AWS Certified Data Engineer – Associate
Build and operate data pipelines on AWS — ingestion, transformation, storage, and governance — at associate level.
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Start a practice exam →The AWS Certified Data Engineer – Associate (DEA-C01) validates your ability to implement and manage data pipelines on AWS, and to monitor, troubleshoot, and optimize their cost and performance in line with best practices. The target candidate has roughly 2–3 years of data engineering experience plus 1–2 years of hands-on experience with AWS services.
While studying for this certification, you will learn how to design, build, and manage data pipelines and data processing solutions using AWS services. You will gain an understanding of how to collect, transform, store, and analyze data at scale while ensuring data quality, security, and reliability.
The course covers key concepts such as designing data ingestion workflows, implementing data transformation processes, managing data lakes and warehouses, and selecting the right AWS services for different data engineering requirements. You will also learn how to work with batch and streaming data processing, optimize data storage solutions, monitor data pipelines, and implement governance practices to support enterprise data platforms.
This certification also tests your understanding of building secure and efficient data architectures on AWS. It focuses on data security, access control, encryption, monitoring, and compliance considerations while working with large-scale data solutions. Additionally, it introduces best practices for designing cost-effective, scalable, and reliable data processing workflows that support analytics, machine learning, and business intelligence use cases.
Who Should Take This Certification?
Good fit for:
- Data engineers
- Cloud data engineers
- Analytics engineers
- Database engineers
- Data architects
- Solution architects working on data platforms
- ETL/ELT developers
- Business intelligence professionals
- Developers transitioning into data engineering roles
- Professionals involved in cloud-based data modernization initiatives
However, this certification is an associate-level certification designed to validate practical data engineering skills on AWS, including data ingestion, transformation, storage, processing, and monitoring. It focuses on designing and implementing data solutions rather than advanced data science, machine learning model development, or deep analytics expertise.
It helps learners build a strong foundation in AWS data services, data pipeline architecture, data processing frameworks, security practices, and operational best practices. The certification is ideal for professionals who want to demonstrate their ability to build and maintain scalable data solutions that support analytics and business decision-making.
Those seeking advanced expertise in enterprise data architecture, large-scale data platform design, or specialized analytics and machine learning solutions should consider pursuing advanced AWS certifications such as AWS Certified Solutions Architect – Professional, AWS Certified Machine Learning Engineer – Associate, or other specialized data and analytics certifications after completing this certification.
Read the exam structure guide to understand exactly what's tested, or the benefits guide to see why this certification is worth pursuing. When you're ready, take a full practice exam.
Your 8-week study plan
This plan follows the four official DEA-C01 exam domains, with study spread across 6 focused weeks and the final 2 weeks reserved entirely for full-length practice papers and targeted revision. Aim for 6–8 hours of study per week.
- Complete a study session on batch ingestion patterns — understand push vs. pull, schedule-based loads, and how AWS Glue crawlers and ETL jobs ingest data from S3, JDBC sources, and on-premises systems.
- Complete a deep dive into real-time and near-real-time streaming with Amazon Kinesis Data Streams and Kinesis Data Firehose — understand shards, retention, and delivery destinations.
- Complete a review of AWS Database Migration Service (DMS) and AWS Snow Family for bulk and offline ingestion use cases.
- Complete a session mapping ingestion tool selection to scenario types — know when to use Kinesis vs. Glue vs. DMS vs. Direct Connect vs. Transfer Family.
Week 1 checkpoint: Draw a quick diagram of a streaming ingestion pipeline end-to-end (source → Kinesis → Firehose → S3 → Glue). If you can annotate each component's role from memory, you're ready for Week 2.
- Complete an in-depth study of AWS Glue — ETL jobs (Python Shell, Spark, Ray), Glue Studio visual ETL, DynamicFrames, job bookmarks, and error handling.
- Complete a session on Amazon EMR — cluster types (EC2, Serverless, on EKS), Spark and Hive on EMR, instance groups vs. instance fleets, and cost optimisation with Spot.
- Complete a review of pipeline orchestration with AWS Step Functions and Amazon MWAA (Managed Workflows for Apache Airflow) — when to use each and how they integrate with Glue and EMR.
- Complete a scenario drill on choosing the right transformation tool — Glue vs. EMR vs. Lambda vs. Athena for a given workload type, scale, and latency requirement.
Week 2 checkpoint: For each tool (Glue, EMR, Lambda, Athena), write one sentence on the ideal workload it serves. Domain 1 is 34% of the exam — time spent here pays the biggest return.
- Complete a detailed study of Amazon S3 as a data lake foundation — storage classes, lifecycle policies, S3 Intelligent-Tiering, S3 Select, and partitioning strategies for query performance.
- Complete a session on AWS Lake Formation — blueprint-based ingestion, fine-grained access control, data filters, and the difference between Lake Formation permissions and IAM.
- Complete a deep dive into Amazon Redshift — cluster architecture, distribution styles (KEY, ALL, EVEN), sort keys, Redshift Spectrum for querying S3, Redshift Serverless, and the COPY command for bulk loading.
