Python Spark Certification Training

Categories
Big Data
Read Review
5.0 (1125 satisfied learners)

This PySpark course is created to help you master the skills required to become a successful Spark developer using Python.

Course Description

The course is designed to provide you with the knowledge and skills to become a successful Big Data & Spark Developer. You will learn how Spark enables in-memory data processing and runs much faster than Hadoop MapReduce, RDDs, Spark SQL for structured processing, different APIs offered by Spark such as Spark Streaming Spark MLlib.

PySpark is the alliance of Apache Spark and Python. Apache Spark is a framework created around quickness, effortless use, and streaming analytics, whereas Python is a general-purpose programming language.

Developers and Architects BI /ETL/DW Professionals Senior IT Professionals Mainframe Professionals Freshers Big Data Architects, Engineers, and Developers Data Scientists and Analytics Professionals.

PySpark is an interface for Apache Spark in Python. It allows you to write Spark applications using Python APIs and delivers the PySpark shell for interactively examining data in a dispersed environment.

The prerequisite for this course is: knowledge of Python Programming and SQL

Apache Spark is an open-source, spread processing system utilized for big data workloads. It uses in-memory caching and optimized query execution for quick queries against data of any size.

Spark is used in the world's top organization, and it is considered the third generation of a big data world. So, the knowledge of Spark unlocks new career opportunities.

The PySpark framework processes enormous amounts of data much quicker than other established frameworks. Python is well-suited for dealing with RDDs as it is dynamically typed.

What you'll learn

  • In this course, you will learn: data processing Apache Kafka Apache Flume Spark MLlib DataFrames and Spark SQL, and more.

Requirements

  • Basic knowledge about the programming language as well as the framework basics of Apache Spark as well as Python.

Curriculam

Discover 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.

What is Big Data?
Big Data Customer Scenarios
Restrictions and Resolutions 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 along with Batch & Real-Time Processing
Why is Spark Needed?
What is Spark?
How Spark Differs from its Competitors?
Spark at eBay
Spark's Place in Hadoop Ecosystem

knows the basics of Python programming and learns different types of sequence structures, related operations, and their usage.

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
Dictionaries and related operations
Creating "Hello World" code
Demonstrating Conditional Statements
Demonstrating Loops
Tuple - properties, associated processes, compared with the list
List - properties, related operations
Dictionary - properties, related operations
Set - properties, related operations

earn how to create generic python scripts, address errors/exceptions in code, and finally extract/filter content using regex.

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
Functions
Lambda
Sorting
Errors and Exceptions
Packages and Module

understand Apache Spark and various Spark components, create and run multiple spark applications.

Spark Components & its Architecture
Spark Deployment Modes
Introduction to PySpark Shell
Submitting PySpark Job
Spark Web UI
Writing PySpark Job Using Jupyter Notebook
Data Ingestion using Sqoop
Building and Running Spark Application
Spark Application Web UI
Understanding different Spark Properties

learn about Spark RDDs and further RDD-related manipulations for implementing business logic.

Challenges in Existing Computing Methods
Possible Solution & How RDD Solves the Problem
RDD, Its Functions, Transformations & Activities
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 Accomplishing Parallelization
Passing Functions to Spark
Loading data in RDDs
Saving data through RDDs
RDD Transformations
RDD Actions and Functions
RDD Partitions
WordCount through RDDs

learn about SparkSQL, data-frames, and datasets in Spark SQL, and different kinds of SQL operations performed on the data-frames.

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
Spark SQL – Creating data frames
Loading and transforming data through different sources
Stock Market Analysis
Spark-Hive Integration

learn why machine learning is needed, different Machine Learning techniques/algorithms, and their implementation using Spark MLlib.

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

Discover executing different algorithms backed by MLlib such as Linear Regression, Decision Tree, Random Forest, etc.

