What Is PySpark? The Python API for Apache Spark, How It Works, and Where It Runs
PySpark is the Python API for Apache Spark. You write DataFrame code in Python, and Spark's engine on the Java Virtual Machine runs it across many machines. Here is how the driver and executors split the work, why nothing runs until an action, and where PySpark runs.
By Durgesh Yadav — Senior Data Engineer @ 7-Eleven · Updated 15 Oct 2026. Preparation guidance, not a hiring guarantee.
What is PySpark, and why is it used?
PySpark is the Python API for Apache Spark, an open-source engine that processes large data across many machines. You write DataFrame or SQL code in Python, and Spark plans it and runs it in parallel on executors, so the same code works on a laptop sample and on a cluster in Databricks or Amazon EMR. Data engineers use it for ETL, joins and aggregations too big for pandas.
What is PySpark, and how is it related to Apache Spark?
PySpark is the Python API for Apache Spark, not a separate engine. Apache Spark is a unified analytics engine for large-scale data processing, with APIs in Java, Scala, Python and R. From Python, PySpark gives you Spark SQL, DataFrames, Structured Streaming, MLlib and Spark Core.
The engine runs on the Java Virtual Machine. The Spark 4.2.0 docs list Java 17, 21 or 25 and Python 3.10 or later, which is why installing PySpark on a laptop also means installing Java. "Spark experience" and "PySpark experience" point at the same engine; the second also names the language.
Apache Spark: the engine that splits data into partitions and processes them in parallel
PySpark: the Python package (pip install pyspark) that drives that engine
Spark SQL: the same engine through SQL; spark.sql() runs a query and returns a DataFrame
What does PySpark architecture look like: driver, executors and Py4J?
A PySpark application has one driver and many executors. Your Python script runs in the driver. When it creates a SparkSession, PySpark launches a JVM and talks to it through Py4J, so each DataFrame call becomes an instruction to Spark's engine. The driver turns your code into a plan, splits it into tasks and sends them to executors, the processes on worker machines that compute and hold data.
A cluster manager (Spark standalone, Hadoop YARN or Kubernetes) hands out the machines. Python worker processes start on the executors only when Python code must touch the data, such as a pandas UDF or the RDD API; DataFrame code built from Spark's own functions stays inside the JVM.
The parts of a PySpark application, and what each does in your job
Part
What it is
What it does in your job
Driver
Runs your Python script and the SparkSession
Builds the plan, splits it into stages and tasks, collects results
Py4J
The bridge library PySpark depends on
Carries each Python call to the driver's JVM
Cluster manager
Spark standalone, Hadoop YARN or Kubernetes
Gives the application machines, cores and memory
Executors
JVM processes on the worker machines
Run tasks on partitions and keep cached data
Python workers
Python processes next to the executors
Start only for Python UDFs, pandas UDFs or the RDD API
Spark UI
A web page on the driver, typically port 4040
Shows jobs, stages, tasks, shuffles and spills
What is lazy evaluation in PySpark?
Lazy evaluation means a transformation records what to do and computes nothing until an action asks for a result. filter, select, withColumn, join and groupBy are transformations; show, count, collect and write are actions. Spark waits so it can see the whole chain and pick an efficient plan, for example reading only the columns the query uses.
Two things follow. Runtime problems, such as a bad value under strict casting, surface at the action, so the error points at show() or write. And a DataFrame used twice is computed twice unless you cache it. pandas, by contrast, runs each step the moment you call it.
A first PySpark job (spark and F are ready in Coding Practice)
sales = spark.createDataFrame([('Pune', 1200), ('Pune', 800), ('Delhi', 500)], ['city', 'amount']); result = sales.filter(F.col('amount') > 600).groupBy('city').agg(F.sum('amount').alias('revenue')); result.show(). Nothing runs until show(). It prints one row, Pune with 2000, because Delhi's 500 is filtered out first.
