Python Spark using PySpark Certification Training

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    • Learn and get certified in Python Spark using PySpark from the comfort of your home and transform your career

    • Preferred language for new technologies such as Data Science and Machine Learning

    • Become a master in Python Spark using PySpark programming concepts such as Spark RDD, Spark SQL, Spark MLlib, Spark Streaming, HDFS, Sqoop, Flume, Spark GraphX and Messaging System such as Kafka

    • We provide great learning experience at lowest price in the industry

    • Exclusive Buy 1 - Get 1 Free Offer (till 31st Dec, 2020) Know More

Live-Virtual Class
  • Attend Python Spark using PySpark Certification training from the comfort of your home with hands-on practice with Cloud Lab
  • Taught using real life case studies and live project based on any of the selected use cases
  • In case you miss a session, you can either attend the missed session in any other live batch or view the recorded session in the LMS
  • 24x7 support for lifetime through ticket based support system
  • 36 Hours (12 sessions of 3 hours each) of Live Classes taught by experts over online training platform
  • Lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are available
  • Access to community forums for peer interaction and knowledge sharing
  • Exclusive Buy 1 - Get 1 Free Offer (till 31st Dec, 2020) Know More
Date : Dec 25 Jan 30 ( Weekend)
USD 649 429
Dec 25 Fri Dec 26 Sat Jan 01 Fri Jan 02 Sat Jan 08 Fri Jan 09 Sat Jan 15 Fri Jan 16 Sat Jan 22 Fri Jan 23 Sat Jan 29 Fri Jan 30 Sat

Description

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.

 

In this course, 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.

 

Course Objectives:

  • 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 our CloudLab

Spark is one of the most growing and widely used tools 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 kinds 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 this PySpark course, you will be working on various industry-based use-cases and projects incorporating big data and spark tools as a part of the solution strategy.

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

PySpark Training is curated by Industry experts and helps you to become a Spark developer. During this course, you will be trained by our expert instructors on:

 

  • Basics of Python programming and different types of sequence structures, related operations and their usage.
  • Create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.
  • Understand Apache Spark in depth and you will be learning about various Spark components, creating and running various spark applications. At the end, you will learn how to perform data ingestion using Sqoop.
  • Learn about Spark - RDDs and other RDD related manipulations for implementing business logics (Transformations, Actions, and Functions performed on RDD).
  • Learn about SparkSQL which is used to process structured data with SQL queries. 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.
  • Learn about why machine learning is needed, different Machine Learning techniques/algorithms and their implementation using Spark MLlib.
  • Implementing various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and many more.
  • Understand Kafka and Kafka Architecture. You will go through the details of Kafka Cluster and you will also learn how to configure different types of Kafka Cluster.
  • Work on Spark streaming which is used to build scalable fault-tolerant streaming applications. Learn about DStreams and various Transformations performed on the streaming data.
  • Learn about the different streaming data sources such as Kafka and flume. Create a spark streaming application.
  • Learning the key concepts of Spark GraphX programming concepts and operations along with different GraphX algorithms and their implementations.

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

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.

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.

  • 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

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

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Course 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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)
  • 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
  • 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
  • Apache Spark Streaming: Data Sources
  • Streaming Data Source Overview
  • Apache Flume and Apache Kafka Data Sources
  • Example: Using a Kafka Direct Data Source
  • Project 1- Domain: Finance
  • Statement: 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 this underserved population has a positive loan experience, it makes use of a variety of alternative data including telco and transactional information to predict their client's repayment abilities. The bank has asked you to develop a solution to ensure that clients capable of repayment are not rejected and that loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful.


     
  • Project 2- Domain: Media and Entertainment 
  • Statement: Analyze and deduce the best performing movies based on the customer feedback and review. Use two different API's (Spark RDD and Spark DataFrame) on datasets to find the best ranking movies.

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

Certification & Exam

Towards the end of the course, you will be working on a project. You will be certified as a Python Spark using PySpark Programmer based on the project. Once you  successfully submit your Python Spark using PySpark certification project, it will be reviewed by the expert panel. After a successful evaluation, you will be awarded Python Spark using PySpark Expert certificate.

Why Choose Us

Most Popular

More than 7000 satisfied learners have taken this course to get certified as Python Spark using PySpark expert.

Convenient Schedule

We have batches both on weekends and weekdays to accommodate the need of different professionals.

Low Cost

Without compromising on quality, we have priced our Python Spark using PySpark training courses very competitively. We guarantee that you will find us more economical than other training providers.

Unmatched Quality

We along with our affiliate partners are dedicated in creating the best quality study materials and student experience across our products. All content complies with quality conformance standards to ensure that our content is the best in class and free of any errors.

Course Design

Based on years of experience in delivering effective professional training, our courses are designed not only to provide you the Python Spark using PySpark certification, but also to empower with best practices. We achieve this by providing a unique blend of concepts, case studies, and simulations that guarantee our students know how to implement Python Spark using PySpark in their organizations.

Trainers

All our trainers are highly qualified and certified in various industry frameworks. On an average, they have 10+ years of professional experience in their respective fields. Our trainers are not only experts in their domains but are also passionate about sharing their knowledge and expertise with other professionals thereby enriching careers of students.

Never miss a class

In case you miss a session because of any reason, you can either attend the missed session in any other live batch or view the recorded session in the LMS.

Lifetime Access

You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.

Cloud Lab

Cloud Lab has been provided to ensure you get real-time hands-on experience to practice your new skills on a pre-configured environment.

Customer Satisfaction

We are here to ensure you get heard 24/7, and to take care of every single questions and doubts you have. Our dedicated support team will provide you best in class round the clock customer support. Superior customer service is the hallmark of our company and we always go the extra mile to satisfy each of our customers whether an individual or a corporate client.

FREQUENTLY ASKED QUESTIONS


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