AWS Certified Data Engineer – Associate (DEA-C01)
An exam-aligned certification program that trains you to implement, operate, and secure data pipelines on AWS — ingesting and transforming data, choosing and managing data stores, monitoring and troubleshooting pipelines, and enforcing security and governance. Built around the official DEA-C01 exam guide with hands-on labs across Glue, Athena, Redshift, EMR, Kinesis, and S3.
The AWS Certified Data Engineer – Associate (DEA-C01) program is an instructor-led certification course that prepares you for AWS's data engineering credential. Every module is mapped directly to the official DEA-C01 exam guide, so what you practice is exactly what the exam validates.
The exam validates the ability to implement data pipelines and to monitor, troubleshoot, and optimize cost and performance issues per best practices: ingesting and transforming data and orchestrating pipelines while applying programming concepts; choosing an optimal data store, designing data models, cataloging schemas, and managing data lifecycles; operationalizing, maintaining, and monitoring pipelines and ensuring data quality; and implementing authentication, authorization, encryption, privacy, governance, and logging. You build all of this hands-on with the core services — AWS Glue, Amazon Athena, AWS Lake Formation, Amazon Redshift, Amazon EMR, Amazon Kinesis, and Amazon S3 — and the modern additions the exam now covers, from Apache Iceberg and vector indexes to LLM-assisted data processing.
On completion you will be ready to sit the DEA-C01 exam and to take on data engineering roles on AWS.
Exam at a Glance
- Certification: AWS Certified Data Engineer – Associate | Offered by: Amazon Web Services (AWS)
- Exam code: DEA-C01 | Level: Associate
- Recommended experience: 2–3 years of data engineering experience, plus 1–2 years of hands-on experience with AWS services
- Format: 65 questions (50 scored + 15 unscored); multiple choice and multiple response | Duration: 130 minutes
- Passing score: 720 / 1000 (pass/fail)
- Delivery: Pearson VUE test center or online proctored | Recertification: every 3 years
Exam Domains & Weightings
- Domain 1 — Data Ingestion and Transformation — 34%
- Domain 2 — Data Store Management — 26%
- Domain 3 — Data Operations and Support — 22%
- Domain 4 — Data Security and Governance — 18%
Course Highlights
- 100% aligned to the official DEA-C01 exam guide
- Hands-on pipelines with Glue, Athena, Lake Formation, Redshift, EMR, Kinesis, and S3
- Streaming and batch ingestion, orchestration with MWAA and Step Functions
- Modern exam topics: Apache Iceberg, vector indexes (HNSW/IVF), LLM-assisted processing
- IaC-driven delivery with CloudFormation, CDK, and SAM
- Full-length mock exams with explanations
Learning Outcomes
- Ingest streaming and batch data and orchestrate resilient pipelines
- Transform data at scale with EMR, Glue, Lambda, and Redshift
- Choose optimal data stores, design schemas, and manage data lifecycles
- Operate, monitor, and troubleshoot pipelines and enforce data quality
- Secure pipelines with IAM, KMS, Lake Formation, and audit logging
- Pass the DEA-C01 exam with confidence
Job Opportunities
- AWS Data Engineer
- Data Engineer
- Analytics Engineer
- Big Data Engineer
- ETL Developer
- Data Platform Engineer
Material Includes
- Class recordings
- Lab guides
- Mock exams
- Certificate
Who Can Join
- Data Engineers & ETL Developers
- Developers & Analysts moving to AWS
- Students & IT Professionals
Estimated Time: 15–16 Hours
1.1 Perform data ingestion- Read from streaming sources (Amazon Kinesis, Amazon MSK, DynamoDB Streams, AWS DMS, AWS Glue, Amazon Redshift) and batch sources (Amazon S3, Glue, EMR, DMS, Redshift, Lambda, Amazon AppFlow)
- Configure batch ingestion; consume data APIs; set up schedulers (Amazon EventBridge, Apache Airflow, time-based) and event triggers (S3 Event Notifications, EventBridge)
- Handle throttling and rate limits; manage fan-in/fan-out for streaming; describe pipeline replayability; define stateful vs. stateless transactions
- Optimize container usage (Amazon EKS, ECS); connect to sources via JDBC/ODBC; integrate data from multiple sources; optimize processing costs
- Implement transformation services (Amazon EMR, AWS Glue, Lambda, Redshift); convert formats (e.g., CSV to Apache Parquet); troubleshoot transformation failures
- Create data APIs; define volume/velocity/variety; integrate large language models (LLMs) for data processing
- Build workflows with Lambda, EventBridge, Amazon MWAA, AWS Step Functions, Glue workflows; design for performance, availability, scalability, resiliency, fault tolerance; maintain serverless workflows; send alerts (Amazon SNS, SQS)
- Optimize code and Lambda concurrency; use languages/frameworks (Python, SQL, Scala, R, Java, Bash, PowerShell); apply software engineering best practices (version control, testing, logging, monitoring)
