Data Analytics on AWS - Easy Explanation - Part 01

Name: Data Analytics on AWS - Easy Explanation - Part 01
Rating: 4.6 (126 reviews)

Learn about Data Analytics and AWS Service supporting it

Created byAshish Prajapati

Last updated 11/2025

English

English [Auto],

What you'll learn

What is Data Analytics?
AWS Services for Data Analytics
Identify the use cases for various services
Understand the Data Pipeline

Course content

1 section • 6 lectures • 1h 28m total length

What is Data Analytics?9:54
Discover how data analytics on the cloud turns raw data into actionable insight. Follow an end-to-end pipeline from data sources through batch and streaming ingestion, storage, processing, cataloging, and visualization.
Basic Terminologies11:12
Learn core data analytics terms: database schema, data modeling, data warehouse, olap, oltp, and etl. See how data flows from sources through etl to a warehouse.
Data Analytics on AWS6:30
Build a flexible data analytics pipeline on AWS, from ingestion with DMS, Kinesis, and Snow Family to processing with Glue and EMR, and querying with Athena and QuickSight.
AWS Glue31:29
Explore how AWS Glue provides a serverless data integration catalog, using crawlers to scan S3 data, infer schemas, and create databases and tables for analytics.
Amazon Athena13:57
Explore Amazon Athena, a serverless, interactive sql analytics service that queries data on S3 with Presto, using AWS Glue catalog and supports csv, json, parquet, avro, and ORC.
Amazon Redshift15:00
Explore amazon redshift, a fully managed data warehouse optimized for analytics with columnar storage and massive parallel processing to run sql queries fast, scale via ra3 and aqua.

Requirements

A basic understanding of AWS Cloud

Description

What is data analytics?

Data analytics converts raw data into actionable insights. It includes a range of tools, technologies, and processes used to find trends and solve problems by using data. Data analytics can shape business processes, improve decision-making, and foster business growth.

Why is data analytics important?

Data analytics helps companies gain more visibility and a deeper understanding of their processes and services. It gives them detailed insights into the customer experience and customer problems. By shifting the paradigm beyond data to connect insights with action, companies can create personalized customer experiences, build related digital products, optimize operations, and increase employee productivity.

AWS analytics services

AWS provides a comprehensive set of analytics services that fit all your data analytics needs and enables organizations of all sizes and industries to reinvent their business with data. From storage and management, data governance, actions, and experiences, AWS offers purpose-built services that provide the best price-performance, scalability, and lowest cost.

How is data analytics used in business?

Businesses capture statistics, quantitative data, and information from multiple customer-facing and internal channels. But finding key insights takes careful analysis of a staggering amount of data. This is no small feat. Look at some examples of how data analytics and data science can add value to a business.

Data analytics improves customer insight

Data analytics can be conducted on datasets from various customer data sources such as the following:

Third-party customer surveys
Customer purchase logs
Social media activity
Computer cookies
Website or application statistics

Analytics can reveal hidden information such as customer preferences, popular pages on a website, the length of time customers spend browsing, customer feedback, and interaction with website forms. This enables businesses to respond efficiently to customer needs and increase customer satisfaction.

Who this course is for:

Beginner and Intermediate learners who are willing to learn about Data Analytics on AWS