PySpark AWS- Master Big Data With PySpark and AWS

PySpark & AWS: Master Big Data With PySpark and AWS, available at $84.99, has an average rating of 4.51, with 207 lectures, 12 quizzes, based on 2184 reviews, and has 15398 subscribers.

You will learn about ● The introduction and importance of Big Data. ● Practical explanation and live coding with PySpark. ● Spark applications ● Spark EcoSystem ● Spark Architecture ● Hadoop EcoSystem ● Hadoop Architecture ● PySpark RDDs ● PySpark RDD transformations ● PySpark RDD actions ● PySpark DataFrames ● PySpark DataFrames transformations ● PySpark DataFrames actions ● Collaborative filtering in PySpark ● Spark Streaming ● ETL Pipeline ● CDC and Replication on Going This course is ideal for individuals who are ● People who are beginners and know absolutely nothing about PySpark and AWS. or ● People who want to develop intelligent solutions. or ● People who want to learn PySpark and AWS. or ● People who love to learn the theoretical concepts first before implementing them using Python. or ● People who want to learn PySpark along with its implementation in realistic projects. or ● Big Data Scientists. or ● Big Data Engineers. It is particularly useful for ● People who are beginners and know absolutely nothing about PySpark and AWS. or ● People who want to develop intelligent solutions. or ● People who want to learn PySpark and AWS. or ● People who love to learn the theoretical concepts first before implementing them using Python. or ● People who want to learn PySpark along with its implementation in realistic projects. or ● Big Data Scientists. or ● Big Data Engineers.

Enroll now: PySpark & AWS: Master Big Data With PySpark and AWS

Summary

Title: PySpark & AWS: Master Big Data With PySpark and AWS

Price: $84.99

Average Rating: 4.51

Number of Lectures: 207

Number of Quizzes: 12

Number of Published Lectures: 190

Number of Published Quizzes: 12

Number of Curriculum Items: 219

Number of Published Curriculum Objects: 202

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

● The introduction and importance of Big Data.

● Practical explanation and live coding with PySpark.

● Spark applications

● Spark EcoSystem

● Spark Architecture

● Hadoop EcoSystem

● Hadoop Architecture

● PySpark RDDs

● PySpark RDD transformations

● PySpark RDD actions

● PySpark DataFrames

● PySpark DataFrames transformations

● PySpark DataFrames actions

● Collaborative filtering in PySpark

● Spark Streaming

● ETL Pipeline

● CDC and Replication on Going

Who Should Attend

● People who are beginners and know absolutely nothing about PySpark and AWS.

● People who want to develop intelligent solutions.

● People who want to learn PySpark and AWS.

● People who love to learn the theoretical concepts first before implementing them using Python.

● People who want to learn PySpark along with its implementation in realistic projects.

● Big Data Scientists.

● Big Data Engineers.

Target Audiences

● People who are beginners and know absolutely nothing about PySpark and AWS.

● People who want to develop intelligent solutions.

● People who want to learn PySpark and AWS.

● People who love to learn the theoretical concepts first before implementing them using Python.

● People who want to learn PySpark along with its implementation in realistic projects.

● Big Data Scientists.

● Big Data Engineers.

Comprehensive Course Description:

The hottest buzzwords in the Big Data analytics industry are Python and Apache Spark. PySpark supports the collaboration of Python and Apache Spark. In this course, you’ll start right from the basics and proceed to the advanced levels of data analysis. From cleaning data to building features and implementing machine learning (ML) models, you’ll learn how to execute end-to-end workflows using PySpark.

Right through the course, you’ll be using PySpark for performing data analysis. You’ll explore Spark RDDs, Dataframes, and a bit of Spark SQL queries. Also, you’ll explore the transformations and actions that can be performed on the data using Spark RDDs and dataframes. You’ll also explore the ecosystem of Spark and Hadoop and their underlying architecture. You’ll use the Databricks environment for running the Spark scripts and explore it as well.

Finally, you’ll have a taste of Spark with AWS cloud. You’ll see how we can leverage AWS storages, databases, computations, and how Spark can communicate with different AWS services and get its required data.   

How Is This Course Different? 

In this Learning by Doing course, every theoretical explanation is followed by practical implementation.   

