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Python Spark Certification Training using PySpark

Python Spark Certification Training using PySpark

Edureka’s PySpark Certification Training is designed to provide you the knowledge and skills that are required to become a successful Spark Developer using Python and prepare you for the Cloudera Hadoop and Spark Developer Certification Exam (CCA175). Throughout the PySpark Training, you will get an in-depth knowledge of Apache Spark and the Spark Ecosystem, which includes Spark RDD, Spark SQL, Spark MLlib and Spark Streaming. You will also get comprehensive knowledge of Python Programming language, HDFS, Sqoop, Flume, Spark GraphX and Messaging System such as Kafka.

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60 days of Cloud Lab access

Why should you take Python Spark Certification Training?

Apache Spark will dominate the Big Data landscape by 2022 - Wikibon

As per Indeed.com, the average salary for Spark Developer is $180,000 per annum.

Instructor-led Python Spark Certification Training live online classes

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Python Spark Course Curriculum

Introduction to Big Data Hadoop and Spark

Learning Objectives: In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, Hadoop ecosystem components, Hadoop Architecture, HDFS, Rack Awareness, and Replication. You will learn about the Hadoop Cluster Architecture, important configuration files in a Hadoop Cluster. You will also get an introduction to Spark, why it is used and understanding of the difference between batch processing and real-time processing.

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

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Introduction to Python for Apache Spark

Learning Objectives: In this module, you will learn basics of Python programming and learn different types of sequence structures, related operations and their usage. You will also learn diverse ways of opening, reading, and writing to files.

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

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Functions, OOPs, and Modules in Python

Learning Objectives: In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.

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

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Deep Dive into Apache Spark Framework

Learning Objectives: In this module, you will understand Apache Spark in depth and you will be learning about various Spark components, you will be creating and running various spark applications. At the end, you will learn how to perform data ingestion using Sqoop.

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

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Playing with Spark RDDs

Learning Objectives: In this module, you will learn about Spark - RDDs and other RDD related manipulations for implementing business logics (Transformations, Actions, and Functions performed on RDD).

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

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DataFrames and Spark SQL

Learning Objectives: In this module, you will learn about SparkSQL which is used to process structured data with SQL queries. You will learn about data-frames and datasets in Spark SQL along with different kind of SQL operations performed on the data-frames. You will also learn about the Spark and Hive integration.

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

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Machine Learning using Spark MLlib

Learning Objectives: In this module, you will learn about why machine learning is needed, different Machine Learning techniques/algorithms and their implementation 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

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Deep Dive into Spark MLlib

Learning Objectives: In this module, you will be implementing various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and many more.

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

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Understanding Apache Kafka and Apache Flume

Learning Objectives: In this module, you will understand Kafka and Kafka Architecture. Afterwards you will go through the details of Kafka Cluster and you will also learn how to configure different types of Kafka Cluster. After that you will see how messages are produced and consumed using Kafka API’s in Java. You will also get an introduction to Apache Flume, its basic architecture and how it is integrated with Apache Kafka for event processing. You will learn how to ingest streaming data using 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

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Apache Spark Streaming - Processing Multiple Batches

Learning Objectives: In this module, you will work on Spark streaming which is used to build scalable fault-tolerant streaming applications. You will learn about DStreams and various Transformations performed on the streaming data. You will get to know about commonly used streaming operators such as Sliding Window Operators and Stateful Operators.

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

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Apache Spark Streaming - Data Sources

Learning Objectives: In this module, you will learn about the different streaming data sources such as Kafka and flume. At the end of the module, you will be able to create a spark streaming application.

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

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Implementing an End-to-End Project

Project 1-

Statement:

Project 2-

Statement:

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Spark GraphX (Self-Paced)

Learning Objective: In this module, you will be learning the key concepts of Spark GraphX programming concepts and operations along with different GraphX algorithms and their implementations.

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

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Python Spark Course Description

About the PySpark Online Course

PySpark Certification Training Course is designed to provide you with the knowledge and skills to become a successful Big Data & Spark Developer. This Training would help you to clear the CCA Spark and Hadoop Developer (CCA175) Examination.

You will understand the basics of Big Data and Hadoop. You will learn how Spark enables in-memory data processing and runs much faster than Hadoop MapReduce. You will also learn about RDDs, Spark SQL for structured processing, different APIs offered by Spark such as Spark Streaming, Spark MLlib. This course is an integral part of a Big Data Developer’s Career path. It will also encompass the fundamental concepts such as data capturing using Flume, data loading using Sqoop, messaging system like Kafka, etc.

What are the objectives of our Online PySpark Training Course?

