Python Spark Certification Training Course

BY
Edureka

To acquire expertise in Python and prepare for the Cloudera Hadoop and Spark Developer certification exam (CCA175).

Mode

Online

Duration

6 Weeks

Fees

₹ 19795 21995

Important Dates

28 Dec, 2024

Course Commencement Date

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom
Mode of Delivery Video and Text Based
Frequency of Classes Weekends

Course overview

Through the Edureka Python Spark Certification Training using PySpark, you will develop an extensive understanding of Python. In addition, the certification training course familiarizes you with the fundamentals of Hadoop and Big Data.

The Python Spark Certification using PySpark certification course primarily prepares you for the CCA Spark and Hadoop Developer Exam; you will gain core skills and knowledge of Spark and the Spark Ecosystem. The course curriculum also covers Spark SQL and Spark Resilient Distributed Datasets for streamlined processing. 

Moreover, the programme touches down at Flume, Spark Streaming, Kafka, and other Spark Ecosystem components. With the accessibility of Edureka Cloud Lab, you can look forward to gaining practical experience in the field. 

In fact, the course curriculum is crafted by industry professionals to ensure the certification exam training is in-line with the latest trends and industry use cases. When you complete the Python Spark Certification Training using PySpark programme satisfactorily, Edureka will certify you as an Apache Spark and Scala Developer using Python.

The highlights

  • 36 hours live instructor-led training
  • Apache Spark and Scala Developer Using Python certification from Edureka
  • Live industry application projects
  • Community forum
  • 60 days of access to Cloud Lab
  • Real-world case studies for vivid and engaging learning
  • 24x7 expert help
  • Class recordings over LMS

Program offerings

  • Instructor-led online training
  • Apache spark and scala developer using python certification
  • Live projects
  • Hands-on experience
  • Cloud lab

Course and certificate fees

Fees information
₹ 19,795  ₹21,995

The Python Spark Certification Training Using PySpark course fee has two components: the course fee and GST. You can pay the fee in either a lump sum or zero-interest EMI.  

Python Spark Certification Training Using PySpark Fee Details

HeadAmount
Original PriceRs. 21,995
Discounted PriceRs. 19,795

*EMI Starts at Rs. 6,599 / month

certificate availability

Yes

certificate providing authority

Edureka

Who it is for

The following professionals can benefit from taking the Edureka Python Spark Certification Training Using PySpark programme:

  • Engineers 
  • Big Data Developers
  • Data Architects
  • Business Intelligence Professionals
  • Mainframe Professionals
  • Data Warehouse Professionals
  • IT Professionals
  • University freshers
  • Data Scientists 
  • Big Data Analytics Architects 
  • Analytics Professionals

Eligibility criteria

There are no specific prerequisites for the Python Spark Certification Training Using PySpark course by Edureka. However, prior experience with Python Programming and SQL can be useful but not necessary.

Furthermore, candidates need to complete a certification project. The project consists of implementing the concepts that they learned in the training course. Students have two weeks from the date of course completion to mail the project to the support team of Edureka. Experts at Edureka will evaluate the project based on performance and provide a grade and certification. 

Certificate Qualifying Details

Edureka will certify candidates as Apache Spark and Scala Developer Using Python if you complete the project successfully. 

What you will learn

Sql knowledge Knowledge of python Knowledge of big data Knowledge of kafka

The Edureka Python Spark Certification Training Using PySpark programme will help you become adept in the following:

  • Acquire a comprehensive understanding of Big Data, the shortcomings of the existing associated challenges, and how Hadoop helps solve those challenges.
  • Gain proficiency in the core concepts of Python language and its applications.
  • Learn to create and run various Spark tools and applications. 
  • Learn to leverage Sqoop for ingesting data and Spark RDDs for the implementation of various business logics such as actions and transformations.
  • Develop an in-depth understanding of the advantages of machine learning.
  • Acquire proficiency in Spark MLib for various ML algorithm implementations such as Decision Tree and Linear Regression.
  • Be proficient in processing structured data using SQL Queries and different SQL operations, and integration of Hive and Spark.
  • Master creation of an Apache Streaming application from your knowledge of various data streaming sources. 
  • Gain knowledge of the ingestion of data through Apache Flume while the data is being streamed.
  • Learn the configuration of multiple kinds of Apache Kafka Clusters and master the integration of Kafka and Flume for processing events.  

