Curriculum Overview: Programming Concepts for Data Engineering

[!IMPORTANT] This curriculum is specifically aligned with the AWS Certified Data Engineer – Associate (DEA-C01) exam, focusing on Domain 1: Data Ingestion and Transformation.

Prerequisites

Before beginning this curriculum, students should possess the following foundational knowledge:

• Experience: 2–3 years of experience in data engineering or a related IT role.
• General IT Knowledge: Familiarity with the ETL (Extract, Transform, Load) lifecycle and basic Git commands for source control.
• Core AWS Knowledge: A high-level understanding of S3, Lambda, and compute/storage concepts.
• Programming Foundations: Intermediate proficiency in Python or SQL. Knowledge of shell scripting (Bash/PowerShell) is highly recommended.
• Mathematics: Understanding of general vector concepts for modern data processing (LLMs).

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Module Breakdown

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Module Objectives per Module

Module 1: Foundational Scripting & APIs

• Objective: Develop and optimize scripts for data ingestion using multiple languages (Python, SQL, Scala).
• Skill: Configure Lambda functions to meet specific concurrency and performance needs using storage volume mounts.
• Skill: Create and consume data APIs to expose data to downstream systems.

Module 2: Infrastructure as Code (IaC)

• Objective: Implement repeatable resource deployment using AWS CDK and CloudFormation.
• Skill: Package and deploy serverless data pipelines (Lambda, DynamoDB) using the AWS Serverless Application Model (SAM).

Module 3: Orchestration & Workflow Design

• Objective: Build resilient, fault-tolerant ETL workflows using AWS Step Functions and Apache Airflow (MWAA).
• Skill: Implement event-driven triggers via Amazon EventBridge and S3 Event Notifications.

Module 4: Distributed Computing & Optimization

• Objective: Define and apply distributed computing principles to handle massive data volumes.
• Skill: Optimize code runtime for large-scale transformations and manage data skew in Spark-based environments.

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Success Metrics

To demonstrate mastery of this curriculum, the student must satisfy the following criteria:

1. Deployment Proficiency: Successfully deploy a multi-stage data pipeline using a single sam deploy or cdk deploy command without manual console intervention.
2. Performance Optimization: Refactor a standard data transformation script to reduce runtime by at least 30% through parallelization or optimized SQL queries.
3. Resiliency Validation: Construct a state machine that handles 3 distinct failure scenarios (e.g., API throttling, timeout, data validation error) using automated retries and Dead Letter Queues (DLQs).
4. Security Compliance: Implement fine-grained access control using IAM roles and ensure all data in transit is encrypted using AWS SDKs.

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Real-World Application

[!TIP] "AI is only as good as its data. Behind every LLM are armies of data engineers who cleaned, structured, and delivered the training data."

Programming concepts in data engineering are not just about "writing code"; they are about building reliable systems. In a production environment, these skills translate to:

• Scalability: Using distributed computing to process Petabytes of data that a single server could never handle.
• Cost Management: Using IaC and serverless (Lambda) to ensure you only pay for the compute you use, avoiding expensive "always-on" idle servers.
• Reliability: CI/CD ensures that a small bug in a SQL script doesn't take down an entire company's dashboard, as changes are tested automatically before deployment.

Performance Trade-off Visualization

Estimated Timeline