Introduction to Data Analytics on Amazon AWS Cloud

Name: Introduction to Data Analytics on Amazon AWS Cloud
Rating: 3.9 (64 reviews)

Learn AWS Redshift Essentials, AWS Glue (Extract, Transform, Load Process) and AWS QuickSight with Practical Code Labs

Created bySKILL CURB

Last updated 4/2021

English

What you'll learn

Basic SQL
Aggregations (SUM, AVG, MIN, MAX) using SQL
Cloud Computing Basics
Data Warehousing Basics
AWS Redshift Essentials
Loading Datasets to S3 Bucket
Multiple Data Importing Techniques in Redshift
AWS Glue (Extract, Transform, Load Process)
Exporting S3 Data to AWS Redshift
Security Group Settings
Data Visualization on AWS QuickSight
Dataset Report Generation and Sharing

Course content

8 sections • 68 lectures • 5h 5m total length

What is Cloud3:35
Understand how the cloud stores big data securely, from terabytes to petabytes, with privacy, backups, and universal access across devices.
What is Cloud Computing6:58
Cloud Computing Services12:23
Discover core cloud computing services, including virtual machines, storage, databases, and applications, with emphasis on redundancy and on-demand scalability. Explore IaaS, PaaS, and SaaS and their roles in cloud deployment.
Why Cloud Computing3:53
Quiz

Lab: Creating AWS Account4:43
Lab: AWS Web Console Overview3:05
Navigate the AWS web console to access services like Redshift and manage regions such as us east 2. Explore tutorials for building websites, web apps, and databases.
Lab: Identity and Access Management IAM Roles4:30
Lab: Creating a AWS Cluster6:38
Lab: Creating IAM User on Redshift3:55
Lab: Getting Started with AWS RedShift QueryEditor5:35
Lab: Installing SQL Workbench Client Tool4:13
Lab: Connecting AWS RedShift with SQL Client7:41
Connect aws redshift to a sql client using the jdbc driver, configure eskil workbench with the redshift driver, update security group to allow your IP, and verify the connection.
Lab: AWS RedShift SQL Queries4:27
Lab: Loading Dataset into S3 Bucket5:52
Lab: Setting up AWS CLI4:29
Lab: Load Dataset to S3 Bucket from CLI4:52
Learn to create an s3 bucket, navigate local directories, and upload datasets to the bucket using the command line interface, including creating a folder and syncing files.
Lab: Data Loading from S3 to AWS RedShift - Part15:14
Lab: Data Loading from S3 to AWS RedShift - Part24:39

What is ETL7:34
Introduction to AWS GLUE5:43
ETL Process in Data Warehouse3:24
Learn how the etl and edl processes power data warehouses with in-place transformations, incremental loading, and log replication across diverse sources, enabling transparent analytics in the escudo analytics database.
ETL Tools2:15
Lab: Fetch Table From S3 using AWS GLUE6:02
Lab: Create IAM Role and Fetch Data from S3 Bucket9:03
Lab: Security Group and VPC Setup7:06
Create a security group in the VPC and set access rules, then create a VPC endpoint for AWS Glue, and finally configure a new Amazon Redshift cluster with these settings.
Lab: Connecting AWS Redshift to GLUE3:30
Lab: Crawl RedShift Table MetaData to GLUE3:52
Lab: GLUE Job to Transfer Data from S3 to RedShift4:11
Lab: Data Transformation with AWS GLUE11:09
Lab: Loading datasets into RedShift using JDBC9:33
Learn to load multiple datasets into Amazon Redshift via JDBC using AWS Glue crawlers and separate jobs, creating databases and tables for dispatch items and item sales data.

Introduction to SQL2:13
Learn the Eskil commands and essential functions to perform analytics on a relational database management system, including select, update, delete, insert, and generate tabulated views under ANSI and ISO standards.
Basic SQL Commands1:43
Lab: SELECT Statement Part13:00
Lab: SELECT Statement Part21:31
Lab: SELECT Statement Part31:28
Lab: DISTINCT Statement1:26
Lab: COUNT keyword2:57
Learn to use count to tally total countries (fifty thousand values) and distinct to count unique country names (185), then count distinct regions (seven) and alias the output with as.
Lab: WHERE Keyword3:44
Lab: AND Keyword2:30
Describe how the and operator enforces multiple conditions to filter data by region and country, such as Europe and Moldova, and count the matching records in the dataset.
Lab: OR Keyword2:16
Lab: NOT keyword1:25
Lab: UPDATE Statement3:26
Lab: DELETE Statement1:35
Lab: INSERT Statement2:39
Learn to use the insert statement to add a new row to the sales dataset, mapping values to region, country, item type, and sales, then verify the insertion.
Lab: Table SQL Joins Part12:49
Lab: Table SQL Joins Part23:09
Lab: SQL Functions Part14:26
Lab: SQL Functions Part22:43
Lab: Value Calculation and Sorting4:30
Lab: Slicing and Limiting Data3:50
Lab: Grouping and Filtering4:39
Group data by region and sales channel to count totals, then apply having filters. Sum total costs by item type and explore visuals with AWS QuickSight.

Introduction to AWS QuickSight5:35
Lab: Creating AWS QuickSight Account2:00
Lab: Importing Dataset3:38
Import datasets into the AWS cloud analytics dashboard by uploading from local files or external sources, previewing and editing data, then visualize and apply basic aggregations.
Lab: Basic Data Aggregation in AWS QuickSight3:29
Lab: Donut and Pie Chart3:44
Lab: Bar Chart3:28
Lab: Line Chart3:39
Learn to use line charts and pivot tables to compare total cost and total profit across regions.
Lab: Dashboard5:01
Learn to build a dashboard in quick site, show distinct regions and countries, cost and profit, and visualize regional profits by item type with online and offline sales, publish.

Requirements

Existing AWS Account

Description

This course is designed for the students who are at their initial stage or at the beginner level in learning data analytics, cloud computing data visualization and Analytics using the Amazon AWS Cloud Services.

This course focuses on what cloud computing is followed by some essential concepts of data analytics. It also has practical hands-on lab exercises which covers a major portion of importing and performing some Analytics on the datasets.

The ETL tool used is AWS Glue and analytics is performed using a visual tool known as QuickSight. The lab portion covers all the essentials of the two platforms starting from importing the datasets, loading it, performing powerful SQL queries and then analyzing the same data using the visual graphical tools available on QuickSight platform.

The course goes into AWS core product of data-warehousing Redshift which is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more.

This course will give you the experience to leverage the data analytical strengths of AWS Cloud and strengthen your resume. This will also enable with skills to acquire new insights for your business and customers.

Please join us in this end to end course which will take you through the learning journey of AWS core products.

Who this course is for:

Beginners in Data Visualization, Data Warehousing.
Students at their initial stage of learning Amazon Web Services.
Students at their initial stage of learning AWS Redshift.
Beginners in learning AWS Glue for ETL Service

Introduction to Data Analytics on Amazon AWS Cloud

What you'll learn

Explore related topics

Course content

Introduction to Cloud Computing4 lectures • 27min

Introduction to Data Warehouse5 lectures • 33min

Introduction to AWS RedShift3 lectures • 13min

Lab: Practical Guide to AWS Redshift14 lectures • 1hr 10min

ETL with AWS GLUE12 lectures • 1hr 13min

SQL Foundation for Data Analytics21 lectures • 58min

AWS QuickSight Dashboard and Visualization8 lectures • 31min

Capstone Project1 lecture • 1min

Requirements

Description

Who this course is for: