Ingesting Data Using AWS Glue

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Ingesting Data Using AWS Glue

You are a data engineer tasked with migrating some new CSV files into S3. Once there, you need to add the schema to Glue using Glue Crawler. The Data Science team would like to train some new models on a combined table of this information but only using a few select columns. To decrease cost and improve processing time, you need to create a job in Glue that will combine the CSV files together into one table but only including the needed columns. Then, you need to run the job and verify success in S3. After that, you will be ready to inform the Data Science team that the data is ready for training.

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Lab Info

Level

Intermediate

Last updated

Sep 23, 2025

Duration

30m

Challenge

Prepare the Environment
1. Create an S3 Bucket
2. Create a "parking_data" folder
3. Download Parking-Ticket-2022 data (https://open.toronto.ca/dataset/parking-tickets/)
4. Upload file: Parking_Tags_Data_2022.000.csv to the "parking_data" folder
Challenge

Create a Glue Crawler
1. Create a Glue Crawler to crawl the "parking_data" folder in S3.
2. Create a Database named "parking"" in AWS Glue.
3. Create and run an on demand crawler that will crawl the "parking_data" S3 folder.
4. Create a new policy named "AWSGlueServiceRole-Parking"
Challenge

Create a Job in Glue
1. Edit the AWSGlueServiceRole-Parking policy to allow for Read and Write access to AWS Glue.
2. Create a Visual Job in AWS Glue
3. Set the newly uploaded S3 file as the source
4. Create a Transformation that will drop all colums except "date_of_infraction", "infraction_code", and "infraction_description"
5. Create a data target to move the data back to the "parking_data" folder in S3. Have AWS Glue create a new "results" table and add it to the catalog.
6. Run the job and verify the file exists in S3.

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