- Use Infrastructure as Code (AWS CloudFormation, AWS CDK) and AWS SAM for serverless pipelines; describe CI/CD, distributed computing, data structures, and algorithms
- Build a streaming (Kinesis) and batch (S3 + Glue) ingestion pipeline with EventBridge triggers
- Orchestrate an end-to-end workflow with Step Functions and deploy it via CDK
Estimated Time: 11–12 Hours
2.1 Choose a data store- Select and configure storage for cost, performance, and access patterns (Amazon Redshift, EMR, Lake Formation, RDS, DynamoDB, Kinesis Data Streams, MSK)
- Apply services to use cases (e.g., HNSW indexing with Aurora PostgreSQL, Amazon MemoryDB for fast key/value access); manage migration and remote access (Redshift federated queries, materialized views, Redshift Spectrum); manage locks
- Manage open table formats (Apache Iceberg); describe vector index types (HNSW, IVF)
- Build and reference technical data catalogs (AWS Glue Data Catalog, Apache Hive metastore); use Glue crawlers to discover schemas and populate catalogs; synchronize partitions; create and manage business catalogs (Amazon SageMaker Catalog)
- Load/unload between S3 and Redshift; manage S3 Lifecycle policies and storage tiering; expire aged data; manage S3 versioning and DynamoDB TTL; delete data for legal/business requirements; protect data with resiliency and availability
- Design schemas for Redshift, DynamoDB, and Lake Formation; handle changing data characteristics; perform schema conversion (AWS SCT, AWS DMS Schema Conversion); establish data lineage; apply indexing, partitioning, and compression best practices; describe vectorization concepts (Amazon Bedrock knowledge base)
- Catalog a data lake with Glue crawlers and query it via Redshift Spectrum
- Design an Iceberg-backed table with lifecycle and partitioning strategies
Estimated Time: 9–10 Hours
3.1 Automate data processing- Orchestrate pipelines (Amazon MWAA, Step Functions); troubleshoot managed workflows; call SDKs; process data with EMR, Redshift, Glue; prepare data (AWS Glue DataBrew, Amazon SageMaker Unified Studio); query with Amazon Athena; automate with Lambda and EventBridge
- Visualize data (DataBrew, Amazon QuickSight); verify and clean data (Lambda, Athena, QuickSight, Jupyter, SageMaker Data Wrangler); use SQL in Redshift and Athena; use Athena Spark notebooks; compare provisioned vs. serverless; define aggregation, rolling averages, grouping, pivoting
- Extract logs for audits; deploy logging and monitoring; send alerts; troubleshoot performance; use AWS CloudTrail and Amazon CloudWatch Logs; analyze logs (Athena, EMR, Amazon OpenSearch Service, CloudWatch Logs Insights)
- Run data quality checks; define quality rules and investigate consistency (DataBrew); describe data sampling techniques; implement data skew mechanisms
- Automate a Glue/Athena pipeline with MWAA and alerting
- Build data quality rules in DataBrew and monitor with CloudWatch Logs Insights
Estimated Time: 7–8 Hours
4.1 Apply authentication mechanisms- Update VPC security groups; create and manage IAM groups, roles, endpoints; rotate credentials (AWS Secrets Manager); set up IAM roles for access; apply IAM policies (S3 Access Points, AWS PrivateLink); use SageMaker Unified Studio domains and projects
- Create custom IAM policies; store credentials (Secrets Manager, Systems Manager Parameter Store); manage database access (Redshift); manage permissions via AWS Lake Formation; apply role-based, tag-based, and attribute-based authorization following least privilege
- Apply data masking and anonymization; encrypt/decrypt with AWS KMS; configure encryption across account boundaries; enable encryption in transit
- Track API calls with AWS CloudTrail; store logs in CloudWatch Logs; use CloudTrail Lake for centralized queries; analyze logs (Athena, CloudWatch Logs Insights, OpenSearch)
- Grant data sharing permissions (Redshift); identify PII (Amazon Macie with Lake Formation); prevent data replication to disallowed Regions; track configuration changes (AWS Config); maintain data sovereignty; manage access via SageMaker Catalog; describe governance frameworks and data sharing patterns
- Secure a data lake with Lake Formation permissions, KMS encryption, and masking
- Audit pipeline activity with CloudTrail Lake and Athena
Estimated Time: 3–4 Hours
Learning Objectives:- Master the 65-question, 130-minute format and pacing
- Reason service-selection questions like the exam: cost, performance, and access patterns
- Domain-by-domain review keyed to the official weightings
- Question strategy and elimination techniques
- Recertification path: 3-year validity
- Full-length timed mock exams with detailed review
- Capstone: end-to-end pipeline — ingest, transform, catalog, govern, and monitor
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