The course ‘PySpark & AWS: Master Big Data With PySpark and AWS’ is crafted to reflect the most in-demand workplace skills. This course will help you understand all the essential concepts and methodologies with regards to PySpark. The course is:

? Easy to understand. 

? Expressive. 

? Exhaustive. 

? Practical with live coding. 

? Rich with the state of the art and latest knowledge of this field. 

As this course is a detailed compilation of all the basics, it will motivate you to make quick progress and experience much more than what you have learned. At the end of each concept, you will be assigned Homework/tasks/activities/quizzes along with solutions. This is to evaluate and promote your learning based on the previous concepts and methods you have learned. Most of these activities will be coding-based, as the aim is to get you up and running with implementations.   

High-quality video content, in-depth course material, evaluating questions, detailed course notes, and informative handouts are some of the perks of this course. You can approach our friendly team in case of any course-related queries, and we assure you of a fast response.   

The course tutorials are divided into 140+ brief videos. You’ll learn the concepts and methodologies of PySpark and AWS along with a lot of practical implementation. The total runtime of the HD videos is around 16 hours.

Why Should You Learn PySpark and AWS? 

PySpark is the Python library that makes the magic happen.   

PySpark is worth learning because of the huge demand for Spark professionals and the high salaries they command. The usage of PySpark in Big Data processing is increasing at a rapid pace compared to other Big Data tools.   

AWS, launched in 2006, is the fastest-growing public cloud. The right time to cash in on cloud computing skills—AWS skills, to be precise—is now.

Course Content:

The all-inclusive course consists of the following topics:

1. Introduction:

a. Why Big Data?

b. Applications of PySpark

c. Introduction to the Instructor

d. Introduction to the Course

e. Projects Overview

2. Introduction to Hadoop, Spark EcoSystems, and Architectures:

a. Hadoop EcoSystem

b. Spark EcoSystem

c. Hadoop Architecture

d. Spark Architecture

e. PySpark Databricks setup

f. PySpark local setup

3. Spark RDDs:

a. Introduction to PySpark RDDs

b. Understanding underlying Partitions

c. RDD transformations

d. RDD actions

e. Creating Spark RDD

f. Running Spark Code Locally

g. RDD Map (Lambda)

h. RDD Map (Simple Function)

i. RDD FlatMap

j. RDD Filter

k. RDD Distinct

l. RDD GroupByKey

m. RDD ReduceByKey

n. RDD (Count and CountByValue)

o. RDD (saveAsTextFile)

p. RDD (Partition)

q. Finding Average

r. Finding Min and Max

s. Mini project on student data set analysis

t. Total Marks by Male and Female Student

u. Total Passed and Failed Students

v. Total Enrollments per Course

w. Total Marks per Course

x. Average marks per Course

y. Finding Minimum and Maximum marks

z. Average Age of Male and Female Students

4. Spark DFs:

a. Introduction to PySpark DFs

b. Understanding underlying RDDs

c. DFs transformations

d. DFs actions

e. Creating Spark DFs

f. Spark Infer Schema

g. Spark Provide Schema

h. Create DF from RDD

i. Select DF Columns

j. Spark DF with Column

k. Spark DF with Column Renamed and Alias

l. Spark DF Filter rows

m. Spark DF (Count, Distinct, Duplicate)

n. Spark DF (sort, order By)

o. Spark DF (Group By)

p. Spark DF (UDFs)

q. Spark DF (DF to RDD)

r. Spark DF (Spark SQL)

s. Spark DF (Write DF)

t. Mini project on Employees data set analysis

u. Project Overview

v. Project (Count and Select)

w. Project (Group By)

x. Project (Group By, Aggregations, and Order By)

y. Project (Filtering)

z. Project (UDF and With Column)

aa. Project (Write)

5. Collaborative filtering:

a. Understanding collaborative filtering

b. Developing recommendation system using ALS model

c. Utility Matrix

d. Explicit and Implicit Ratings

e. Expected Results

f. Dataset

g. Joining Dataframes

h. Train and Test Data

i. ALS model

j. Hyperparameter tuning and cross-validation

k. Best model and evaluate predictions

l. Recommendations

6. Spark Streaming:

a. Understanding the difference between batch and streaming analysis.

