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PySpark Certification Training Course
PySpark Certification Training Course
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Live Online Classes starting on 28th Dec 2024
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Instructor-led Python Spark Certification Training using PySpark live online Training Schedule
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21,995
Starts at 7,332 / monthWith No Cost EMI Know more
Why enroll for PySpark course?
PySpark Course Benefits
There are several industries making significant investments in big data analytics, including banking, retail, manufacturing, finance, healthcare, and government, aiming to make more informed business decisions. This translates into a variety of jobs being created within each sector, requiring individuals with expertise in this field. Furthermore, it is forecasted that the increasing demand for these roles far exceeds the current supply. Obtaining a PySpark certification will undoubtedly enhance your chances of securing a rewarding job with an attractive salary.
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Want to become a Big Data Engineer?
Annual Salary
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Want to become a Big Data Engineer?
Annual Salary
Hiring Companies
Want to become a Big Data Engineer?
Why PySpark course from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your PySpark course
Skills Covered
Tools Covered
PySpark Certification Course Curriculum
Curriculum Designed by Experts
Introduction to Big Data Hadoop and Spark
18 Topics
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
![skill]( "Hands On Experience")
- Hadoop terminal commands
![skill]( "skill you will learn")
- Hadoop components and its architecture
- Storing data in HDFS
- Working with HDFS commands
Introduction to Python for Apache Spark
15 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Writing Python Programs
- Implementing Collections in Python
Functions, OOPs, and Modules in Python
11 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Implementing OOPs Concepts
- Functional Programming
Deep Dive into Apache Spark Framework
7 Topics
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
![skill]( "Hands On Experience")
- Building and Running Spark Application
- Spark Application Web UI
- Understanding different Spark Properties
![skill]( "skill you will learn")
- Writing basic Spark application
- Spark architecture and its components
- Ingesting structured data into HDFS
Playing with Spark RDDs
11 Topics
Topics
- 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
![skill]( "Hands On Experience")
- Loading data in RDDs
- Saving data through RDDs
- RDD Transformations
- RDD Actions and Functions
- RDD Partitions
- WordCount through RDDs
![skill]( "skill you will learn")
- Transformations and actions in Spark
- Implementing RDDs in Spark
DataFrames and Spark SQL
11 Topics
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
![skill]( "Hands On Experience")
- Spark SQL – Creating data frames
- Loading and transforming data through different sources
- Stock Market Analysis
- Spark-Hive Integration
![skill]( "skill you will learn")
- Working with DataFrame API
- Querying structured data using Spark SQL
- Integrating Spark with Hive
Machine Learning using Spark MLlib
8 Topics
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
![skill]( "Hands On Experience")
- Face detection use case
![skill]( "skill you will learn")
- Understanding machine learning
- Functions and features of MLlib
Deep Dive into Spark MLlib
3 Topics
Topics
- 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)
![skill]( "Hands On Experience")
- Machine Learning MLlib
- K- Means Clustering
- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
![skill]( "skill you will learn")
- Working with machine learning algorithms
- Implementing Spark MLlib
Understanding Apache Kafka and Apache Flume
16 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Ingesting unstructured data into HDFS
- Working with Kafka command line tools
Apache Spark Streaming - Processing Multiple Batches
12 Topics
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
![skill]( "Hands On Experience")
- WordCount Program using Spark Streaming
![skill]( "skill you will learn")
- Working with DStream API
Apache Spark Streaming - Data Sources
4 Topics
Topics
- Apache Spark Streaming: Data Sources
- Streaming Data Source Overview
- Apache Flume and Apache Kafka Data Sources
- Example: Using a Kafka Direct Data Source
![skill]( "Hands On Experience")
- Various Spark Streaming Data Sources
![skill]( "skill you will learn")
- Real-time data processing
- Building data pipelines
Implementing an End-to-End Project
2 Topics
Topics
- Project 1- Domain: Finance
- Project 2- Domain: Media and Entertainment
![skill]( "Hands On Experience")
- Implementing an End-to-End Project
![skill]( "skill you will learn")
- Building a data pipeline
Spark GraphX (Self-paced)
4 Topics
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
![skill]( "Hands On Experience")
- The Traveling Salesman problem
- Minimum Spanning Trees
![skill]( "skill you will learn")
- Spark GraphX programming concepts and operations
- Implementing GraphX algorithms
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Course Details
Overview
The PySpark course is designed to provide you with the knowledge and skills needed to become a successful Big Data & Spark Developer. This PySpark online training will help you clear the CCA Spark and Hadoop Developer (CCA175) Examination. You will understand the basics of Big Data and Hadoop, along with how Spark enables in-memory data processing and runs much faster than Hadoop MapReduce. This course also covers RDDs, Spark SQL for structured processing, and different APIs offered by Spark, such as Spark Streaming, Spark MLlib, HDFS, Flume, Spark GraphX, and Kafka. The best PySpark online courses are an integral part of a Big Data Developer’s career path.
