PySpark Certification Training Course

PySpark Certification Training Course by the Teaching Krow is strategically curated by the top industry professionals to cater to the needs of the current market situations and evolve with the market trends. This training course is designed to help you master the essential skills required to become a successful Spark developer using Python. The training is immersive and offers you the environment to learn and interact with your trainers and peers to grow to become an excellent certified PySpark professional by clearing the exams on your first attempt.

Why should you take PySpark Certification Training Course ?

Promotes faster development and processing and attracts higher paying packages.

Offers you to select the best company from the pool of opportunities available to you.

Earn a globally recognized certificate and learn from the experts.


24 hours of instructor-led training
22 hours of self-paced videos
One year access
Projects and exercises
Mentor support

PySpark Certification Training Course Overview

What Will You Learn In The PySpark Certification Training Course by Teaching Krow?
  • Familiarize yourself with Apache Spark, along with its application and Spark 2.0 architecture
  • Gain hands-on experience with multiple tools in the Spark Ecosystem, including Spark SQL, Kafka, Flume, Spark MLlib, and Spark Streaming
  • Understand the architecture of lazy evaluation and RDD.
  • Learn how to change the architecture of the Data Frame effectively and how to interact with it using Spark SQL
  • Build multiple APIs that effectively work with Spark DataFrame
  • Nurture your skills to filter, aggregate, sort, and transform data leveraging the Spark DataFrame


Self Paced Training


PySpark Certification Training Course Curriculum

Introduction To Big Data Hadoop and Spark
  • What is Big Data?
  • Big Data Customer Scenarios
  • Downsides and Solutions of Existing Data Analytics Architecture along with Uber Use Case
  • What is Hadoop?
  • How does Hadoop Effortlessly Solve the Big Data Problem?
  • Hadoop’s Primary Characteristics
  • Hadoop Primary Components
  • Hadoop Ecosystem and HDFS
  • YARN and its Advantage
  • Rack Awareness and Block Replication
  • Hadoop Cluster and Architecture
  • Different Cluster Modes of Hadoop
  • Big Data Analytics with Batch & Real-Time Processing
  • Why is Spark Needed?
  • What is Spark?
  • How Does Spark Differ from its Competitors?
  • Spark at eBay
  • Spark’s Place in Hadoop Ecosystem
  • Overview of Python
  • Various Applications where Python is Used
  • Values, Types, and Variables
  • Operands and Expressions
  • Conditional Statements
  • Writing to the Screen
  • Loops
  • Command Line Arguments
  • Python files I/O Functions
  • Numbers
  • Tuples and related operations
  • Dictionaries and related operations
  • Strings and related operations
  • Lists and related operations
  • Sets and related operations
  • Functions
  • Global Variables
  • Function Parameters
  • Variable Scope and Returning Values
  • Object-Oriented Concepts
  • Modules Used in Python
  • Lambda Functions
  • Module Search Path
  • Standard Libraries
  • The Import Statements
  • Package Installation Ways
  • Spark Components & its Architecture
  • Introduction to PySpark Shell
  • Spark Deployment Modes
  • Writing your PySpark Job Using Jupyter Notebook
  • Submitting Spark Job
  • Spark Web UI
  • Data Ingestion using Sqoop
  • Challenges in Current Computing Methods
  • Probable Solution & How RDD Solves the Problem
  • Data Loading & Saving Through RDDs
  • What is RDD, Its Operations, Transformations and Actions
  • Key-Value Pair RDDs
  • RDD Lineage
  • Other Pair RDDs, Two Pair RDDs
  • RDD Persistence
  • Passing Functions to Spark
  • WordCount Program Using RDD Concepts
    • RDD Partitioning & How it Helps Achieve Parallelization
  • Need for Spark SQL
  • What is Spark SQL?
  • Spark SQL Architecture
  • SQL Context in Spark SQL
  • Schema RDDs
  • Data Frames & Datasets
  • User Defined Functions
  • Interoperating with RDDs
  • JSON & Parquet File Formats
  • Spark-Hive Integration
  • Loading Data through Different Sources
  • Why is Machine Learning Important?
  • What is Machine Learning?
  • Where is Machine Learning used?
  • Face Detection: USE CASE
  • Various Types of Machine Learning Techniques
  • Introduction to MLlib
  • Features of MLlib & MLlib Tools
  • Various ML algorithms supported by MLlib
  • Need for Kafka
  • What is Kafka?
  • Core Concepts of Kafka
  • Where is Kafka Used?
  • Kafka Architecture
  • Configuring Kafka Cluster
  • Understanding the Components of Kafka Cluster
  • Kafka Producer & Consumer Java API
  • What is Apache Flume?
  • Need of Apache Flume
  • Basic Flume Architecture
  • Flume Channels
  • Flume Sources
  • Flume Sinks
  • Flume Configuration
  • Integrating Apache Flume & Apache Kafka
  • Drawbacks in Existing Computing Methods
  • Why is Streaming Necessary?
  • What is Spark Streaming?
  • Spark Streaming’s Key Features
  • Spark Streaming Workflow
  • How Does Uber Use Streaming Data?
  • Transformations on DStreams
  • Streaming Context & DStreams
  • Important Windowed Operators
  • Describe Windowed Operators & Why it is Useful?
  • Slice, Window & ReduceByWindow Operators
  • Stateful Operators
  • Apache Spark Streaming and its various Data Sources
  • Streaming Data Source Overview
  • Apache Flume & Apache Kafka Data Sources
  • Examples of Using a Kafka Direct Data Source
  • Supervised learning
  • Unsupervised learning
  • Analysis of the US election data