Overview of Big Data & Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator)
Comprehensive knowledge of various tools that falls in Spark Ecosystem like Spark SQL, Spark MlLib, Sqoop, Kafka, Flume and Spark Streaming
The capability to ingest data in HDFS using Sqoop & Flume, and analyze those large datasets stored in the HDFS
The power of handling real time data feeds through a publish-subscribe messaging system like Kafka
The exposure to many real-life industry-based projects which will be executed using Edureka’s CloudLab
Projects which are diverse in nature covering banking, telecommunication, social media, and govenment domains
Rigorous involvement of a SME throughout the Spark Training to learn industry standards and best practices

Why should you go for Online Spark Training?

Spark is one of the most growing and widely used tool for Big Data & Analytics. It has been adopted by multiple companies falling into various domains around the globe and therefore, offers promising career opportunities. In order to take part in these kind of opportunities, you need a structured training that is aligned as per Cloudera Hadoop and Spark Developer Certification (CCA175) and current industry requirements and best practices.

Besides strong theoretical understanding, it is quite essential to have a strong hands-on experience. Hence, during the Edureka’s PySpark course, you will be working on various industry-based use-cases and projects incorporating big data and spark tools as a part of solution strategy.

Additionally, all your doubts will be addressed by the industry professional, currently working on real-life big data and analytics projects.

What are the skills that you will be learning with our PySpark Certification Training?

Edureka’s PySpark Training is curated by Industry experts and helps you to become a Spark developer. During this course, you will be trained by Industry practitioners having multiple years of experience in the same domain. During Apache Spark and Scala course, you will be trained by our expert instructors to:

Master the concepts of HDFS
Understand Hadoop 2.x Architecture
Learn data loading techniques using Sqoop
Understand Spark and its Ecosystem
Implement Spark operations on Spark Shell
Understand the role of Spark RDD
Work with RDD in Spark
Implement Spark applications on YARN (Hadoop)
Implement machine learning algorithms like clustering using Spark MLlib API
Understand Spark SQL and it’s architecture
Understand messaging system like Kafka and its components
Integrate Kafka with real time streaming systems like Flume
Use Kafka to produce and consume messages from various sources including real time streaming sources like Twitter
Learn Spark Streaming
Use Spark Streaming for stream processing of live data
Solve multiple real-life industry-based use-cases which will be executed using Edureka’s CloudLab

Who should go for our PySpark Training Course?

Market for Big Data Analytics is growing tremendously across the world and such strong growth pattern followed by market demand is a great opportunity for all IT Professionals. Here are a few Professional IT groups, who are continuously enjoying the benefits and perks of moving into Big Data domain.

Developers and Architects
BI /ETL/DW Professionals
Senior IT Professionals
Mainframe Professionals
Freshers
Big Data Architects, Engineers and Developers
Data Scientists and Analytics Professionals

How will PySpark Online Training help your career?

The stats provided below will provide you a glimpse of growing popularity and adoption rate of Big Data tools like Spark in the current as well as upcoming years:

56% of Enterprises Will Increase Their Investment in Big Data over the Next Three Years – Forbes
McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts
Average Salary of Spark Developers is $113k
According to a McKinsey report, US alone will deal with shortage of nearly 190,000 data scientists and 1.5 million data analysts and Big Data managers by 2018

As you know, nowadays, many organizations are showing interest in Big Data and are adopting Spark as a part of solution strategy, the demand of jobs in Big Data and Spark is rising rapidly. So, it is high time to pursue your career in the field of Big Data & Analytics with our PySpark Certification Training Course.

What are the pre-requisites for Edureka's PySpark Online Training Course?

There are no such prerequisites for Edureka’s PySpark Training Course. However, prior knowledge of Python Programming and SQL will be helpful but is not at all mandatory.

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Spark Developer Using Python Certification

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Reviews

5000 Total number of reviews

4.57 Aggregate review score

80% Course completion rate

Testimonials
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Sivanand Software Engineer

Flexibility and Readyness to serve , Content Quality ,Content availability

Pritam Student

Everything about this training was excellent. No complaints. I would recommend this course to others.

Gopakumar

here are many online trainings available and believe meâ¦Its not quite easy to choose as everyone promises the best. After careful reviews and attending demos, I picked â Edureka and I realized that my decision was correct in every aspect. I attended 2 courses - Big Data Hadoop and PySpark. The trainer dived deep into concepts in Hadoop, like pig, hive, Sqoop, NoSQL, and Map Reduce. When comes PySpark too, the trainer explained in detail - Spark streaming, Spark SQL, Spark RDD, Spark Machine Learning libraries (Spark MLlib) and Phython.

