---
title: AWS Certified Data Engineer — Associate
description: The hands-on AWS data engineering certification covering ingestion, storage, transformation, security, and operations across Glue, Athena, Redshift, Kinesis, MSK, EMR, S3 Tables, and Lake Formation.
url: https://www.factualminds.com/certifications/aws-data-engineer-associate/
examCode: DEA-C01
level: associate
publishDate: 2026-05-15
updateDate: 2026-05-15
---

# AWS Certified Data Engineer — Associate

> DEA-C01 is the AWS data engineering certification that replaced the retiring Data Analytics Specialty (DAS-C01). It targets data engineers building ingestion, storage, and transformation pipelines on AWS for analytics and ML workloads.

## Why DEA-C01 exists

AWS retired the Data Analytics Specialty (DAS-C01) in 2024 and replaced it with the Data Engineer Associate (DEA-C01) to better reflect what data engineers actually do on AWS in 2024–2026:

- **Lakehouse over warehouse-only** — S3 Tables (managed Iceberg), Lake Formation, Athena, EMR Serverless feature heavily.
- **Streaming alongside batch** — Kinesis, MSK, and Managed Flink are first-class objectives.
- **Zero-ETL patterns** — Aurora to Redshift, DynamoDB to Redshift, RDS to OpenSearch show up.
- **Governance** — Lake Formation tag-based access, data quality, and PII detection are tested deeply.

## Recommended 5-week study plan

**Week 1 — Ingestion (10 hours)**
Build a Glue job from S3 to Iceberg on S3 Tables. Set up Kinesis Data Firehose to Redshift. Try Aurora zero-ETL into Redshift.

**Week 2 — Storage (8 hours)**
Walk through S3 lifecycle policies, S3 Tables with Athena queries, Redshift Serverless. Compare DynamoDB capacity modes on a sample workload.

**Week 3 — Transformation + orchestration (10 hours)**
Build a Step Functions pipeline that triggers a Glue job, runs a Redshift stored procedure, and notifies via SNS. Try MWAA on a tutorial DAG.

**Week 4 — Governance + security (8 hours)**
Configure Lake Formation tag-based access control for a Glue catalog. Run Macie on a sample bucket. Set up a Glue Data Quality DQDL rule set.

**Week 5 — Practice exams + weak areas (8 hours)**
Two Tutorial Dojo practice sets. Review every wrong answer. Re-read the AWS _Well-Architected Analytics Lens_.

## Related FactualMinds Content

- [AWS Data Analytics Services](/services/aws-data-analytics/)
- [Lakehouse on AWS Pattern](/patterns/lakehouse-on-aws/)

## Exam Details

- **Duration:** 130 minutes
- **Questions:** 65
- **Passing score:** 720 / 1000
- **Cost:** $150 USD
- **Format:** Multiple choice and multiple response
- **Validity:** 3 years
- **Recommended experience:** 2–3 years of data engineering experience and 1+ year of hands-on AWS data services

## Exam Domains

### Data Ingestion and Transformation (34%)

- Batch ingestion: AWS Glue, EMR Serverless, AWS DataSync, AWS Transfer Family
- Streaming ingestion: Kinesis Data Streams, Kinesis Data Firehose, Amazon MSK, Amazon Managed Service for Apache Flink
- Change data capture: DMS, Aurora zero-ETL to Redshift, DynamoDB zero-ETL
- Transformation: Glue Spark jobs, Glue Studio visual ETL, dbt on Glue / Redshift, EMR Serverless Spark
- Schema management: Glue Data Catalog, Glue Schema Registry, Iceberg schema evolution
- Data formats: Parquet, Apache Iceberg (via S3 Tables), Avro, ORC; compression and partitioning

### Data Store Management (26%)

- S3 storage classes: Standard, IA, One Zone, Glacier tiers; lifecycle policies
- S3 Tables (managed Apache Iceberg) for lakehouse workloads
- Amazon Redshift architecture: leader nodes, compute nodes, RA3 with managed storage, Redshift Serverless
- Redshift workload management, materialized views, federated queries, Spectrum
- DynamoDB capacity modes, partition keys, GSI/LSI, DAX, DynamoDB Streams
- OpenSearch Service: domains, indexes, ISM policies, OpenSearch Serverless trade-offs
- Aurora and RDS for transactional data feeding analytics; Aurora DSQL for distributed Postgres

### Data Operations and Support (22%)

- Orchestration: Step Functions, MWAA (Apache Airflow), EventBridge Scheduler, Glue Workflows
- Monitoring: CloudWatch, Application Signals, EMR Studio, Glue job monitoring, Redshift Advisor
- Cost optimization: Athena query cost, partition pruning, Glue worker tuning, Redshift concurrency scaling
- Data quality: Glue Data Quality (DQDL), deequ, Great Expectations on Glue
- Logging and alerting: CloudWatch Logs Insights, Athena over CloudTrail data, GuardDuty findings

### Data Security and Governance (18%)

- AWS Lake Formation: row, column, and cell-level access; tag-based access control
- Encryption: KMS-managed keys, S3 server-side encryption variants, Redshift encryption
- PII detection: Macie scans, Glue PII detection transforms
- Compliance for analytics: HIPAA-eligible data lake on Glue + S3, SOC 2 evidence collection
- Data sharing: Redshift data sharing, Lake Formation cross-account, AWS Data Exchange
- Audit trails: CloudTrail for data services, Lake Formation logging, dataset-level audit

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*Source: https://www.factualminds.com/certifications/aws-data-engineer-associate/*