Python Spark using PySpark Certification Training

Python Spark using PySpark Certification Training in Colorado Springs, CO

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

Python Spark using PySpark Course Overview

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

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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.

Python Spark using PySpark 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

Why choose our Python Spark using PySpark in Colorado Springs, CO?

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

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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 experts 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.

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Python Spark using PySpark Frequently Asked Questions

Yes, we do offer additional discounts to group and corporate training customers. Please email us at support@encertify.com to find out more about our group discount offerings.

The orientation class is a preparatory session which gives a basic overview of the course and also guides the learners about any software/license installation required for the course. This will prepare you for the actual class, which will start the next week. Attending the orientation class is optional.

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We do not offer placement or placement assistance services at this time. However, our training is designed to equip you with in-demand skills, hands-on experience, and certification readiness to help you confidently pursue new career opportunities. Many of our learners have successfully transitioned into new roles or advanced in their careers based on the knowledge and certifications gained through our programs