Anil Algole Principle Consultant at Infosys

Experience with Edureka is world class. I took 2 courses Informatica PowerCenter 9.x and Tableau Certification Training. I feel both the courses had extremely knowledgeable instructors, professional course delivery, recorded classes, great customer support and the access to needed tools at the comfort of your time and place (home). I have not seen such a value for money for anywhere in the world. I will keep taking many many courses with Edureka! Thank you for your help I am able to increase my billing rate and subject level expertise."

Rajendran Gunasekar Offshore Delivery Manager at CSC, Chennai, India

Knowledgeable Presenters, Professional Materials, Excellent Customer Support what else can a person ask for when acquiring a new skill or knowledge to enhance their career. Edureka true to its name is the place to gather garner and garden the knowledge for all around the globe. My Best wishes to Edureka's team for their upcoming bright future in E-Learning sector."

Abhishek Mishra Experience in Hadoop and Big Data Analytic, Java developer.

Awesome faculty. Awesome explanation on topics. I really appreciate Edureka Support team. They are really doing a fantastic job. All my queries were answered in no time. I also approached your support team for a change in class and the instructor. They allowed me to choose instructor of my choice and now my learning is much at its peak. Thanks to one and all sitting out there in the support team. They even provide a prompt response during weekends. That's a good feature. We professionals get time only during the weekend to focus on studies and when we do so we get stuck into problems; but no way have we had a good Edureka support Team. Thanks once again.

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Python Spark Training Projects

How will I execute the practicals in this PySpark Certification Training?

You will execute all your PySpark Course Assignments/Case Studies in the Cloud LAB environment provided by Edureka. You will be accessing the Cloud LAB via a browser. In case of any doubt, Edureka’s Support Team will be available 24*7 for prompt assistance.

What is CloudLab?

CloudLab is a cloud-based Spark and Hadoop environment that Edureka offers with the PySpark Training Course where you can execute all the in-class demos and work on real life spark case studies fluently. This will not only save you from the trouble of installing and maintaining Spark and Python on a virtual machine, but will also provide you an experience of a real big data and spark production cluster. You’ll be able to access the Spark Training CloudLab via your browser which requires minimal hardware configuration. In case, you get stuck in any step, our support team is ready to assist 24×7.

What are the system requirements PySpark Training Course?

You don’t have to worry about the system requirements as you will be executing your practicals on a Cloud LAB which is a pre-configured environment. This environment already contains all the necessary tools and services required for Edureka's PySpark Training.

Which projects and case studies will be a part this Edureka's PySpark Online Training Course?

At the end of the PySpark Training, you will be assigned with real-life use-cases as certification projects to further hone your skills and prepare you for the various Spark Developer Roles. Following are few industry-specific case studies that are included in our Apache Spark Developer Certification Training.

Project 1-

Statement:

Project 2-

Business challenge/requirement:

Considerations:

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Python Spark Training Features

Instructor-led Sessions

36 Hours of Online Live Instructor-Led Classes. Weekend Class : 12 sessions of 3 hours each.
Weekday Class: 18 sessions of 2 hours each.

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various PySpark concepts.

Lifetime Access

You get lifetime access to LMS where presentations, quizzes, installation guide & class recordings are there.

24 x 7 Expert Support

We have lifetime 24x7 online support team to resolve all your technical queries, through ticket based tracking system.

Certification

Sucessfully complete your final course project and Edureka will certify you as a Apache Spark and Scala Developer Using Python.

Forum

We have a community forum for all our learners that facilitates learning through peer interaction and knowledge sharing.

Python Spark Training FAQ's

What if I miss a class?

"You will never miss a lecture at Edureka! You can choose either of the two options:

View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch."

What if I have queries after I complete this course?

Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.

Will I get placement assistance?

To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.

Is the course material accessible to the students even after the course training is over?

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

Can I attend a demo session before enrollment?

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight into how are the classes conducted, 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 minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.

What is PySpark?

Apache Spark is an open-source real-time in-memory cluster processing framework. It is used in streaming analytics systems such as bank fraud detection system, recommendation system, etc. Whereas Python is a general-purpose, high-level programming language. It has a wide-range of libraries which supports diverse types of applications. PySpark is a combination of Python and Spark. It provides Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.

What if I have more queries?

You can give us a CALL at +91 88808 62004/1800 275 9730 (US Tollfree Number) OR email at sales@edureka.co

What is RDD in PySpark?

RDD stands for Resilient Distributed Dataset which is the building block of Apache Spark. RDD is fundamental data structure of Apache Spark which is an immutable distributed collection of objects. Each dataset in RDD is divided into logical partitions, which may be computed on different nodes of the cluster.

Is PySpark a language?

PySpark is not a language. PySpark is Python API for Apache Spark using which Python developers can leverage the power of Apache Spark and create in-memory processing applications. PySpark is developed to cater the huge amount of Python community.

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