PySpark Tutorial

PySpark is the Python API for Apache Spark, designed for big data processing and analytics. It lets Python developers use Spark's powerful distributed computing to efficiently process large datasets across clusters. It is widely used in data analysis, machine learning and real-time processing.

Important Facts to Know

• Distributed Computing: PySpark runs computations in parallel across a cluster, enabling fast data processing.
• Fault Tolerance: Spark recovers lost data using lineage information in resilient distributed datasets (RDDs).
• Lazy Evaluation: Transformations aren’t executed until an action is called, allowing for optimization.

What is PySpark Used For?

PySpark lets you use Python to process and analyze huge datasets that can’t fit on one computer. It runs across many machines, making big data tasks faster and easier. You can use PySpark to:

• Perform batch and real-time processing on large datasets.
• Execute SQL queries on distributed data.
• Run scalable machine learning models.
• Stream real-time data from sources like Kafka or TCP sockets.
• Process graph data using GraphFrames.

Why Learn PySpark?

PySpark is one of the top tools for big data. It combines Python’s simplicity with Spark’s power, making it perfect for handling huge datasets.

• Enables efficient processing of petabyte-scale datasets.
• Integrates seamlessly with the Python ecosystem (pandas, NumPy, scikit-learn).
• Offers unified APIs for batch, streaming, SQL, ML and graph processing.
• Runs on Hadoop, Kubernetes, Mesos or standalone.
• Powering companies like Walmart, Trivago and many more.

PySpark Basics

Learn how to set up PySpark on your system and start writing distributed Python applications.

• Introduction to PySpark
• Installing PySpark in Jupyter Notebook
• Installing Pyspark in kaggle
• Checking Pyspark Version

Working with PySpark

Start working with data using RDDs and DataFrames for distributed processing.

Creating RDDs and DataFrames: Build DataFrames in multiple ways and define custom schemas for better control.

• Load data from external sources (CSV, JSON, Parquet)
• Convert between RDDs and DataFrames
• Create a DataFrame
• From multiple lists
• From dictionary
• Using custom row objects
• Appy custom Schema

Data Operations

Basic Transformations

Perform transformations like joins, filters and mappings on your datasets.

• Join Types
• Join on Multiple Columns

Column Operations

Manipulate DataFrame columns add, rename or modify them easily.

• Add Constant Column
• Add New Column
• Add Multiple Columns
• Change Column Names
• Rename Columns for Aggregates
• Rename Column by Index

Data Cleaning and Null Handling

Clean your dataset by dropping or filtering out null and unwanted values.

• Drop Rows by Value
• Drop NULL/None Rows
• Filter NULL Columns
• Use isNotNull
• Clean Data
• Clean Using dropna()
• Drop Columns with All Nulls

Transformations and String/Array Ops

Use advanced transformations to manipulate arrays and strings.

• String to Array
• Union and UnionAll
• Pivot Function
• Add Column from Other Columns
• Show Full Column Content

Filtering and Selection

Extract specific data using filters and selection queries.

• Where Filter
• GroupBy and Filter
• Count Distinct
• Show Distinct Column Values
• Select Columns by Type
• Get Specific Row

Sorting and Ordering

Sort your data for better presentation or grouping.

• Sort Within Groups
• orderBy() and sort()
• Sort Columns Asc/Desc

Machine Learning with PySpark (MLlib)

Train ML models on large data with built-in tools for classification, regression and clustering.

• Introduction to MLlib
• LogisticRegression with MLib
• Linear regression with MLlib
• Clustering (e.g., KMeans)

Advanced PySpark Techniques

Improve performance and scale by using advanced features.

Partitioning and Performance Optimization

Split data into smaller parts for faster processing and less memory usage.

• Partitioning data
• Show RDD Partitions

Data Manipulation using UDFs

Create user-defined functions (UDFs) to apply custom logic.

• Sort list using UDF
• MapType in UDF

Aggregation and Collection

Summarize your data using powerful aggregation functions.

• GroupBy
• sum() Function
• collect() Function

Core PySpark Modules

Explore PySpark’s four main modules to handle different data processing tasks.

PySpark Core

This module is the foundation of PySpark. It provides support for Resilient Distributed Datasets (RDDs) and low-level operations, enabling distributed task execution and fault-tolerant data processing.

Common PySpark Core Methods

PySpark SQL

The SQL module allows users to process structured data using DataFrames and SQL queries. It supports a wide range of data formats and provides optimized query execution with the Catalyst engine.

Common PySpark SQL Methods

PySpark MLlib

MLlib is PySpark’s scalable machine learning library. It includes tools for preprocessing, classification, regression, clustering and model evaluation, all optimized to run in a distributed environment.

Common PySpark MLlib Methods

PySpark Streaming

This module allows processing of real-time data streams from sources like Kafka or sockets. It works using DStreams (Discretized Streams) which enable micro-batch stream processing.

Common PySpark Streaming Methods