Data Analysis on AWS- Glue, Athena, Quicksight

Data Analysis on AWS- Glue, Athena, Quicksight, Learn to create Glue Crawler, run SQL queries in Athena and create Visualization Charts using AWS Quicksight.

This course will teach you about the numerous cloud services available through Amazon Web Services’ or AWS’ cloud computing platform. This chapter will teach you how to do data analysis on a dataset in order to uncover numerous insights that may be utilized to make key decisions based on the data. There are numerous techniques available for data analysis. SQL queries can be used to extract important data from a database-stored dataset. Additionally, you can utilize business intelligence tools such as AWS Quicksight to generate visualization charts and analytics reports that may be used to visually display findings through the use of colorful charts and creative parameter settings.

This course will introduce you to a variety of AWS Analytics services, including the following:

AWS Glue

Amazon Athena

AWS Quicksight

The process involves a number of processes, from data preparation and cleaning to analysis and visualization. This course will teach you how to use AWS Glue to develop a crawler that can traverse a dataset stored in Amazon S3 or DynamoDB and detect the schema. This crawler could be used to construct a database from which SQL queries could be run using AWS Athena to uncover insights based on specified circumstances and parameters. Additionally, you can use SQL queries to customize records in a table and execute other activities. SQL is a Structured Query Language that is commonly used for data analysis on databases and can be utilized on the AWS cloud computing platform as well.

Additionally, you will learn how to use AWS Quicksight to generate visualization charts and business intelligence reports. You will learn how to design a variety of visualization charts in Quicksight, including a treemap, a word cloud, a bar chart, and a pie chart. Additionally, you will learn how to prepare your dataset prior to building visualization charts.

When combined, all of these talents can be applied to Data Science projects as well.