The PySpark Certification Training Course Online is designed to provide learners with a comprehensive understanding of big data processing using Apache Spark and Python. This course is ideal for data engineers, data scientists, and big data professionals looking to master distributed computing, data transformations, and real-time analytics with PySpark. Achieving this certification enhances career opportunities in big data analytics and machine learning.

Read Review

Instructor led live online Classes

PySpark Certification Training Course Online

Instructor led live online Training (Weekday/Weekend)

Why Enroll in the PySpark Certification Training Course Online?

Master PySpark and Big Data: Learn Apache Spark architecture, RDDs, DataFrames, and Spark SQL.
Career Growth: Gain a globally recognized certification in big data analytics and machine learning.
Hands-on Labs: Work on real-world projects using PySpark.
In-Demand Skills: Learn data processing, machine learning pipelines, and real-time streaming with Spark Streaming.
Globally Recognized Certification: Showcase your expertise in big data analytics and PySpark to employers worldwide.

Course Description

The PySpark Certification Training Course Online validates your ability to process and analyze big data using Apache Spark and Python.

1. Data Engineers working on big data pipelines and ETL workflows. 2. Data Scientists leveraging PySpark for large-scale data processing. 3. Big Data Professionals implementing real-time analytics and machine learning models. 4. Beginners looking to start a career in big data analytics and machine learning.

1. Comprehensive curriculum covering Apache Spark fundamentals and PySpark techniques. 2. Hands-on practice with real-world big data scenarios. 3. 24/7 expert support for technical guidance.

What you'll learn

Introduction to PySpark: Understanding Apache Spark architecture and components.

Data Processing with RDDs and DataFrames: Working with Resilient Distributed Datasets (RDDs) and Spark DataFrames.

Spark SQL: Implementing structured data processing.

Machine Learning with MLlib: Building scalable machine learning models.

Real-Time Data Processing with Spark Streaming: Handling streaming data with PySpark.

Requirements

Basic Python Knowledge: Familiarity with Python programming and data structures.
Understanding of Big Data Concepts: Knowledge of Hadoop, SQL, or distributed computing is beneficial.

Curriculum Designed by Experts

What is Big Data?
Big Data Customer Scenarios
Limitations and Solutions of Existing Data Analytics Architecture with Uber Use Case
How Hadoop Solves the Big Data Problem?
What is Hadoop?
Hadoop’s Key Characteristics
Hadoop Ecosystem and HDFS
Hadoop Core Components
Rack Awareness and Block Replication
YARN and its Advantage
Hadoop Cluster and its Architecture
Hadoop: Different Cluster Modes
Big Data Analytics with Batch & Real-Time Processing
Why Spark is Needed?
What is Spark?
How Spark Differs from its Competitors?
Spark at eBay
Spark’s Place in Hadoop Ecosystem

Hadoop terminal commands

Overview of Python
Different Applications where Python is Used
Values, Types, Variables
Operands and Expressions
Conditional Statements
Loops
Command Line Arguments
Writing to the Screen
Python files I/O Functions
Numbers
Strings and related operations
Tuples and related operations
Lists and related operations
Dictionaries and related operations
Sets and related operations

Creating “Hello World” code
Demonstrating Conditional Statements
Demonstrating Loops
Tuple - properties, related operations, compared with list
List - properties, related operations
Dictionary - properties, related operations
Set - properties, related operations

Functions
Function Parameters
Global Variables
Variable Scope and Returning Values
Lambda Functions
Object-Oriented Concepts
Standard Libraries
Modules Used in Python
The Import Statements
Module Search Path
Package Installation Ways

Functions - Syntax, Arguments, Keyword Arguments, Return Values
Lambda - Features, Syntax, Options, Compared with the Functions
Sorting - Sequences, Dictionaries, Limitations of Sorting
Errors and Exceptions - Types of Issues, Remediation
Packages and Module - Modules, Import Options, sys Path

