AWS Certified Data Engineer – Associate (DEA-C01)

Data pipelines stitched together without a clear design tend to fail quietly: lost records, unmanaged costs, and access controls nobody can explain to an auditor. This course gives IT Directors and Team Leads a structured way to build shared AWS data engineering skills across the team, covering AWS data engineering strategies, AWS terminology, security and governance considerations, Amazon Redshift, and the best-practice danger zones teams need to recognize.

Role fit: Best suited to data engineers, cloud engineers, and DBAs with existing data and cloud experience. This is not an introductory course; the certification expects roughly 2–3 years of data experience and 1–2 years of hands-on AWS experience. It also works for practitioners coming from Azure, GCP, or on-premises data work who need to become productive in AWS environments.

Time investment: About 18 hours across 18 skills of roughly one hour each. Most learners can finish in 4–6 weeks at 3–4 hours per week. Each skill stands alone, so Training Managers can assign targeted topics ahead of specific projects.

Change-management note: Hands-on work runs in an AWS account that learners create during the course. Before assigning it, provide a sandbox account with billing guardrails. The instructor stresses resource teardown so services left running don't incur extra costs.

Compliance angle: The course covers IAM policy structure, AWS data governance concepts, and analytics tools such as QuickSight. These topics support least-privilege access discussions and audit-ready data governance planning.

Validation questions at the end of each skill reinforce DEA-C01 exam reasoning, helping teams build consistent certification readiness.

IT teams use this training to move from scattered, hand-built data workflows to pipelines that are secure, observable, and resilient. Throughout the course, learners build in their own AWS account using a recurring project dataset. They copy public data into S3 with the AWS CLI, then work through it with Glue, DynamoDB, and dashboards. The result is practice on a full workflow, not isolated feature demos.

• Fewer lost records and failed writes: Your team learns why tightly coupled architectures pass errors back to clients and drop data. They also learn how API Gateway, SQS, and SNS decouple ingestion so failed writes can be handled without losing records.
• Faster troubleshooting of messy data: When the Glue visual editor can't preview data with unusual characters, learners switch to Glue notebooks to inspect, pre-convert, and cleanse that data.
• Repeatable, automated pipelines: Learners deploy an AWS SAM template in which a Step Functions state machine runs a Lambda function that loads data into DynamoDB. This helps cut down on manual reruns.
• Tighter access control and cost discipline: The course covers IAM policy components such as principals, resources, and conditions, along with row-level security in QuickSight. Guided teardown steps help prevent forgotten resources from adding unplanned spend.

The course also covers near real-time analytics scenarios, such as QuickSight dashboards that visualize data stored in Redshift as it updates, so teams can support reporting requests with controlled, repeatable workflows.

Knowledge

• How IAM policies are structured, including the roles of principal, action, resource, effect, and condition in securing data access
• The tradeoffs among AWS file, object, and database storage options, including Amazon S3 and Amazon RDS scaling and multi-AZ high availability
• How AWS Glue, Amazon Athena, Amazon Redshift, and Amazon EMR fit into ETL and analytics workflows
• How structured, semi-structured, and unstructured data shape AWS data engineering designs and service choices
• Why loosely coupled architectures can reduce lost data and client-facing errors compared to tightly coupled architectures
• How Amazon OpenSearch, AWS Lake Formation, and Amazon QuickSight, including row-level security, support governed search and visualization

Ability

• Set up an AWS account as a data engineering lab and use the AWS CLI to retrieve and store project data in S3
• Cleanse and prepare data with AWS Glue, switching to Glue notebooks when the visual editor cannot render problem data
• Run Spark-based batch steps on Amazon EMR to process CSV data sets
• Build and run workflow orchestration with AWS Step Functions, and know when Amazon MWAA fits
• Deploy a data flow as infrastructure as code with AWS CloudFormation and AWS SAM, loading results into DynamoDB
• Tear down lab resources after ML and analytics exercises to avoid unplanned resource usage