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Crack the data engineering interview on real Spark problems.

SQL, PySpark and Databricks questions asked at Amazon, Adobe and Deloitte, graded on hidden datasets with runtime and shuffle metrics.

No card. 20 graded submissions every month on Free.

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Deduplicate customers (PySpark)Accepted 7/7
solution.py · Apache Spark 3.5Submitted
from pyspark.sql import DataFrame, functions as Ffrom pyspark.sql.window import Window def solve(customers: DataFrame) -> DataFrame:    w = (Window.partitionBy("customer_id")         .orderBy(F.desc("updated_at"), F.desc("source_id")))    return (customers            .withColumn("rn", F.row_number().over(w))            .filter(F.col("rn") == 1)            .drop("rn"))
Runtime1.42 s
Memory612 MB
Shuffles2
Rows1.28M
Built by Sagar PrajapatiDatabricks practitioner, founder of GeekCoders

Sound familiar?

Most practice sites train you for a round data teams stopped running. Here is what changes on CodeArena.

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You solve it at your desk, then freeze when they ask why you joined that way.

Every accepted answer shows how it ran, and Pro adds optimisation feedback on what it would do at scale: the “why” interviewers ask about.

Optimisation feedback →

Practice sites give you three rows of data. Interviews are about skew, nulls and 50 million rows.

Hidden test datasets go up to 50M rows, with skewed keys and nulls. A naive answer times out instead of quietly passing.

Hidden datasets →

“Accepted” doesn’t tell you whether your answer shuffled twice and collected to the driver.

The verdict reports runtime, peak memory, shuffle stages and partitions on every submission.

Run metrics →

The posting asks for Databricks and window functions. You’re grinding tree traversal.

GeekMatch AI reads a real posting against your resume, names the gaps, and points you at the questions that close them.

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What makes this different

The same tools and data shapes your next team uses, not toy inputs.

customershidden dataset
customer_idbigint
emailstring
source_idint
updated_attimestamp
50,000,000 rowsskewed key

Real schemas, big data

Hidden datasets up to 50M rows, so a naive answer times out instead of passing.

Browse problems →
Accepted7 / 7 tests · beats 87%
Runtime1.42 s
Peak memory612 MB
Shuffle stages2
Partitions8

Graded on how it ran

Runtime, memory, shuffle stages and partitions on every submission.

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Sagar Prajapati
Who built this

Sagar Prajapati

6,433+GeekCoders learners
50k+ LinkedIn followers

CodeArena turns the GeekCoders course questions into problems you can run and get graded on.

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Questions before you start

Do I need to be a data engineer already?

No. Questions are tagged by experience band. The lowest starts at joins, grouping and reading a file in Python; the highest is distributed systems, cost and governance. You pick the band, not us.

How is this different from LeetCode?

It grades SQL, Python, PySpark, Spark, Databricks and data modelling against hidden datasets in a real sandbox, and reports runtime, memory and shuffle stages alongside the verdict.

What do I get without paying?

Every published question with its schema, worked example and public tests, the editor, and a verdict. Free includes 20 graded submissions a month against the hidden datasets. Nothing is deleted when you run out — grading pauses until the month turns over.

Is the certification a vendor certification?

No. It is our exam and our credential, issued by GeekCoders and verifiable on a public page. It is not affiliated with or endorsed by Databricks, Snowflake, Microsoft or the Apache Software Foundation.

Can I just paste an answer from a model?

You can, and it will pass. It will also report the same runtime and shuffle count to you that it would to an interviewer, and the round you are preparing for ends with somebody asking you to explain it.

Your next interview is a data engineering interview.

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