The syllabus

Introduction to Big Data Hadoop and Spark

Topics
  • 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
Hands-On
  • Hadoop terminal commands

Introduction to Python for Apache Spark

Topics
  • 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
Hands-On
  • 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, OOPs, and Modules in Python

Topics
  • 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
Hands-On
  • 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

Deep Dive into Apache Spark Framework

Topics
  • 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
Hands-On
  • Building and Running Spark Application
  • Spark Application Web UI
  • Understanding different Spark Properties

Playing with Spark RDDs

Topics
  • Challenges in Existing Computing Methods
  • Probable Solution & How RDD Solves the Problem
  • What is RDD, It’s 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
Hands-On
  • Loading data in RDDs
  • Saving data through RDDs
  • RDD Transformations
  • RDD Actions and Functions
  • RDD Partitions
  • WordCount through RDDs

DataFrames and Spark SQL

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

Machine Learning using Spark MLlib

Topics
  • 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
Hands-On
  • Face detection use case

Deep Dive into Spark MLlib

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

Understanding Apache Kafka and Apache Flume

Topics
  • 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
Hands-On
  • Configuring Single Node Single Broker Cluster
  • Configuring Single Node Multi-Broker Cluster
  • Producing and consuming messages through Kafka Java API
  • Flume Commands
  • Setting up Flume Agent
  • Streaming Twitter Data into HDFS

Apache Spark Streaming - Processing Multiple Batches

Topics
  • 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
Hands-On
  • WordCount Program using Spark Streaming

Apache Spark Streaming - Data Sources

Topics
  • Apache Spark Streaming: Data Sources
  • Streaming Data Source Overview
  • Apache Flume and Apache Kafka Data Sources
  • Example: Using a Kafka Direct Data Source
Hands-On
  • Various Spark Streaming Data Sources

Implementing an End-to-End Project

Topics
  • Project 1- Domain: Finance
  • Project 2- Domain: Media and Entertainment
Hands-On
  • Implementing an End-to-End Project

Spark GraphX (Self-Paced)

Topics
  • 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
Hands-On
  • The Traveling Salesman problem
  • Minimum Spanning Trees

Admission details

To enroll in the Edureka Python Spark Certification Training Using PySpark course by Edureka, follow the steps listed below:

Step 1– Visit the official website of Edureka: https://www.edureka.co/

Step 2– Look for the "Python Spark Certification Training Using PySpark" course.

Step 3– Scroll and find the “Enroll now” button on the top of the webpage.

Step 4– Click on the "Enroll now" tab and fill the form with your details.

Step 5– To apply for the course, you need to fill the application form and provide your contact information – email address and phone number.

Step 6– You will be redirected to a webpage where you need to select a batch as per your choice.

Step 7– Lastly, you have to choose the mode of payment and finish paying the net applicable course fee. Download the receipt for future reference.


Filling the form

You need to fill out an online application form on Edureka's official website to register for the Python Spark Certification Training using PySpark programme. As requested in the online application, you must provide your contact information and select a course. You can choose the preferred mode of payment and process the payment to confirm your application.

How it helps

With the Python Spark Certification Training Using PySpark programme, you will gain a thorough understanding of the Big Data and Hadoop ecosystem which will be beneficial for you as a Spark Developer. You will be solving real-world challenges which will enhance your decision-making and logic building skills.

The course curriculum covers all essential concepts and techniques, from Spark Streaming to ML using MLib. When you complete the training course, Edureka will certify you as an Apache Spark and Scala Developer Using Python.

Moreover, the Big Data landscape is expanding tremendously. Upon completion of the Edureka Python Spark Certification Training Using PySpark course by Edureka, you can find plenty of lucrative job opportunities as a certified Spark Developer.

FAQs

Why should I learn PySpark?

PySpark is Spark with Python API that helps you to leverage the simplicity of Python and use Apache Spark to conquer Big Data. Besides, numerous job opportunities are available for certified Apache Spark Developers using Python in the budding Big Data Analytics market.

What is the duration of the Edureka Python Spark Certification Training Using PySpark

The Python Spark Certification Training Using PySpark course by Edureka is a 6 weeks programme covered in online and weekend classes. 

What skills will I learn by pursuing the Python Spark Certification Training Using PySpark programme?

You will acquire skills such as Sqoop data loading techniques, implementation of Apache Spark operations, integration of Apache Kafka with Apache Flume. You will also learn to apply clustering algorithms with the Spark MLib APIs, and more with the Edureka Python Spark Certification Training Using PySpark course. 

Am I eligible to attend a demo session?

Unfortunately, to maintain the quality of classes, only a limited number of learners are allowed in every demo session. You cannot attend any demo before you enrol in the course. However, you can view the sample recordings to acquire clarity before you enrol in the certification training. 

What is the mode of learning for the programme?

The Edureka Python Spark Certification Training Using PySpark course by Edureka is provided via instructor-led online sessions. Candidates have two options; they can either take weekday classes or weekend classes based on convenience.

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