in the browser against sample tables, and every answer is checked automatically.

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Every one of the 224 practice problems on PySpark.in, grouped by technology and topic. Use the filters and search above to narrow things down, or pick a problem directly from here.

PySpark 62 challenges

Adaptive Query Execution

Optimize Join Operation Using Broadcast in AQEhard

Aggregation

Broadcast Join Optimisation

Optimize Small DataFrame Join with Broadcasteasy

Data Quality Validation

RBS: Build a Data Quality Gate for the Daily Customer Extracthard

DataFrame Basics

DataFrame Deduplication

Deduplicate and Aggregate User Actions with Latest Sessionhard

DataFrame Operations

GroupBy Aggregations

Incremental Load with CDC

Incremental Load with CDC and Latest Update Filteringmedium

Incremental Processing

Incremental Toll Processing with Late-Arriving Events (PySpark)hard

Joins

Inner Join Orders with Customerseasy

Null Handling and Data Cleaning

Clean Order Prices by Handling Nulls and Filtering Negativesmedium

Performance

Optimize the 100M-Row Joinhard

Performance Optimization

Handle Data Skew from High-Frequency Vehicles (PySpark)hard

Pivot and Unpivot DataFrames

Pivot Sales Data to Show Monthly Totals by Producteasy

PySpark SQL Optimisation

Optimize Average Rating Calculation for Productsmedium

Rules Engine

Dynamic Pricing Rules: Point-in-Time Resolution (PySpark)hard

Schema Evolution

Evolve Customer Schema with Incremental Updatesmedium

String Processing

Structured Streaming

Handling Duplicate, Late, and Out-of-Order Eventshard

Window Functions

SQL 142 challenges

Aggregation

CASE WHEN Pivoting

CTEs

Total Sales per Region using CTEseasy

Date Functions

DAU / WAU / MAU Metrics

Deduplication with ROW_NUMBER

Filtering

Funnel Analysis

E-commerce Funnel Conversion Rateshard

Gap and Island Problems

Consecutive Low-Sales Islands per Producthard

Joins

SCD Type 2

Current and Previous Address Tracking with SCD Type 2medium

Session Analysis

Longest Session Duration per Usereasy

String Processing

Count Individual Tagsmedium

Subqueries

Second Highest Salaryeasy

Subquery

Find Employees Who Earn More Than Their Department Averagemedium

Subquery vs CTE Patterns

Window Functions

Python 20 challenges

Basics

Data Structures

Data Validation Pipeline

Dictionary and Counter

Filtering

Filter Numeric Values Stored as Stringseasy

Heap / Priority Queue

JSON Manipulation

Flatten Nested JSON Structureeasy

JSON Processing

Complex JSON Transformation for Nested Metricshard

LRU Cache

Simple LRU Cache Simulationeasy

Pandas GroupBy Aggregation

Sum of Values by Keyeasy

Regular Expressions

Extract Error Codes from Log Stringsmedium

Sliding Window

Sorting with Custom Comparators

Custom Sort by Absolute Value and Signeasy

Strings

Reverse Words in a Sentenceeasy

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PySpark 62 challenges

Adaptive Query Execution

Aggregation

Broadcast Join Optimisation

Data Quality Validation

DataFrame Basics

DataFrame Deduplication

DataFrame Operations

Deduplication with ROW_NUMBER

Delta Lake MERGE / SCD Type 1

Delta Lake MERGE / SCD Type 2

Explode Nested JSON Arrays

GroupBy Aggregations

Incremental Load with CDC

Incremental Processing

Joins

Null Handling and Data Cleaning

Performance

Performance Optimization

Pivot and Unpivot DataFrames

PySpark SQL Optimisation

Rules Engine

Schema Evolution

String Processing

Structured Streaming

Window Functions

SQL 142 challenges

Aggregation

CASE WHEN Pivoting

CTEs

Date Functions

DAU / WAU / MAU Metrics

Deduplication with ROW_NUMBER

Filtering

Funnel Analysis

Gap and Island Problems

Joins

SCD Type 2

Session Analysis

String Processing

Subqueries

Subquery

Subquery vs CTE Patterns

Window Functions

Python 20 challenges

Basics

Data Structures

Data Validation Pipeline

Dictionary and Counter

Filtering

Heap / Priority Queue

JSON Manipulation

JSON Processing

LRU Cache

Pandas GroupBy Aggregation

Regular Expressions

Sliding Window

Sorting with Custom Comparators

Strings