Supervised Learning
Decision Tree, Random Forest
K-Means Clustering & it's working with MLlib
Analysis of Election Data using MLlib (K-Means)
K- Means Clustering
Linear Regression
Logistic Regression
Decision Tree
Random Forest

understand Kafka and Kafka Architecture, Kafka Cluster, different types of Kafka Cluster, Apache Flume, etc.

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
Configuring Single Node Single Broker Cluster
Configuring Single Node Multi-Broker Cluster
Creating and using messages through Kafka Java API
Flume Commands
Setting up Flume Agent
Streaming Twitter Data into HDFS

Learn to operate Spark streaming which is utilized to create scalable fault-tolerant streaming applications.

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
Windowed Operators and its uses
Important Windowed Operators
Slice, Window, and ReduceByWindow Operators
Stateful Operators
WordCount Program using Spark Streaming

understand various streaming data sources such as Kafka and flume, create a spark streaming application.

Apache Spark Streaming: Data Sources
Streaming Data Source Overview
Apache Flume and Apache Kafka Data Sources
Example: Using a Kafka Direct Data Source
Various Spark Streaming Data Sources

Statement: A bank is attempting to widen the financial inclusion for the unbanked population by delivering a joyful and secure borrowing experience. To ensure this underserved population has a favourable loan experience, it uses various 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 accepted and that loans are given with a principal, maturity, and repayment calendar to empower their clients to succeed.

Statement: Analyze and deduce the best-performing movies based on customer feedback and review. Use two different APIs (Spark RDD and Spark DataFrame) on datasets to find the best ranking movies.

Discover Spark GraphX programming concepts and operations' fundamental concepts and different GraphX algorithms and their implementations.

Introduction to Spark GraphX
Information about a Graph
GraphX Basic APIs and Operations
Spark GraphX Algorithm
The Traveling Salesman problem
Minimum Spanning Trees

FAQ

On average, a python Spark developer earns $155,000 annually.

To better understand Python Spark, one must learn as per the curriculum.

An Apache Spark developer's responsibilities include creating Spark jobs for data aggregation and transformation, building unit tests for Spark helper and transformations methods, using all code writing Scaladoc-style documentation, And designing data processing pipelines.

Big Data technologies are in demand as spark processing is faster than Hadoop processing. So indeed, there is tremendous scope in pyspark as companies are hiring prospects for pyspark even if they do not have any Hadoop knowledge.

The PySpark framework processes enormous amounts of data faster than other conventional frameworks. Python is good for dealing with RDDs as it is dynamically typed.

CertZip Support Unit is available 24/7 to help with your queries during and after completing Python Spark Certification Training using PySpark.

You will receive CertZip Python Spark Training using PySpark on completing live online instructor-led classes. After completing the Python Spark Training using the PySpark module, you will receive the certificate.

By enrolling in the Python Spark Training using PySpark Course and completing the module, you can get CertZip Python Spark Training using PySpark Certification.

Yes, Access to the course material will be available for a lifetime once you have enrolled in the CertZip Python Spark Training using the PySpark Course.

product-2.jpg
$427 $449
$22 Off
ADD TO CART

Training Course Features

Assessments
Assessments

Every certification training session is followed by a quiz to assess your course learning.

Mock Tests
Mock Tests

The Mock Tests Are Arranged To Help You Prepare For The Certification Examination.

Lifetime Access
Lifetime Access

A lifetime access to LMS is provided where presentations, quizzes, installation guides & class recordings are available.

24x7 Expert Support
24x7 Expert Support

A 24x7 online support team is available to resolve all your technical queries, through a ticket-based tracking system.

Forum
Forum

For our learners, we have a community forum that further facilitates learning through peer interaction and knowledge sharing.

Certification
Certification

Successfully complete your final course project and CertZip will provide you with a completion certification.

Python Spark Certification Training

A Python Spark Training using PySpark is a certification that verifies that the holder has the knowledge and skills required to work with Pyspark Programming.

demo certificate

Reviews

H Hera C
A Alex
J James

Related Courses

Discover your perfect program in our courses.