Curriculum Overview: Pipeline Orchestration and Programming

This curriculum provides a comprehensive deep-dive into the automation and management of data lifecycles within the AWS ecosystem. It focuses on transition from manual scripts to scalable, resilient, and code-driven data pipelines, preparing candidates for the AWS Certified Data Engineer – Associate (DEA-C01) certification.

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Prerequisites

Before starting this curriculum, students should have a foundational understanding of the following:

• Cloud Fundamentals: Basic knowledge of AWS Identity and Access Management (IAM), Amazon S3 storage buckets, and VPC networking.
• Programming Proficiency: Working knowledge of Python (for Lambda and PySpark) and SQL (for data querying and transformation).
• Core Data Concepts: Familiarity with the ETL (Extract, Transform, Load) lifecycle and the difference between batch and streaming data processing.

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Module Breakdown

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Module Objectives

POP-01: Orchestration Fundamentals

• State Machine Logic: Build and manage resilient workflows using AWS Step Functions to coordinate multi-step ETL processes.
• Workflow Selection: Evaluate when to use Amazon MWAA (Managed Apache Airflow) for complex directed acyclic graphs (DAGs) versus AWS Step Functions for event-driven logic.

POP-02: Event-Driven Architectures

• Trigger Mechanisms: Configure S3 Event Notifications and EventBridge rules to automate pipeline execution based on real-time data arrival.
• Serverless Automation: Use AWS Lambda to perform lightweight data transformations and manage service-to-service communication.

POP-03: Data Programming & Best Practices

• Distributed Computing: Understand the architecture of Spark and how to optimize code for high-volume data ingestion.
• Software Engineering: Apply version control (Git), modular coding practices, and unit testing to data scripts.

POP-04 & POP-05: IaC and CI/CD

• Repeatable Infrastructure: Define entire data environments using AWS CloudFormation or CDK to eliminate manual configuration errors.
• Automated Release: Build a continuous delivery pipeline that automatically tests and deploys transformation code into production environments.

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Visual Overview of the Curriculum

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Success Metrics

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

1. Design a Resilient Workflow: Create a Step Functions state machine that includes error handling, retries, and parallel task execution.
2. Deploy via Code: Deploy a serverless data pipeline (Lambda + S3 + DynamoDB) exclusively using AWS SAM or CloudFormation.
3. Optimize Runtime: Refactor a provided PySpark script to reduce runtime by at least 20% through efficient data partitioning or caching techniques.
4. Auditability: Implement a logging and monitoring solution using CloudWatch that triggers an SNS alert upon pipeline failure.

[!IMPORTANT] Success is measured not just by "making it work," but by the ability to recover from failures automatically (resiliency) and deploy changes without manual intervention (automation).

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Real-World Application

In a professional environment, these skills translate directly to the following high-impact roles and tasks:

• Data Architect: Designing "City Traffic Control" systems for data, ensuring that millions of records flow from ingestion to analytics without collisions.
• Data Engineer: Building automated "Data Lakes" where raw data is instantly transformed into business-ready insights the moment it hits S3.
• Analytics Engineer: Bridging the gap between software engineering and data analysis by applying CI/CD practices to SQL and Python codebases.

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Comparison of Orchestration Tools