PySpark Certification Training Course Projects

Into Financial Domain

Certificate For PySpark Certification Training Course

The training will help clear the PySpark Certification Training Course Exam. The complete training course content is aligned with these certification programs and helps you quickly clear these certification exams and get the best jobs in the top companies. As part of the training, you will be working on real-time assignments and projects with practical implications in the real-world industry, helping you fast-track your career. Multiple quizzes at the end of this training program will perfectly reflect the questions in the actual certification exams and help you score better.

CERTIFICATE FOR PySpark Certification Training Course
Your Name
PySpark Certification Training Course
Issued By
Certificate ID __________
Date __________

Frequently Asked Questions on PySpark Certification Training Course

Is PySpark a Language?

No, PySpark is not a programming language. Instead, it is a Python API for Apache Spark, using which the developers can leverage the optimum power of Apache Spark to create an in-memory processing application.

The PySpark Certification Training Course is designed to help you become a certified Spark Developer. The PySpark course offers:

  • Overview of Hadoop and Big Data, including HDFS and Yarn
  • Comprehensive knowledge of multiple tools that are a part of Spark Ecosystem like Spark Mlilb, Spark SQL, Kafka, Flume, Sqoop, and Spark Streaming
  • Develop the capability to ingest data in HDFS using Scoop and Flume and successfully analyze large datasets stored in the HDFS
  • Develop the power of effectively handling real-time data feeds through publish-subscribe messaging systems like Kafka.
  • Get exposure to various real-time industry-based projects. 
  • Rigorous involvement of small and medium-scale businesses throughout the training.

During PySpark Certification Training, you’ll be trained by industry experts with decades of experience in the same domain. During the course, you’ll be trained by the experts to:

  • Master the critical concepts of HDFS
  • Learn data loading techniques using Sqoop
  • Understand Hadoop 2.x Architecture
  • Understand Spark & its Ecosystem
  • Understand the role of Spark RDD
  • Implement Spark operations on Spark Shell
  • 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 its architecture
  • Understand messaging systems like Kafka and its components
  • Integrate Kafka with real-time streaming systems like Flume
  • Use Kafka to produce & 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 Teaching Krow’s CloudLab
  • Developers and Architects
  • Senior IT Professionals
  • BI /ETL/DW Professionals
  • Mainframe Professionals
  • Freshers
  • Big Data Architects, Developers and Engineers 
  • Data Scientists and Analytics Professionals

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

  • Self-paced training
  • Online Classroom
  • Corporate training
  • Instructor-led training

Yes, you can.

With Teaching Krow, you'll never miss a class. You'll have a recording even if you have missed a live class. Furthermore, you can also attend the same lecture in the next batch.

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

We have a 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 the classes are conducted, quality of instructors and the level of interaction in a class.