100% PASS AWS Certified Data Engineer Associate Exam Study Guide + CHEAT SHEET (Downloadable)

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Get exam-ready with a clear, exam-aligned overview & domain-by-domain walkthrough of the AWS Certified Data Engineer - Associate (DEA-C01) topics.

What you’ll learn
AWS Data Engineering Deep Dive: DEA-C01 Exam Prep Guide
This video breaks down the four key domains you need to master for the AWS Certified Data Engineer – Associate (DEA-C01) exam. We cover everything from building scalable ETL pipelines to securing and governing your data on AWS.

1. Data Ingestion & Transformation
Learn the core skills for managing data flow on AWS:

Ingestion: Master Batch (S3, EMR, DMS) and Streaming (Kinesis, MSK, DynamoDB Streams) patterns, including managing throughput, latency, and replayability.

Transformation (ETL): Build robust ETL pipelines using AWS Glue, Amazon EMR, and Lambda. Focus on Spark processing, data staging, optimizing container usage, and transforming data between formats (e.g., CSV to Parquet).

Orchestration: Configure and manage complex workflows using services like AWS Step Functions, Amazon MWAA (Apache Airflow), and EventBridge.

Programming: Apply programming and SQL skills, including using IaC (CDK/CloudFormation), optimizing code, and deploying serverless applications with AWS SAM.

2. Data Store Management
The backbone of your data architecture:

Data Store Selection: Choose the right service—Amazon Redshift, DynamoDB, Amazon RDS, or Amazon EMR—based on cost, performance, and access patterns.

Data Cataloging: Build and reference a centralized data catalog using the AWS Glue Data Catalog to discover schemas and partition data.

Data Lifecycle: Implement policies for hot/cold data storage, cost optimization, and data retention using S3 Lifecycle Policies and DynamoDB TTL.

Data Modeling: Design schemas for various data stores, understand schema evolution, and establish data lineage (e.g., using AWS SCT and SageMaker ML Lineage Tracking).

3. Data Operations & Support
Keep your pipelines running smoothly and reliably:

Automation & Analysis: Use Lambda, Amazon MWAA, and Step Functions for automated processing. Analyze data with Amazon Athena, QuickSight, and AWS Glue DataBrew.

Monitoring & Maintenance: Implement robust logging (CloudWatch Logs, CloudTrail) for audits and traceability. Troubleshoot performance and maintain pipelines (Glue, EMR).

Data Quality: Implement data validation, run quality checks (e.g., checking for empty fields), and define data quality rules with tools like AWS Glue DataBrew.

4. Data Security & Governance
Secure and govern your data environment:

Security Mechanisms: Apply Authentication (IAM roles/policies, Secrets Manager) and Authorization (Least Privilege) concepts. Secure VPCs and manage access through Lake Formation.

Encryption: Implement client-side and server-side encryption using AWS KMS and configure encryption in transit. Understand data anonymization and masking.

Audit Readiness: Prepare logs for auditing using CloudTrail, CloudWatch Logs, and CloudTrail Lake.

Privacy: Implement PII identification (Amazon Macie), data sharing permissions (Redshift Data Sharing), and data sovereignty strategies.