Curriculum Overview: Data Lifecycle Management

This curriculum is designed to prepare data engineers for the AWS Certified Data Engineer – Associate (DEA-C01) exam, specifically focusing on the management of data from creation through destruction. You will learn to balance performance, cost-efficiency, and compliance using the AWS storage ecosystem.

Prerequisites

Before beginning this module, students should have a baseline understanding of the following:

• Basic AWS Storage Knowledge: Familiarity with Amazon S3 (buckets/objects) and Amazon EBS (volumes).
• Cloud Fundamentals: Understanding of Regions, Availability Zones (AZs), and the Shared Responsibility Model.
• Data Governance Concepts: General awareness of RPO (Recovery Point Objective) and RTO (Recovery Time Objective).
• JSON Syntax: Ability to read and write basic JSON, as it is used for S3 Lifecycle and IAM policies.

Module Breakdown

Data Lifecycle Visualization

Learning Objectives per Module

Module 1: Foundations of DLM

• Define the Data Lifecycle: Trace data from creation/entry to destruction.
• Identify Business Goals: Align DLM strategies with data security, availability, and organizational performance.

Module 2: Tiering & Cost Optimization

• Categorize Data (Hot vs. Cold): Differentiate between frequently accessed data (latency-sensitive) and archival data.
• Storage Selection: Choose appropriate storage classes (e.g., S3 Standard-IA vs. S3 One Zone-IA) based on access patterns.

Module 3: Automation & Expiration

• Configure S3 Lifecycle Policies: Automate transitions between storage tiers and set expiration rules for old objects.
• Implement TTL: Use DynamoDB Time-to-Live (TTL) to automatically expire items from tables without consuming WCU (Write Capacity Units).

Module 4: Data Movement & Integration

• Load/Unload Operations: Perform high-performance data movement between Amazon S3 and Amazon Redshift for analytics workflows.
• Hybrid Cloud Transfer: Use AWS DataSync for efficient data transfer from on-premises to AWS archival tiers.

Module 5: Compliance & Security

• Enforce Retention: Implement WORM (Write Once, Read Many) compliance using S3 Object Lock.
• Resiliency: Configure S3 Cross-Region Replication (CRR) and S3 Versioning to protect against accidental deletion or regional outages.

Success Metrics

To demonstrate mastery of Data Lifecycle Management, the learner must be able to:

1. Deploy a multi-tier S3 Lifecycle Policy that transitions data to Glacier Deep Archive after 90 days and deletes it after 7 years.
2. Calculate Cost-Benefit: Compare the cost of $1TB$ of data in S3 Standard vs. S3 Glacier Deep Archive over a 12-month period.
3. Audit Data Usage: Use AWS Config and Amazon Macie to identify unencrypted or sensitive data that violates DLM policies.
4. Operationalize TTL: Successfully configure a DynamoDB table to auto-delete session logs older than 24 hours.

[!IMPORTANT] Mastery is not just knowing the services, but knowing when to use them. For the exam, always prioritize Cost Optimization if the performance requirements are loose, and Security if compliance (HIPAA/GDPR) is mentioned.

Real-World Application

Use Case: Healthcare Data Retention (HIPAA)

In the healthcare industry, patient records must often be kept for 7+ years. Using the tools learned in this curriculum:

• Hot Data: Current year records are stored in Amazon RDS or S3 Standard for immediate access.
• Cold Data: Records from years 2–7 are moved to S3 Glacier via automated lifecycle policies to save costs while remaining retrievable within minutes/hours.
• Compliance: S3 Object Lock is enabled to prevent any alteration of the records for the duration of the retention period.

Storage Class Comparison Logic

Summary Checklist

• Understand the difference between S3 Lifecycle Transitions and Expirations.
• Know the use cases for S3 Versioning vs. Object Lock.
• Identify the specific storage needs for Transactional (RDS/EBS) vs. Analytical (Redshift/S3) workloads.