Lab: Implementing Data Privacy and Governance on AWS

This hands-on lab guides you through implementing data privacy controls, PII identification, and fine-grained access control using AWS Lake Formation, Amazon Glue, and Amazon S3. These skills are critical for the AWS Certified Data Engineer - Associate (DEA-C01) exam.

[!WARNING] Remember to run the teardown commands at the end of this lab to avoid ongoing charges. Estimated cost is < $0.50 if within the Free Tier.

Prerequisites

An active AWS Account.
AWS CLI installed and configured with Administrator access.
Basic knowledge of IAM (Identity and Access Management).
A text editor to create a sample CSV file.
Placeholder: Replace <YOUR_ACCOUNT_ID> and <YOUR_REGION> with your actual details.

Learning Objectives

Create a secure data lake storage structure in Amazon S3.
Catalog datasets using AWS Glue Crawlers.
Implement Fine-Grained Access Control (FGAC) (column-level) using AWS Lake Formation.
Understand the workflow for PII Identification and data masking.

Architecture Overview

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Step-by-Step Instructions

Step 1: Prepare Sample Data and S3 Bucket

First, we create a bucket and a mock dataset containing PII (Personally Identifiable Information).

Create a file named users.csv with the following content:

csv
user_id,name,email,phone,credit_card,zipcode 1,John Doe,john@example.com,555-0101,1234-5678-9012,90210 2,Jane Smith,jane@example.com,555-0102,9876-5432-1098,10001
Run the following CLI commands:

bash

# Create a unique bucket name
BUCKET_NAME="brainybee-lab-privacy-<YOUR_ACCOUNT_ID>"

# Create the bucket
aws s3 mb s3://$BUCKET_NAME --region <YOUR_REGION>

# Upload the data to a 'raw' prefix
aws s3 cp users.csv s3://$BUCKET_NAME/raw/users.csv

▶Console alternative

Navigate to S3 > Create bucket.
Name it brainybee-lab-privacy-<YOUR_ACCOUNT_ID>.
Click Create.
Upload the users.csv file into a new folder named raw/.

Step 2: Set Up Lake Formation Permissions

Before Lake Formation can manage the data, the IAM role/user running this lab must be a Data Lake Administrator.

bash

# Grant your current user administrative rights in Lake Formation
aws lakeformation put-data-lake-settings --data-lake-settings '{
    "DataLakeAdmins": [{"DataLakePrincipalIdentifier": "arn:aws:iam::<YOUR_ACCOUNT_ID>:root"}]
}'

Step 3: Register S3 Location & Catalog Data

We will now register the S3 bucket as a managed location and run a crawler to populate the metadata.

bash

# Register the S3 location
aws lakeformation register-resource --resource-arn arn:aws:s3:::$BUCKET_NAME

# Create a Glue Database
aws glue create-database --database-input '{"Name": "lab_privacy_db"}'

# Create and run a Crawler (Simplified IAM role assumption assumed)
aws glue create-crawler --name privacy-crawler --role AWSGlueServiceRole-Lab --database-name lab_privacy_db --targets '{"S3Targets": [{"Path": "s3://'$BUCKET_NAME'/raw/"}]}'

aws glue start-crawler --name privacy-crawler

[!TIP] It takes about 1-2 minutes for the crawler to finish. You can check status with aws glue get-crawler --name privacy-crawler.

Step 4: Implement Column-Level Security

In this step, we restrict an IAM user from seeing the credit_card column.

bash

# Create a Data Cell Filter (This is the mechanism for FGAC)
aws lakeformation create-data-cells-filter --table-data-cells-filter '{
    "TableName": "raw",
    "DatabaseName": "lab_privacy_db",
    "Name": "mask_pii_filter",
    "ColumnNames": ["user_id", "name", "email", "zipcode"],
    "ColumnWildcard": {"ExcludedColumnNames": ["credit_card", "phone"]}
}'

Checkpoints

Glue Catalog Check: Run aws glue get-table --database-name lab_privacy_db --name raw. You should see the schema including credit_card.
Lake Formation Check: Open the Lake Formation Console > Data filters. You should see mask_pii_filter listed.
Permission Verification: Ensure your principal does not have SELECT on the full table but only through the filter.

Troubleshooting

Error	Possible Cause	Fix
`Insufficient Lake Formation Permissions`	You aren't a Data Lake Admin.	Repeat Step 2 or add your IAM ARN to Admin list in LF Console.
`Crawler failed: Access Denied`	S3 bucket policy or Glue Role issues.	Ensure the IAM Role used by the crawler has `s3:GetObject` on the bucket.
`Table not found`	Crawler hasn't finished.	Wait for the crawler status to return to `READY`.

Clean-Up / Teardown

Execute these commands to remove all resources and stop charges:

bash

# 1. Delete Glue Crawler and Database
aws glue delete-crawler --name privacy-crawler
aws glue delete-database --name lab_privacy_db

# 2. Deregister S3 location
aws lakeformation deregister-resource --resource-arn arn:aws:s3:::$BUCKET_NAME

# 3. Delete S3 Bucket and objects
aws s3 rb s3://$BUCKET_NAME --force

Cost Estimate

Service	Estimated Cost
S3	Free Tier (0.023/GB if exceeded)
AWS Glue	~$0.44 per DPU-Hour (Crawler typically uses 2 DPUs for < 2 mins)
Lake Formation	$0.00 (Governance is free)
Total	~$0.10 - $0.25

Stretch Challenge

Row-Level Filtering: Modify the Data Cell Filter in Step 4 to only show users where zipcode = '90210'. This demonstrates how to handle data residency requirements (e.g., ensuring local analysts only see local data).

Concept Review

Data Privacy Pillars on AWS

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Data Masking: Replacing sensitive data with functional aliases. Lake Formation handles this via cell-level filters.
PII Identification: The process of finding sensitive strings (SSNs, Emails). Amazon Macie automates this using machine learning.
Principle of Least Privilege: Granting users only the specific columns and rows they need to perform their jobs.

Feature	IAM Policy	Lake Formation
Granularity	Resource/API level	Row, Column, Cell level
Ease of Use	Complex JSON policies	Visual Grant/Revoke
Cross-Account	Manual Role Assumption	Automated Data Sharing