Spark Components & its Architecture
Spark Deployment Modes
Introduction to PySpark Shell
Submitting PySpark Job
Spark Web UI
Writing your first PySpark Job Using Jupyter Notebook
Data Ingestion using Sqoop

Building and Running Spark Application
Spark Application Web UI
Understanding different Spark Properties

Challenges in Existing Computing Methods
Probable Solution & How RDD Solves the Problem
What is RDD, Its Operations, Transformations & Actions
Data Loading and Saving Through RDDs
Key-Value Pair RDDs
Other Pair RDDs, Two Pair RDDs
RDD Lineage
RDD Persistence
WordCount Program Using RDD Concepts
RDD Partitioning & How it Helps Achieve Parallelization
Passing Functions to Spark

Loading data in RDDs
Saving data through RDDs
RDD Transformations
RDD Actions and Functions
RDD Partitions
WordCount through RDDs

Need for Spark SQL
What is Spark SQL
Spark SQL Architecture
SQL Context in Spark SQL
Schema RDDs
User Defined Functions
Data Frames & Datasets
Interoperating with RDDs
JSON and Parquet File Formats
Loading Data through Different Sources
Spark-Hive Integration

Spark SQL – Creating data frames
Loading and transforming data through different sources
Stock Market Analysis
Spark-Hive Integration

Why Machine Learning?
What is Machine Learning?
Where Machine Learning is Used?
Face Detection: USE CASE
Different Types of Machine Learning Techniques
Introduction to MLlib
Features of MLlib and MLlib Tools
Various ML algorithms supported by MLlib

Face detection use case

Supervised Learning - Linear Regression, Logistic Regression, Decision Tree, Random Forest
Unsupervised Learning - K-Means Clustering & How It Works with MLlib
Analysis on US Election Data using MLlib (K-Means)

Machine Learning MLlib
K- Means Clustering
Linear Regression
Logistic Regression
Decision Tree
Random Forest

Need for Kafka
What is Kafka
Core Concepts of Kafka
Kafka Architecture
Where is Kafka Used
Understanding the Components of Kafka Cluster
Configuring Kafka Cluster
Kafka Producer and Consumer Java API
Need of Apache Flume
What is Apache Flume
Basic Flume Architecture
Flume Sources
Flume Sinks
Flume Channels
Flume Configuration
Integrating Apache Flume and Apache Kafka

Configuring Single Node Single Broker Cluster
Configuring Single Node Multi Broker Cluster
Producing and consuming messages
Flume Commands
Setting up Flume Agent
Streaming Twitter Data into HDFS

Drawbacks in Existing Computing Methods
Why Streaming is Necessary
What is Spark Streaming
Spark Streaming Features
Spark Streaming Workflow
How Uber Uses Streaming Data
Streaming Context & DStreams
Transformations on DStreams
Describe Windowed Operators and Why it is Useful
Important Windowed Operators
Slice, Window and ReduceByWindow Operators
Stateful Operators

WordCount Program using Spark Streaming

Apache Spark Streaming: Data Sources
Streaming Data Source Overview
Apache Flume and Apache Kafka Data Sources
Example: Using a Kafka Direct Data Source

Various Spark Streaming Data Sources

Project 1- Domain: Finance
Project 2- Domain: Media and Entertainment

Implementing an End-to-End Project

Introduction to Spark GraphX
Information about a Graph
GraphX Basic APIs and Operations
Spark GraphX Algorithm - PageRank, Personalized PageRank, Triangle Count, Shortest Paths, Connected Components, Strongly Connected Components, Label Propagation

The Traveling Salesman problem
Minimum Spanning Trees

Free Career Councelling

we are happy to help you 24*7

Email Id

Mobile Number

Achieve Certification with Our 100% Pass Guarantee.

PySpark Certification Training Course Online

Read Review

Instructor led live online Classes

PySpark Certification Training Course Online