• 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

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 NVidya’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 NVidya’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 NVidya, 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 NVidya are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by NVidya 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.