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Spark vs PySpark

Spark vs PySpark: The Difference, the Performance Truth, and Which to Learn for Data Engineering

Apache Spark is the engine. PySpark is its Python API. DataFrame code written in PySpark runs on the same JVM engine as Scala code, so the speed gap people talk about comes from Python UDFs, not from PySpark itself. Here is the difference, the performance detail interviewers ask about, and which one to learn for data engineering in India.

By Durgesh Yadav — Senior Data Engineer @ 7-Eleven · Updated 15 Oct 2026. Preparation guidance, not a hiring guarantee.

Spark or PySpark: what is the difference, and which should a data engineer learn?

Apache Spark is the distributed processing engine; PySpark is its Python API, one of several next to Scala, Java, SQL and R. DataFrame and SQL code runs on the same JVM engine whichever language you write it in, so speed is the same unless you add Python UDFs. For data engineering in India, learn PySpark with Spark SQL, and read Scala only when a posting asks for it.

Spark Interview Questions See the Data Engineering cohort

Guide

What To Learn And How To Practice

What is the difference between Spark and PySpark?

Apache Spark is the engine, and PySpark is one way to program it. Spark is a unified analytics engine for large-scale data processing, with APIs in Java, Scala, Python and R, running on the Java Virtual Machine. PySpark is the Python API: the pyspark package from pip, which starts Spark's JVM and passes your DataFrame and SQL calls to it through Py4J. So this is not one tool against another. Knowing Spark means knowing the engine: partitions, shuffles, stages and the optimizer. Knowing PySpark means writing its jobs in Python. A PySpark interview asks about both.

Apache Spark against PySpark: the engine and its Python API
DimensionApache SparkPySpark
What it isA unified analytics engine for large-scale data processingThe Python API for Apache Spark
LanguagesAPIs in Scala, Java, Python and R, plus SQLPython, with SQL through spark.sql()
Where the work runsThe JVM, on a driver and executorsPython builds the plan; DataFrame work runs in the same JVM
Typed Dataset APIAvailable in Scala and JavaNot available; Python uses DataFrames
How you add itA Spark release, or Maven coordinatespip install pyspark, plus Java 17 or later
Custom functionsScala or Java UDFs run inside the JVMPython UDFs run in separate Python workers
Named in Indian data-engineer postings, 23 Aug to 27 Sep 2026Spark: 17.2% of 1,470Python: 42.7% of 1,470; PySpark was not counted on its own

Is PySpark slower than Scala Spark?

For DataFrame and SQL code, no. The Spark SQL guide says the same execution engine runs the query whichever API or language expresses it. When you write df.groupBy('city').agg(F.sum('amount')) in Python, Python only builds the plan; the work happens in the JVM executors, as it would in Scala. Spark's debugging guide adds that Python worker processes are not launched unless Python functions or data have to be handled. The cost appears when your own Python function touches each row. A Python UDF sends rows from the JVM to a Python worker and back, and the optimizer cannot see inside it. In Spark 4.2.0, regular Python UDFs use Arrow batches for that transfer by default (spark.sql.execution.pythonUDF.arrow.enabled is true), which cuts the cost but does not remove it. Built-in functions avoid it completely.

Built-in functions (F.when, F.regexp_extract, F.to_date): run in the JVM, same speed as Scala
Python UDF (F.udf): rows go to a Python worker and back; use only when no built-in fits
pandas UDF (pandas_udf): vectorized, works on whole pandas Series at a time
collect() and toPandas(): pull data into the driver; fine for small results only

Same arithmetic, two plans

spark.range(5).withColumn('sq', F.col('id') * F.col('id')).explain() prints a plan with no Python step. Now sq = F.udf(lambda x: x * x, 'long'); spark.range(5).withColumn('sq', sq('id')).explain() adds an ArrowEvalPython step (BatchEvalPython with Arrow off). That step is where rows leave the JVM.

When does Scala still matter for Spark work?

