Analyzing Flights within the U.S. with AWS and Energy BI | by Aashish Nair | Jul, 2023

Knowledge Warehousing with AWS Redshift

With AWS Glue, the information that was initially in a flat mannequin can now be represented with a extra becoming star schema in an information warehouse.

The cloud knowledge warehouse for this knowledge will likely be created with AWS Redshift Serverless. This entails making a namespace named flights-namespace in addition to a database named dev. As well as, it requires a workgroup named flights-workgroup, which will likely be used to put in writing SQL queries.

Observe: The workgroup has been configured to permit units exterior of the VPC to entry the database. This will likely be helpful when creating the visualization with Energy BI

Workgroup (Created by Writer)

Now, we will open the question editor in Redshift and begin creating the actual fact and dimension tables within the dev database.

Question Editor (Created by Writer)

First, the 4 tables within the schema should be created within the warehouse utilizing the next instructions:

Created Tables (Created by Writer)

The 4 tables are actually within the knowledge warehouse, however they’re all empty for the reason that knowledge continues to be within the flights-data-processed bucket.

The info will be copied into this knowledge warehouse utilizing the COPY command.

As an example, the information in flights.csv will be copied into the flights desk utilizing the next command syntax:

Observe: the iam_role variable needs to be assigned no matter iam function is was chosen when creating the workgroup.

By executing the COPY command for every of the csv information within the flights-data-processed bucket, the 4 tables needs to be full of the required knowledge.

For instance, here’s a preview of the airport desk:

Question Output (Created by Writer)

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