b. Hands-on with spark streaming through word count example

c. Spark Streaming with RDD

d. Spark Streaming Context

e. Spark Streaming Reading Data

f. Spark Streaming Cluster Restart

g. Spark Streaming RDD Transformations

h. Spark Streaming DF

i. Spark Streaming Display

j. Spark Streaming DF Aggregations

7. ETL Pipeline

a. Understanding the ETL

b. ETL pipeline Flow

c. Data set

d. Extracting Data

e. Transforming Data

f. Loading data (Creating RDS)

g. Load data (Creating RDS)

h. RDS Networking

i. Downloading Postgres

j. Installing Postgres

k. Connect to RDS through PgAdmin

l. Loading Data

8. Project – Change Data Capture / Replication On Going

a. Introduction to Project

b. Project Architecture

c. Creating RDS MySql Instance

d. Creating S3 Bucket

e. Creating DMS Source Endpoint

f. Creating DMS Destination Endpoint

g. Creating DMS Instance

h. MySql WorkBench

i. Connecting with RDS and Dumping Data

j. Querying RDS

k. DMS Full Load

l. DMS Replication Ongoing

m. Stoping Instances

n. Glue Job (Full Load)

o. Glue Job (Change Capture)

p. Glue Job (CDC)

q. Creating Lambda Function and Adding Trigger

r. Checking Trigger

s. Getting S3 file name in Lambda

t. Creating Glue Job

u. Adding Invoke for Glue Job

v. Testing Invoke

w. Writing Glue Shell Job

x. Full Load Pipeline

y. Change Data Capture Pipeline

After the successful completion of this course, you will be able to:

● Relate the concepts and practicals of Spark and AWS with real-world problems

● Implement any project that requires PySpark knowledge from scratch

● Know the theory and practical aspects of PySpark and AWS

Who this course is for:

● People who are beginners and know absolutely nothing about PySpark and AWS

● People who want to develop intelligent solutions

● People who want to learn PySpark and AWS

● People who love to learn the theoretical concepts first before implementing them using Python

● People who want to learn PySpark along with its implementation in realistic projects

● Big Data Scientists

● Big Data Engineers

Enroll in this comprehensive PySpark and AWS course now to master the essential skills in Big Data analytics, data processing, and cloud computing.

Whether you’re a beginner or looking to expand your knowledge, this course offers a hands-on learning experience with practical projects. Don’t miss this opportunity to advance your career and tackle real-world challenges in the world of data analytics and cloud computing. Join us today and start your journey towards becoming a Big Data expert with PySpark and AWS!

List of keywords:

Big Data analytics

Data analysis

Data cleaning

Machine learning (ML)

Spark RDDs

Dataframes

Spark SQL queries

Spark ecosystem

Hadoop

Databricks

AWS cloud

Spark scripts

AWS services

PySpark and AWS collaboration

PySpark tutorial

PySpark hands-on

PySpark projects

Spark architecture

Hadoop ecosystem

PySpark Databricks setup

Spark local setup

Spark RDD transformations

Spark RDD actions

Spark DF transformations

Spark DF actions

Spark Infer Schema

Spark Provide Schema

Spark DF Filter rows

Spark DF (Count, Distinct, Duplicate)

Spark DF (sort, order By)

Spark DF (Group By)

Spark DF (UDFs)

Spark DF (Spark SQL)

Collaborative filtering

Recommendation system

ALS model

Spark Streaming

ETL pipeline

Change Data Capture (CDC)

Replication

AWS Glue Job

Lambda Function

RDS

S3 Bucket

MySql Instance

Data Migration Service (DMS)

