Curriculum Overview: Automating Data Processing with AWS

This curriculum is designed to master the orchestration and automation of end-to-end data pipelines using AWS services. It aligns with the AWS Certified Data Engineer – Associate (DEA-C01) exam, specifically focusing on Domain 3: Data Operations and Support.

Prerequisites

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

• AWS Fundamentals: Basic understanding of AWS global infrastructure, IAM, and core services like Amazon S3 and EC2.
• Programming Literacy: Proficiency in at least one common data engineering language, such as Python, SQL, or Scala.
• Data Concepts: Familiarity with ETL (Extract, Transform, Load) concepts, structured vs. unstructured data, and basic database operations.
• Scripting: Experience with command-line tools (AWS CLI) and basic shell scripting (Bash/PowerShell).

Module Breakdown

Learning Objectives per Module

Module 1: Orchestration Masterclass

• Design complex workflows using Amazon Managed Workflows for Apache Airflow (MWAA) and Directed Acyclic Graphs (DAGs).
• Implement State Machines with AWS Step Functions to coordinate multi-step processing tasks across 220+ AWS services.
• Compare Orchestration Tools: Choose between Step Functions (serverless, state-based) and MWAA (code-heavy, Airflow ecosystem).

Module 2: Event-Driven Architectures

• Configure EventBridge to trigger data pipelines based on schedules or system events.
• Deploy Lambda Functions for real-time data validation, enrichment, and filtering.
• Manage Schedulers: Set up time-based triggers for AWS Glue Crawlers and data ingestion jobs.

Module 3: Programmatic Data Operations

• Utilize AWS SDKs: Call Amazon features directly from code to build custom automation scripts.
• Optimize Data Processing: Use Amazon EMR, Redshift, and AWS Glue features for high-scale transformations.
• API Management: Consume and maintain data APIs to expose processed data to downstream systems.

Module 4: Preparation and Transformation

• Automate Data Profiling: Use AWS Glue DataBrew for visual data cleansing and outlier detection.
• Query Automation: Leverage Amazon Athena to trigger SQL-based transformations as part of a pipeline.

Module 5: DevOps for Data

• Implement CI/CD: Use AWS CodePipeline and CodeBuild to automate the deployment of ETL scripts.
• IaC Patterns: Define data infrastructure using the AWS Cloud Development Kit (CDK) or Serverless Application Model (SAM).

[!IMPORTANT] Automation is not just about scheduling; it is about building idempotent pipelines where re-running a job does not cause data duplication or corruption.

Success Metrics

To demonstrate mastery of this curriculum, students must be able to:

1. Build a Resilient Pipeline: Successfully deploy a multi-stage ETL pipeline that handles error retries and dead-letter queues (DLQ).
2. Define Complex DAGs: Create an MWAA DAG that orchestrates tasks across S3, EMR, and Redshift.
3. Optimize Costs: Implement S3 Lifecycle policies and Lambda concurrency limits to reduce operational overhead.
4. Zero-Touch Deployment: Deploy a full data environment (S3, Glue, Lambda) using a single cdk deploy command.

Real-World Application

In a professional setting, automating data processing transforms a reactive data team into a proactive one.

• Scenario A (E-commerce): Real-time processing of clickstream data using Kinesis and Lambda to update recommendation engines instantly.
• Scenario B (Healthcare): Using MWAA to orchestrate complex machine learning pipelines in SageMaker, ensuring data is preprocessed, models are trained, and endpoints are updated weekly without manual intervention.
• Scenario C (Finance): Automating the ingestion of third-party SaaS data via Amazon AppFlow into a Redshift data warehouse for daily executive reporting.

[!TIP] Always use AWS CloudWatch to monitor your automated pipelines. Set up alarms for "Failed Executions" to ensure you are notified before your stakeholders notice missing data.