AWS Certified Data Engineer – Associate Online Training

Data teams often grow AWS analytics environments service by service, which can leave inconsistent pipeline patterns, unclear security responsibilities, and uneven operational handoffs. This AWS Certified Data Engineer – Associate (DEA-C01) course gives IT Directors a structured way to standardize foundational AWS data engineering knowledge across cloud, data, and platform teams.

The course is a practical fit for IT Practitioners with at least a year of cloud experience, plus experienced AWS staff who need to validate skills against an associate-level certification path. Plan for roughly 18 hours per learner, making it realistic for a sprint-based enablement plan or a multi-week certification readiness program.

Training covers AWS storage, database, ETL, analytics, streaming, orchestration, infrastructure as code, machine learning-adjacent services, governance, and visualization topics. For team rollout, CBT Nuggets capabilities such as Playlists, Practice Exams, and Team Reporting help Training Managers assign the path, reinforce certification readiness, and track progress across learners.

IT teams complete this training to build more consistent AWS data engineering practices across pipeline design, analytics, and operations. The course maps common data workloads to AWS services such as AWS Glue, Amazon Athena, Amazon Redshift, Amazon EMR, Kinesis, MSK, EventBridge, SNS, SQS, Step Functions, MWAA, API Gateway, CloudFormation, SAM, OpenSearch, Lake Formation, and AWS visualization tools.

• Reduce pipeline delivery friction: Teams learn where AWS Glue, Athena, Redshift, and EMR fit when preparing, transforming, querying, and analyzing data.
• Support real-time and event-driven workloads: Practitioners build shared understanding of streaming and messaging patterns with Kinesis, MSK, EventBridge, SNS, and SQS.
• Improve operational consistency: Infrastructure as code topics with CloudFormation and SAM help teams move away from one-off manual configuration.
• Strengthen governance conversations: Coverage of IAM, AWS security responsibilities, and Lake Formation gives teams a common baseline for secure data access and management.

Knowledge

• AWS Certified Data Engineer – Associate exam domains reflected through AWS data engineering services and patterns
• Core storage, object storage, and database options used in AWS data workflows
• Where AWS Glue, Athena, Redshift, EMR, Kinesis, MSK, EventBridge, SNS, SQS, Step Functions, and MWAA fit in data pipeline architectures
• Infrastructure as code concepts using AWS CloudFormation and AWS SAM
• Security and governance concepts tied to IAM, AWS shared responsibility, and AWS Lake Formation

Ability

• Choose appropriate AWS services for batch, query, streaming, messaging, orchestration, and analytics scenarios
• Explain how ETL and serverless query workflows can be built with AWS Glue and Amazon Athena
• Identify when Redshift, EMR, OpenSearch, and visualization tools support analytics requirements
• Describe event-driven and real-time data movement patterns across AWS services
• Prepare more effectively for the AWS Certified Data Engineer – Associate (DEA-C01) certification exam