Online PySpark Compiler
Write and run PySpark, Python and SQL in a browser notebook with instant output.
Data Engineering Learning Platform
Learn Data Engineering.
Build Production-Ready Skills.
Learn Python, SQL, PySpark, Apache Spark, ETL and cloud data technologies through structured tutorials, hands-on coding, real-world challenges and interview preparation.
Structured Learning Paths Hands-on Compiler Industry Challenges Interview Preparation Career Certifications
Turn learning into opportunity
Your data career, in motion.
Practice the skills employers look for, sharpen your interview story and prove what you know.
01
Live skill practice
ChallengeArena
Take on PySpark, SQL and Python challenges with instant feedback, XP and streaks.
Interview confidence
Interview Questions
Build concise, experience-backed answers for real data engineering interviews.
Show your progress
Certifications
Prepare with mock exams and earn a shareable PySpark.in completion credential.
Stay current
Blogs
Deep dives, patterns and post-mortems from engineers who ship data pipelines.
Everything in one platform
Learn, practice, prepare and get hired
Move from your first free tutorial to interview-ready confidence with practical tools built specifically for data engineering careers.
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Free Data Engineering Tutorials
Master Python, SQL, PySpark, Apache Spark, ETL, Kafka, Databricks and cloud data engineering through structured, beginner-friendly lessons.
PythonSQLPySparkETL
Build real confidence
Challenge Arena
Solve PySpark, SQL and Python problems with instant feedback, streaks, XP and leaderboards.
Open the compiler
Engineering Blogs
Read practical Spark guides, architecture deep dives, career advice and industry updates.
Interview Questions
Prepare with practical questions, detailed answers and company-style scenarios.
CV Builder
Create an ATS-ready resume and professional portfolio designed to help you stand out.
Learning paths
Pick a path and go from first line to production
Each path is an ordered sequence, not a pile of articles — so you always know what comes next and why it matters.
Python for Data Engineering
Core Python, data structures, file and API handling, and the scripting patterns pipelines are built from.
SQL for Data Engineers
Joins, CTEs, window functions, and the analytical query patterns that show up in every interview.
PySpark: Beginner to Advanced
DataFrames, transformations, joins, UDFs and performance work — from first job to production tuning.
Interactive compiler
Write, Run and Master PySpark in Your Browser
Practice PySpark without installing anything. Run code, explore datasets and understand transformations through instant output.
No local Spark, Java or Python setup Sample datasets loaded and ready to query Syntax highlighting and instant console output Pick up the same code inside any tutorial
Datasetsorders.csvcustomers.parquetevents.json
1from pyspark.sql import SparkSession2from pyspark.sql import functions as F34spark = SparkSession.builder \5 .appName("retail-revenue") \6 .getOrCreate()78orders = spark.read.csv("orders.csv", header=True, inferSchema=True)910revenue = (orders11 .filter(F.col("status") == "SHIPPED")12 .groupBy("region")13 .agg(F.round(F.sum("amount"), 2).alias("revenue"))14 .orderBy(F.desc("revenue")))1516revenue.show()Press Run to execute this job and see the output.
Challenge Arena
Practice the way interviews actually test you
Timed problems across Python, SQL, PySpark and pipeline design — with XP, streaks and a leaderboard to keep the habit going.
Daily Challenge
A new problem every day
One focused question, picked to build a streak rather than eat an evening. Solve it in the browser and bank the XP.
Track your streak and XP
Sign in to save solutions, keep a streak and appear on the leaderboard.
Enter the arenaWhat's inside
Weekly contest with a ranked board Global and topic leaderboards Skill badges as topics are cleared Streak tracking across days
Roadmap
The Data Engineering sequence, in order
Nine stages from first Python script to interview-ready. Select any stage to see what it covers and what it assumes you already know.
Stage 1 of 9
Skills covered
SyntaxData structuresFunctionsFile I/O
- Estimated time
2–4 weeks - Prerequisites
None - Status
- Sign in to track
Interview preparation
Prepare for Your Next Data Engineering Interview
Question banks by topic, company-wise preparation and mock interviews — with worked explanations, not one-line answers.
Certifications
Prove the skill, not just the hours
Assessments built around what the role actually requires — pass one and you get a shareable certificate for your profile.
Databricks Certified Associate Developer for Apache Spark
Topics covered
Spark ArchitectureDataFrame APISpark SQLStreaming+2
Free demo examIntermediate
View Certification Databricks Certified Data Engineer Associate
Topics covered
Delta Lake & Unity CatalogData IngestionETL with PySpark & SQLLakeflow Orchestration+3
Free demo examAssociate
View Certification Databricks Certified Data Engineer Professional
Topics covered
Lakeflow Declarative PipelinesStructured Streaming & AUTO CDCDelta Lake & Liquid ClusteringDelta Sharing+4
Free demo examProfessional
View Certification Certified AI/ML Engineer
Topics covered
Machine LearningDeep LearningNLPGenerative AI+2
Free demo examAdvanced
View Certification - 1Learn
- 2Practise
- 3Complete Assessment
- 4Earn Certificate
- 5Add to LinkedIn
CV Builder
Build a Job-Ready Data Engineering CV
Create an ATS-friendly CV tailored for Data Engineer, PySpark Developer, ETL Developer, Cloud Data Engineer and Analytics Engineer roles.
ATS-friendly templates Live editing Skills suggestions Project bullet generation Job-description matching PDF download LinkedIn profile suggestions
Blogs & resources
Written by people who ship pipelines
Deep dives, patterns and post-mortems across the data stack.
PySpark
DataFrames, Spark SQL, Delta Lake and real-world pipeline write-ups.
Read articlesData Engineering
Pipeline design, orchestration and the modern data stack in practice.
Read articlesSQL
Window functions, CTEs, indexing and query performance.
Read articlesMachine Learning
Feature engineering, MLOps and model evaluation for data teams.
Read articlesGenerative AI
LLMs, RAG pipelines and building AI-backed data products.
Read articlesAI Agents
Autonomous agents, tool use and multi-agent workflows.
Read articlesStart learning Data Engineering — free Start Learning