- Complete a review of open table formats — Apache Iceberg and Hudi on AWS, their use with Glue and Athena, and why they matter for ACID compliance in data lakes.
Week 3 checkpoint: Be able to explain the difference between a data lake (S3 + Lake Formation) and a data warehouse (Redshift), and when you'd use Redshift Spectrum to bridge the two.
- Complete a study block on Amazon DynamoDB for data engineering — partition keys, sort keys, Global Secondary Indexes, DynamoDB Streams, and DynamoDB export to S3.
- Complete a session on Amazon RDS and Amazon Aurora — when to use relational stores vs. NoSQL, Aurora Serverless, and replication patterns relevant to data pipelines.
- Complete a deep dive into Amazon Athena — query optimization with partitioning and columnar formats (Parquet, ORC), Athena Federated Query, Athena for Apache Spark, and workgroup cost controls.
- Complete a review of Amazon OpenSearch Service and Amazon ElastiCache — use cases within a data pipeline (search, real-time dashboards, caching) and when they complement S3 or Redshift.
Week 4 checkpoint: Given a workload (high-throughput key-value, ad-hoc SQL on S3, time-series analytics, full-text search) — practise selecting the right store instantly. This scenario-to-service mapping is heavily tested.
- Complete a session on Amazon CloudWatch for data pipelines — metrics, custom metrics, log groups, metric filters, alarms, dashboards, and CloudWatch Contributor Insights for Glue and EMR.
- Complete a study block on AWS CloudTrail and AWS Config — understanding audit trails for data pipeline actions, config compliance rules for data resources, and how they support incident investigation.
- Complete a troubleshooting deep dive — practice diagnosing common Glue job failures (memory, bookmark, schema mismatch), EMR step failures, Kinesis throttling, and Redshift query performance issues.
- Complete a review of cost optimisation for data pipelines — Glue DPU sizing, EMR Spot strategy, Athena cost-per-query controls, S3 storage class selection, and Redshift concurrency scaling.
Week 5 checkpoint: For each major service (Glue, EMR, Kinesis, Redshift), know the primary CloudWatch metrics to watch and the most likely cause of a job failure. Operational questions often appear as scenario-based troubleshooting.
- Complete a session on IAM for data engineering — roles, policies, resource-based policies, permission boundaries, and cross-account access patterns for data lake architectures.
- Complete a study block on encryption at rest and in transit — AWS KMS (CMKs, data keys, envelope encryption), S3 SSE options (SSE-S3, SSE-KMS, SSE-C), and encryption in Redshift and Glue.
- Complete a deep dive into AWS Glue Data Catalog governance — column-level security, tag-based access control, and how it integrates with Lake Formation for fine-grained data governance.
- Complete a review of data quality and compliance — AWS Glue Data Quality rules, AWS Macie for PII detection in S3, VPC endpoints for private data pipeline access, and relevant compliance frameworks (GDPR, HIPAA considerations on AWS).
Week 6 checkpoint: After completing all four domains, do a full concept sweep — write down one key AWS service or concept per sub-topic across all domains. Flag anything you can't recall clearly; those gaps are exactly what weeks 7 and 8 will address.
- Complete your first full-length timed practice paper under exam conditions — 65 questions, 130 minutes, no pausing. Record your per-domain score.
- Complete a thorough review of every wrong answer — understand not just the correct answer but why each distractor is wrong. For DEA-C01, most wrong answers come from confusing similar services (Glue vs. EMR, Kinesis vs. MSK, Lake Formation vs. IAM).
- Complete a targeted revision session on your two weakest domains — re-read service documentation, re-watch any video resources, and create a one-page summary of each weak area.
- Complete a second full-length practice paper by the end of the week. Compare your domain scores against Paper 1 to confirm the targeted revision is working.
- Complete a third full-length practice paper early in the week — aim to score above 80% consistently. If any domain is still below 70%, do one more focused revision block before the exam.
- Complete a service cheat-sheet review — go through the key service-to-use-case mappings across all four domains one final time, focussing on common confusables (MSK vs. Kinesis, Glue vs. EMR, Lake Formation vs. S3 bucket policies).
- Complete a final consolidation paper 2–3 days before your exam. Once you hit a stable score above 80%, stop doing full papers — light notes review only.
- Complete a pre-exam checklist: confirm your exam slot, check ID requirements, rest well the night before, and arrive (or log in) 15 minutes early. You have earned it.
How to use this plan
Weeks 1–6 are pure study — no practice papers, just building solid domain knowledge. Weeks 7 and 8 shift to exam simulation: sit full-length timed papers, review every answer explanation, and do targeted revision on weak spots. The per-domain score after each paper is your compass. Aim for above 80% consistently before booking — and remember that 15 of the 65 exam questions are unscored trial items, so do not let a few unexpected questions shake your confidence on the day.
Ready to test yourself?
Do you think you are ready? Put your knowledge to the test with a free, timed practice exam that mirrors the Data Engineer format — with instant scoring, per-domain breakdowns, and full answer explanations.
Start a practice exam →