Pyspark Training| Pyspark Certification| Pyspark Course- Prloginfo

Course Overview

Prologinfo offers a Pyspark course that helps in making professional skills that are essential for becoming a successful Spark developer by using Python. The Pyspark certification course is designed for providing the basic knowledge and technical skills that help in clearing the certification exam. During the Pyspark Training Online, you will learn about the fundamentals of Spark and Big Data Hadoop, how to build several APIs that work with Spark Data Frame, how to run Python scripts and explore Python Editors and IDEs, the importance of Pyspark, how to transform and load data through several sources, and many more. You will also improve your technical skills by analyzing case studies and performing real-time projects throughout the online training. We also provide assignments that are based on this Pyspark course and are beneficial in understanding the basic knowledge of Pyspark. Our training also offers course materials for self-preparation for the certification exam after completing this online course. When you will get Pyspark Certification, you will be an expert in all the concepts of Pyspark.

Pyspark tutorial Key Features

Installation and Configuration of PySpark
In-depth knowledge of PySpark documentation
Get PySpark documentation
Provide you with crucial PySpark interview questions
PySpark Job assistance
Guidance in building a good PySpark resume
Schedule your timings according to your convenience
One on One sessions

Who should learn PySpark Course

This course primarily benefits big data architects, engineers, developers, data scientists, and analytics professionals who either want to upskill or shift to the PySpark domain. Fresher’s who want to pursue a career in PySpark can also opt. Professionals are seeking PySpark certification to advance their careers.

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Course curriculum / Syllabus

Introduction to Big Data Hadoop and Spark:

What is Big Data?
Big Data Customer Scenarios
Use Uber Use Case to resolve the limitations of Existing Data Analytics Architecture
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
Perform Big Data Analytics with the help of Batch and Real-Time Processing
Why Spark is needed?
What is Spark?
How Spark Differs from its Competitors?

Introduction to Python for Apache Spark

Overview of Python
Different Applications where Python is used
Values, Types, Variables
Operands and Expressions
Conditional Statements
Using different types of Loops
Command Line Arguments
Writing to the Screen
Python files I/O Functions
Working with Numbers
Strings and related operations
Tuples and related operations
Lists and related operations
Dictionaries and related operations
Sets and related operations

Functions, OOPs, and Modules in Python

How to use Functions?
Types of Function Parameters
Concept of Global Variables
Variable Scope and Returning Values
What are Lambda Functions?
Object-Oriented Concepts
Using Standard Libraries
Modules Used in Python
The Import Statements
Module Search Path
Package Installation Ways

Deep Dive into Apache Spark Framework

Spark Components & its Architecture
Spark Deployment Modes
Introduction to PySpark Shell
Submitting PySpark Job
Spark Web UI
Data Ingestion using Sqoop

Playing with Spark RDDs

Concept of RDD (Resilient Distributed Dataset) and its Transformations, Operations, and 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 and Achieve Parallelization
Passing Functions to Spark

DataFrames and Spark SQL

What is Spark SQL?
Spark SQL Architecture
SQL Context in Spark SQL
Schema RDDs
User-Defined Functions
Data Frames and Datasets
Interoperating with RDDs
JSON and Parquet File Formats
Loading Data through Different Sources
Spark-Hive Integration

pyspark training FAQ’s:

1.What is PySpark?

PySpark is Python API to support Apache Spark. Apache Spark is distributed framework to deal with extensive data analysis. Spark is a written scala that can be integrated with Python. Spark is a computational engine that works on vast sets of data by processing them.

2.How do I get PySpark certification?

We provide you with PySpark certification upon completing the course successfully. Many leading organizations recognize our certificate. It will help you gain credibility among the companies while hiring.

3.What if I miss the class?

We will provide you with the recording of the session and also eLearning material for self-study.

4.Can I attend the demo class?

Yes, you can attend the demo class to a better picture and decide on a continuation with us.

5.Do I get Job placement?

Yes, we provide job placement if you’re residing in the US.

6.Who are the trainers of the course?

We have industry-certified expert trainers. They are experts in using the suite, and you will learn everything under their guidance.