Python Spark Certification Training using PySpark

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Certs Learning
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Big Data

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?

    Spark Certification Training is designed by industry experts to make you a Certified Spark Developer. The PySpark Course offers:

  • 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  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?

Certs Learning’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

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 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.
1
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
2
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
3
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
4
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
5
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
6
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
7
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
8
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
9
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


10
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
11
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
12
Implementing an End-to-End Project

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 clients' 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.

 

13
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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All the instructors at Certs Learning are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by Certs Learning for providing an awesome learning experience to the participants.
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.
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.
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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Lectures: 13