Big Data

Pyspark

Pysparks offers introduction to programming Spark with Python, equipping you with the skills to harness the full…

PySpark Training:

PySpark is the Python API for Apache Spark, a powerful open-source distributed computing framework designed to process large amounts of data efficiently. PySpark enables high-speed data transformation, analysis, and machine learning across clusters of computers, making it one of the best tools for handling big data. 

With PySpark, you can work with structured and unstructured data, perform complex computations in real-time, and build scalable machine learning models. Whether you’re handling batch processing, real-time analytics, or advanced AI-driven insights, PySpark provides a user-friendly yet powerful solution for managing large datasets. 

Learn how to process large datasets efficiently with PySpark, the powerful combination of Apache Spark and Python. This PySpark online course offers comprehensive coverage of PySpark fundamentals, data transformations, analysis, and an introduction to machine learning. Whether you’re looking for a PySpark training to boost your skills or an in-depth learning experience, this course is designed to take you from basic concepts to real-world applications. This PySpark course includes easy-to-follow lessons and hands-on projects to help you gain the skills needed to work with big data effectively.  

Show More

What Will You Learn?

  • Introduction to PySpark and Apache Spark:Learn the basics of Apache Spark and how PySpark uses Python to handle big data.Understand Spark's architecture and why it's a powerful tool for data processing.
  • Working with RDDs and DataFrames:Explore how to create and manipulate Resilient Distributed Datasets (RDDs) and DataFrames.Learn simple operations to filter, transform, and aggregate data.
  • Data Processing with Spark SQL: Discover how to use Spark SQL for querying data easily.Learn to run SQL queries on large datasets and integrate with DataFrames.
  • Introduction to Machine Learning with MLlib: Get started with MLlib to build basic machine learning models. Learn the concepts behind training and evaluating models on big data.
  • Performance Tuning and Optimization: Understand best practices for optimizing PySpark applications. Learn simple tips to improve the speed and efficiency of your data processing tasks.
  • Hands-On Projects and Real-World Applications: Apply your skills on practical projects that mimic real-life data challenges. Gain confidence by building end-to-end solutions using PySpark.

Course Curriculum

Module 1: Introduction to PySpark

  • 1.1 What is PySpark?
  • 1.1.1 Overview
  • :: PySpark vs. Apache Spark
  • :: Key features and benefits of PySpark
  • 1.1.2 Architecture
  • :: Spark architecture overview
  • :: Components: Spark Core, Spark SQL, DataFrames, Datasets, MLlib, Spark Streaming
  • 1.2 Setting Up PySpark
  • 1.2.1 Installation
  • :: Installing PySpark on local machine (using pip or conda)
  • :: Setting up a PySpark development environment (Jupyter Notebooks, IDEs)
  • 1.2.2 Configuration
  • :: Configuring Spark with Hadoop
  • :: Setting up Spark clusters on local, standalone, and cloud environments
  • 1.3 PySpark Basics
  • 1.3.1 Understanding RDDs (Resilient Distributed Datasets)
  • :: RDD concepts: creation, transformations, actions
  • :: Working with RDDs in PySpark
  • 1.3.2 Introduction to DataFrames
  • :: DataFrame creation and operations
  • :: Difference between RDDs and DataFrames
  • 1.4 Hands-On Exercise: Getting Started with PySpark
  • 1.4.1 Environment Setup
  • :: Install PySpark and set up a local environment
  • 1.4.2 Basic Operations
  • :: Create and manipulate RDDs and DataFrames

Module 2: DataFrames and SQL

Module 3: Advanced Data Processing

Module 4: Machine Learning with PySpark

Module 5: Advanced Topics and Best Practices

Module 6: Real-World Projects and Case Studies

A. Course Resources

B. Assignments and Evaluation

No Data Available in this Section
No Data Available in this Section