PgAdmin

Spark Shell Job

Full Load Pipeline

Change Data Capture Pipeline

Course Curriculum

Chapter 1: Introduction

Lecture 1: Why Big Data

Lecture 2: Applications of PySpark

Lecture 3: Introduction to Instructor

Lecture 4: Introduction to Course

Lecture 5: Projects Overview

Lecture 6: Request for Your Honest Review

Lecture 7: Links for the Courses Materials and Codes

Chapter 2: 01-Introduction to Hadoop, Spark EcoSystems and Architectures

Lecture 1: Links for the Courses Materials and Codes

Lecture 2: Why Spark

Lecture 3: Hadoop EcoSystem

Lecture 4: Spark Architecture and EcoSystem

Lecture 5: DataBricks SignUp

Lecture 6: Create DataBricks Notebook

Lecture 7: Download Spark and Dependencies

Lecture 8: Java Setup on Window

Lecture 9: Windows Setup Python Spark Hadoop

Lecture 10: Runing Spark on Window

Lecture 11: Java Download on MAC

Lecture 12: Installing JDK on MAC

Lecture 13: Setting Java Home on MAC

Lecture 14: Java check on MAC

Lecture 15: Installing Python on MAC

Lecture 16: Setup Spark on MAC

Chapter 3: Spark RDDs

Lecture 1: Links for the Courses Materials and Codes

Lecture 2: Spark RDDs

Lecture 3: Creating Spark RDD

Lecture 4: Running Spark Code Locally

Lecture 5: RDD Map (Lambda)

Lecture 6: RDD Map (Simple Function)

Lecture 7: Quiz (Map)

Lecture 8: Solution 1 (Map)

Lecture 9: Solution 2 (Map)

Lecture 10: RDD FlatMap

Lecture 11: RDD Filter

Lecture 12: Quiz (Filter)

Lecture 13: Solution (Filter)

Lecture 14: RDD Distinct

Lecture 15: RDD GroupByKey

Lecture 16: RDD ReduceByKey

Lecture 17: Quiz (Word Count)

Lecture 18: Solution (Word Count)

Lecture 19: RDD (Count and CountByValue)

Lecture 20: RDD (saveAsTextFile)

Lecture 21: RDD (Partition)

Lecture 22: Finding Average-1

Lecture 23: Finding Average-2

Lecture 24: Quiz (Average)

Lecture 25: Solution (Average)

Lecture 26: Finding Min and Max

Lecture 27: Quiz (Min and Max)

Lecture 28: Solution (Min and Max)

Lecture 29: Project Overview

Lecture 30: Total Students

Lecture 31: Total Marks by Male and Female Student

Lecture 32: Total Passed and Failed Students

Lecture 33: Total Enrollments per Course

Lecture 34: Total Marks per Course

Lecture 35: Average marks per Course

Lecture 36: Finding Minimum and Maximum marks

Lecture 37: Average Age of Male and Female Students

Chapter 4: Spark DFs

Lecture 1: Links for the Courses Materials and Codes

Lecture 2: Introduction to Spark DFs

Lecture 3: Creating Spark DFs

Lecture 4: Spark Infer Schema

Lecture 5: Spark Provide Schema

Lecture 6: Create DF from Rdd

Lecture 7: Rectifying the Error

Lecture 8: Select DF Colums

Lecture 9: Spark DF withColumn

Lecture 10: Spark DF withColumnRenamed and Alias

Lecture 11: Spark DF Filter rows

Lecture 12: Quiz (select, withColumn, filter)

Lecture 13: Solution (select, withColumn, filter)

Lecture 14: Spark DF (Count, Distinct, Duplicate)

Lecture 15: Quiz (Distinct, Duplicate)

Lecture 16: Solution (Distinct, Duplicate)

Lecture 17: Spark DF (sort, orderBy)

Lecture 18: Quiz (sort, orderBy)

Lecture 19: Solution (sort, orderBy)

Lecture 20: Spark DF (Group By)

Lecture 21: Spark DF (Group By – Multiple Columns and Aggregations)

Lecture 22: Spark DF (Group By -Visualization)

Lecture 23: Spark DF (Group By – Filtering)

Lecture 24: Quiz (Group By)

Lecture 25: Solution (Group By)

Lecture 26: Quiz (Word Count)

Lecture 27: Solution (Word Count)

Lecture 28: Spark DF (UDFs)

Lecture 29: Quiz (UDFs)

Lecture 30: Solution (UDFs)

Instructors

AI Sciences
AI Experts & Data Scientists |4+ Rated | 168+ Countries

AI Sciences Team
Support Team AI Sciences

Rating Distribution

1 stars: 34 votes

2 stars: 31 votes

3 stars: 191 votes

4 stars: 838 votes

5 stars: 1091 votes

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