Scala matters when you write Spark itself, connectors or libraries, or code that needs the typed Dataset API, which Spark offers only in Scala and Java. Teams that wrote their jobs in Scala keep them, so a posting may ask you to maintain Scala. Scala applications must match the Scala version Spark was built for: Scala 2.13 since Spark 4.0.0. The India Tech Hiring Report did not count Scala, so this page gives no Scala share. It counted Java, named in 12.0% of 1,470 data-engineer postings, but a Java mention does not say whether the Java is for Spark. If your target postings say Scala, learn to read it.

Writing Spark internals, connectors or libraries: Scala or Java
Typed Datasets with compile-time checks: Scala or Java only
Maintaining an existing Scala codebase: learn to read Scala
ETL, analytics engineering and Databricks notebooks: PySpark and Spark SQL

Which should you learn for data engineering in India: Spark with Scala or PySpark?

Learn PySpark and Spark SQL first. In PrepNPlaced's India Tech Hiring Report, the 1,470 data-engineer postings collected 23 August to 27 September 2026 named SQL in 48.6%, Python in 42.7%, Databricks in 19.1% and Spark in 17.2%. The report matched Spark as a whole word, so a posting that writes only PySpark is not inside that 17.2%. PySpark uses the two skills these postings name most: Python, and SQL thinking for every join and aggregate. For data analysts the order is different: Spark appeared in 4.1% of 2,467 data-analyst postings, against SQL in 34.6%. The Data Engineering cohort teaches Spark in Module 2, Big Data and Apache Spark.

First: Python basics and SQL to window-function level
Then: PySpark DataFrames, joins, groupBy and window functions
Then: partitions, shuffles, broadcast joins and skew
Then: the platform your target postings name, such as Databricks

How do you use PySpark on Databricks?

On Databricks you write the same PySpark with less setup. A notebook attached to a cluster or serverless compute already has a SparkSession named spark, so you skip pip install, Java and session code. Databricks' PySpark guide teaches the model you learn locally: DataFrames, lazy transformations, and actions such as show or display. What changes is the platform: tables in Unity Catalog, jobs that run notebooks on a schedule, and Delta Lake as the default table format. If you prefer an editor, the Databricks extension for Visual Studio Code and Cursor runs local Python files on Databricks clusters or serverless compute.

spark is ready in every notebook; do not build your own SparkSession there
Read a table with spark.table('catalog.schema.table'); save one with df.write.saveAsTable(...)
display(df) shows a sortable table; show() still works

From the free book

Data Engineering: From Zero to Production & Interview ReadyChapter 9 · Apache Spark

The chapter reads physical plans line by line, which is the quickest way to see that Python and Scala DataFrame code end up as the same plan.

Read the 244-page book, free

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FAQ

Common Questions

Can I learn PySpark without learning Scala?

Yes. PySpark exposes Spark SQL, DataFrames, Structured Streaming, MLlib and Spark Core from Python, and both the Spark and Databricks docs teach it in Python. You need Scala only for the typed Dataset API, for writing Spark libraries, or for a team whose jobs are in Scala.

Is Spark SQL different from PySpark?

They are two ways into the same engine. spark.sql('SELECT city, SUM(amount) FROM orders GROUP BY city') returns a DataFrame, just as the DataFrame version does. You can mix them in one job: register a DataFrame with createOrReplaceTempView, query it in SQL, and continue in Python.

Do Indian companies use Scala or Python for Spark?

PrepNPlaced has no count for that: the India Tech Hiring Report did not count Scala or PySpark on their own. It counted Python, named in 42.7% of 1,470 data-engineer postings, and Spark, in 17.2% of 1,470, from 23 August to 27 September 2026. Read ten postings from your target companies and count Scala yourself.

Does PySpark work with pandas?

Yes, in three ways. toPandas() turns a small Spark result into a pandas DataFrame on the driver. The pandas API on Spark runs pandas-style code across a cluster. Pandas UDFs let a function work on pandas Series inside Spark, with Arrow moving the data.

What is the best way to prepare PySpark for a Databricks interview?

Solve graded PySpark questions until joins, groupBy and window functions come without looking them up; Coding Practice runs them on a real Spark sandbox. Then learn what Databricks adds: Delta Lake, Unity Catalog and jobs, which the Databricks interview questions page covers.

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Spark Interview Questions