AWS Certified Data Engineer - Associate
This course covers data engineering on AWS, with a focus on building, operating, and maintaining data pipelines using AWS Glue, Amazon Redshift, Amazon Kinesis, Amazon S3, and related AWS services. Training typically combines lectures, hands-on labs, and scenario-based exercises to connect core concepts with practical implementation.
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Description
This course covers data engineering on AWS, with a focus on building, operating, and maintaining data pipelines using AWS Glue, Amazon Redshift, Amazon Kinesis, Amazon S3, and related AWS services.
Training typically combines lectures, hands-on labs, and scenario-based exercises to connect core concepts with practical implementation. Participants study data ingestion, transformation, orchestration, storage, monitoring, security, and cost-aware design.
After completion, they can plan and support AWS data workflows and prepare for the AWS Certified Data Engineer Associate certification exam.
What You Will Learn
Module 1: Exam scope and data engineering foundations
Review the AWS Certified Data Engineer Associate (DEA-C01) exam domains, question format, and core data engineering tasks.
Cover ETL concepts, batch and streaming patterns, data volume, variety, velocity, schema design, and basic programming concepts used in pipelines.
Module 2: Data ingestion and transformation
Ingest data from Amazon S3, Amazon Kinesis Data Streams, Amazon MSK, AWS DMS, Amazon Redshift, AWS Glue, AWS Lambda, and Amazon AppFlow.
Transform and process data with AWS Glue, Amazon EMR, Lambda, Amazon Redshift, JDBC, ODBC, Python, SQL, and Apache Parquet.
Practice scheduling and event-based processing with Amazon EventBridge, AWS Step Functions, Amazon MWAA, AWS Glue workflows, Amazon SNS, and Amazon SQS.
Module 3: Data stores, models, and lifecycle management
Compare storage and database options including Amazon S3, Amazon Redshift, Amazon RDS, Amazon Aurora, Amazon DynamoDB, AWS Lake Formation, and Amazon EMR.
Build data catalogs with AWS Glue Data Catalog and AWS Glue crawlers, manage partitions, and review schema evolution.
Apply lifecycle controls such as S3 Lifecycle policies, DynamoDB TTL, versioning, Redshift load and unload operations, and Apache Iceberg table concepts.
Module 4: Operations, monitoring, security, and governance
Monitor and troubleshoot pipelines using Amazon CloudWatch Logs, AWS CloudTrail, Athena, Amazon OpenSearch Service, and CloudWatch Logs Insights.
Define data quality checks, sampling methods, consistency checks, and common performance issues.
Configure IAM roles and policies, AWS KMS encryption, AWS Secrets Manager, Lake Formation permissions, Amazon Macie, AWS Config, and audit logging.
Training combines lectures, guided labs, AWS service walkthroughs, and exam-style practice. After completion, participants should be able to plan, build, operate, secure, and troubleshoot AWS data pipelines aligned with the DEA-C01 exam scope.
Certification & Exam
Participants who complete this course can prepare for the AWS Certified Data Engineer Associate exam, code DEA-C01. The exam is an Associate-level AWS
Certification assessment with 65 multiple-choice or multiple-response questions and a 130-minute time limit. AWS reports results on a scaled score from 100 to 1,000, with 720 as the minimum passing score. Of the 65 questions, 50 are scored and 15 are unscored.
The exam can be taken through Pearson VUE at a testing center or as an online proctored exam. Successful candidates earn the AWS Certified Data Engineer Associate credential, valid for three years. Reference: official AWS certification page.
What You Will Achieve
Implement batch and streaming data ingestion from sources such as Amazon S3, Amazon Kinesis Data Streams, Amazon MSK, AWS DMS, AWS Glue, Amazon Redshift, and data APIs.
Transform and process data using AWS Glue, Amazon EMR, AWS Lambda, Amazon Redshift, SQL, Python, and common file format changes such as CSV to Apache Parquet.
Orchestrate data pipelines with Amazon EventBridge, Amazon MWAA, AWS Step Functions, AWS Glue workflows, Amazon SNS, and Amazon SQS for scheduled, event-based, and serverless workflows.
Design data storage and data models for AWS services such as Amazon Redshift, Amazon DynamoDB, Amazon RDS, Amazon S3, AWS Lake Formation, and AWS Glue Data Catalog, including schema changes, partitioning, indexing, and lifecycle rules.
Analyze and validate data using Amazon Athena, Amazon Redshift, AWS Glue DataBrew, Amazon QuickSight, Jupyter Notebooks, and data quality checks for completeness, consistency, grouping, aggregation, and sampling.
Monitor and troubleshoot data pipelines using Amazon CloudWatch Logs, AWS CloudTrail, CloudWatch Logs Insights, Amazon OpenSearch Service, and AWS service logs to support auditing, traceability, and performance review.
Apply data security and governance controls using IAM roles and policies, AWS KMS, AWS Secrets Manager, AWS Lake Formation permissions, Amazon Macie, AWS Config, encryption, masking, and least-privilege access.
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