What are the prerequisites for this Course?
There are no prerequisites for the PySpark training course. Prior work experience is also not required. Knowledge of Python programming and SQL will be an added advantage.
What skills will you learn in this certification course?
You will learn about HDFS, Hadoop 2.x, the Spark ecosystem, Spark SQL, and MLlib. The course also covers real-time data processing with Kafka, Flume, and Spark Streaming. Additionally, you will work on practical projects using Edureka’s CloudLab.
How will I execute the practicals in this ?
You are required to complete all your assignments and Case Studies using the VM provided by Edureka. In case you have any doubts or questions, Edureka’s Support Team will be available 24/7 for prompt assistance.
What are the system requirements for the PySpark Certification Training ?
It would help if you had good internet connectivity and a Mobile/tab/laptop/system installed with Zoom/Meet, which is required for the PySpark online training. In addition, we will provide Cloud LAB, a pre-configured environment with the necessary tools and services for executing your practicals.
Once payment is received, you will automatically receive a payment receipt and access information via email.
Projects
Industry: Finance
A leading financial bank is trying to broaden the financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure thi....Industry: Transportation
With the spike in pollution levels and the fuel prices, many Bicycle Sharing Programs are running around the world. Bicycle sharing systems are a means of renting bicycles where ....PySpark Certification
To unlock Edureka’s PySpark course completion certificate, you must ensure the following:
- Fully participate in this PySpark Certification Training Course.
- Complete the assessments and projects listed.
This will help you gain the essential knowledge and skills to become a successful Big Data & Spark Developer.
Please visit the page which will guide you through the top Apache Spark Interview questions and answers.
Upon completing the training, Edureka will provide you with a course completion certificate, which is valid for a lifetime.
Concepts like Spark Libraries, RDD, Spark Core, HDFS commands, and architecture are the building blocks that will help you become a PySpark developer.
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Vinayak TalikotSenior Software Engineer
Vinayak shares his Edureka learning experience and how our Big Data training helped him achieve his dream career path.
Sriram GopalAgile Coach
Sriram speaks about his learning experience with Edureka and how our Hadoop training helped him execute his Big Data project efficiently.
Balasubramaniam MuthuswamyTechnical Program Manager
Our learner Balasubramaniam shares his Edureka learning experience and how our training helped him stay updated with evolving technologies.
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FAQs
What if I have queries after completing this PySpark Training course?
You will have lifetime access to the Support Team, available 24/7. The team will assist you in resolving queries during and after the course.
What if I miss a live class ?
"At Edureka, you will never miss a lecture. You have two options::
- View the recorded session of the class available in your LMS.
- Attend the missed session in any other live batch."
Will I receive placement assistance after completing this training?
- To assist you in your job search, we have included a resume builder tool in your LMS. This tool enables you to create a winning resume in just three easy steps. You will have unlimited access to various templates suitable for different roles and designations. Simply log in to your LMS and click on the "create your resume" option.
Is the course material accessible to students even after completing the training?
Yes, you will have lifetime access to the course material once you have enrolled in the course.
Can I attend a demo session before enrolling?
- To maintain quality standards, we have a limited number of participants in each live session. Therefore, it is not possible to participate in a live class without enrollment. However, you can go through the sample class recording, which will give you a clear insight into how the classes are conducted, the quality of instructors, and the level of interaction in a class.
Who are the instructors?
- All the instructors at Edureka are practitioners from the industry with a minimum of 10-12 years of relevant IT experience. They are subject matter experts and have been trained by Edureka to provide an excellent learning experience to the participants.
Can I cancel my enrollment? Will I get a refund?
Yes, you can cancel your enrollment. If you get a refund, you should claim it within three days of registering for the course. The money-back guarantee is void if the learner fails to raise a refund request within three days of purchasing the course.
What if I have more queries related to this PySpark online course?
- You can contact us via phone at +91 88808 62004/1800 275 9730 (US Toll-free Number) or email us at sales@edureka.co .
How can I learn more about this PySpark course?
Contact us using the "Drop us a Query" form or +91 88808 62004/1800 275 9730 (US Toll-free Number) or email us at sales@edureka.co. Our customer service representatives will be able to give you more details.
Does Edureka provide financial assistance for this course?
We offer various financing options including No Cost EMI, to ensure flexible payment solutions for our learners. For more details, please check our pricing section.
What is covered under the 24/7 Support promise?
Our dedicated team will support you 24/7 through email, chat, and calls even after you have completed your course with us.
What is the course fee?
The Actual course fee is 21,995 . The fee is available starting at 6,232 / month with No EMI cost.
Can I download the full course content?
Yes, you can download the full syllabus for this training course from the Curriculum section.
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