See the plan Spark chose
result.explain() prints the physical plan without running the job. Exchange in it marks a shuffle, where rows move between executors.
Why is PySpark used, and when is pandas or SQL enough?
PySpark is used when data is too large for one machine, or when a job must run on a schedule over files in a data lake. Typical work: read raw CSV or JSON from cloud storage, clean and join it, aggregate it, and write Parquet or Delta tables for analysts. It also processes streams with Structured Streaming and prepares data for machine learning.
It is not always the right tool. For a few hundred megabytes on a laptop, pandas is usually quicker, because Spark spends time scheduling work. If the data already sits in Snowflake or BigQuery, SQL inside the warehouse often does the job. Interviewers like a candidate who can say when not to use Spark.
Batch ETL: raw files in, cleaned Parquet or Delta tables out
Joins and aggregations over tables larger than one machine's memory
Not needed: small files pandas reads in seconds, or data already in a warehouse
Where does PySpark run: Databricks, EMR, Fabric, Google Cloud or a laptop?
PySpark runs wherever Spark runs. Your DataFrame code stays nearly the same; what changes is who creates the session, where files live and how jobs are scheduled. In PrepNPlaced's India Tech Hiring Report, Databricks was named in 19.1% of 1,470 data-engineer postings and Spark in 17.2% of 1,470, collected 23 August to 27 September 2026.
Where PySpark runs, and what each place gives you
Where
What you get
Good for
Your laptop (local mode)
pip install pyspark plus Java; everything in one process
Learning, tests, small samples
Databricks
A platform built on Apache Spark, on AWS, Azure and Google Cloud
Lakehouse work on Delta tables
Amazon EMR
Managed Spark clusters on AWS
AWS teams with data in S3
Google Cloud
Managed Service for Apache Spark, the new name for Dataproc
Google Cloud teams, on clusters or serverless
Microsoft Fabric
Managed Spark pools behind Fabric notebooks
Microsoft-stack teams writing to OneLake
PrepNPlaced Coding Practice
A Spark sandbox with spark, F and Window ready
Graded interview-style questions
How do you try PySpark today without installing anything?
The PySpark track in PrepNPlaced's Coding Practice runs your code on a real Spark sandbox. Running is free; sign in to submit and get graded.
1Open Coding Practice at /data-practice and choose the PySpark track.
2Read the input tables, which arrive as DataFrames in a ready SparkSession called spark.
3Write your answer with DataFrame methods; F and Window are already imported.
4Assign the final DataFrame to a variable named result.
5Press Run to execute it on the Spark sandbox and see the first 50 rows.
6Press Submit to have the grader check your full result against the expected rows.
No. PySpark is a Python library, the Python API for Apache Spark. The language is Python; PySpark adds DataFrames, Spark SQL and the session that connects your code to Spark's engine. Learn ordinary Python first: functions, lists, dictionaries and reading files.
Is PySpark the same as Databricks?
No. Databricks is a data platform built on Apache Spark, and PySpark is one of the languages you write on it, next to SQL and Scala. The same PySpark code runs on your laptop, Amazon EMR, Google Cloud or Microsoft Fabric. On Databricks, the notebook creates the SparkSession for you.
Does PySpark need Java installed?
On your own machine, yes: the PySpark install guide for Spark 4.2.0 asks for Java 17 or later with JAVA_HOME set, and Python 3.10 or above. On Databricks, Amazon EMR, Google Cloud or Fabric, the platform provides Java.
Is PySpark slower than Spark with Scala?
Not for DataFrame and SQL code: Spark runs the same engine whichever language expresses the query. The gap appears with Python UDFs, which send data to separate Python processes and back. The Spark vs PySpark guide covers the detail.
Do data analysts use PySpark too?
Some do, but far fewer postings ask for it. In the India Tech Hiring Report, Spark was named in 4.1% of 2,467 data-analyst postings against 17.2% of 1,470 data-engineer postings, from 23 August to 27 September 2026. Analysts should learn SQL and a BI tool first.
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