Training course

This is a free, self-paced reading course for DEA-C01: AWS Certified Data Engineer – Associate, the associate-level credential Amazon Web Services built specifically for data engineers rather than for general architects. It covers building ingestion and transformation pipelines, choosing and managing the right data stores, operating and troubleshooting pipelines once they are running, and applying security and governance across the AWS data stack: Amazon S3, AWS Glue, Amazon Kinesis, Amazon Redshift, Amazon EMR, Amazon DynamoDB, AWS Lake Formation, AWS Lambda, Amazon Athena, and the services around them.

The course is organized as one module per official exam domain, in the order AWS publishes them, and each module carries that domain’s published weight. On DEA-C01 that weighting is lopsided in a way worth respecting: ingestion and transformation alone is 34% of the exam, more than a third, and the first two modules together account for 60%. The exam also tests judgment rather than recall, roughly 60 percent of items put you in a business situation and ask which approach fits, so the modules are written around trade-offs and constraints rather than service feature lists. Every lesson closes with its key terms and suggested further reading.

DEA-C01 Associate level 4 modules Domain-weighted Self-paced Free account

What the course covers

Data Ingestion and Transformation

Module 1 · 34%

The heaviest domain by a wide margin. Streaming and batch ingestion with Amazon Kinesis Data Streams, Amazon Data Firehose, AWS DMS, Amazon MSK, Amazon AppFlow, AWS Transfer Family, and AWS DataSync; transformation and orchestration with AWS Glue Spark, AWS Glue DataBrew, Amazon EMR, AWS Lambda, AWS Step Functions, and Amazon Managed Workflows for Apache Airflow. The module works through the decisions that actually separate answers on this exam: batch versus streaming, file format choice across Parquet, Avro, and ORC, compression, partitioning strategy, and the small-files problem.

Data Store Management

Module 2 · 26%

Matching the store to the workload: Amazon S3 as the data lake foundation, Amazon Redshift for analytics, Amazon DynamoDB for OLTP key-value access, Amazon Aurora and RDS for relational, and Amazon OpenSearch for search. Data modeling, schema cataloging with the AWS Glue Data Catalog and crawlers, and lifecycle management through S3 storage classes and lifecycle rules. The module also covers the open table formats (Apache Iceberg, Delta Lake, and Apache Hudi) and what they buy you on a lake: upserts, time travel, and schema evolution.

Data Operations and Support

Module 3 · 22%

Running pipelines in production: Amazon CloudWatch metrics, logs, and dashboards; alerting through Amazon SNS; AWS Glue Data Quality and its DQDL rules for row counts, freshness, and completeness; error handling and retries in Step Functions; Spark tuning on Amazon EMR and Glue, including broadcast joins, shuffle behavior, and diagnosing spill; and Redshift query monitoring. The module gives particular attention to reprocessing patterns, idempotency, and the gap between a job that reports success and data that is actually correct.

Data Security and Governance

Module 4 · 18%

Authentication and authorization through AWS IAM roles and IAM Identity Center; fine-grained access with AWS Lake Formation, including row-, column-, and cell-level filters; encryption with AWS KMS customer-managed keys; data classification and PII discovery with Amazon Macie; auditing through AWS CloudTrail and Redshift audit logging; and network isolation with VPC endpoints, security groups, and private subnets. Retention and tamper-evidence are handled with S3 Object Lock and lifecycle policies.

How to use it

Read in order, and do not let the module count mislead you about the workload, module 1 is a third of the exam on its own and deserves proportionally more of your time than the other three. Because DEA-C01 is scenario-based, the useful test of whether a module landed is not whether you can name a service but whether you can say why Firehose beats a Kinesis consumer for a given delivery requirement, or why Iceberg beats plain Parquet when late-arriving records need to be corrected. Read a module, then say the trade-off out loud in your own words before moving on. The course modules themselves open once you are signed in to a free Certifym account.

Build alongside the reading if you can. AWS targets candidates with roughly two to three years of data engineering experience and one to two years of hands-on AWS, and the questions assume you have operated a pipeline rather than only read about one, ingest something into S3, catalog it with Glue, query it with Athena, then put Lake Formation permissions in front of it. For exam logistics (the item count, the 130-minute window, the 720-of-1000 scaled cut score, and the registration cost) see the DEA-C01 certification guide.

DEA-C01: AWS Certified Data Engineer - Associate

One module per DEA-C01 exam domain, weighted the way AWS weights them.

A self-paced reading course for DEA-C01: AWS Certified Data Engineer – Associate, the associate-level credential AWS built for the people who move, shape, store, and guard data rather than for general architects. The course is organized as one module per official exam domain, in the order AWS publishes them, and each module carries that domain's published weight, so the time you spend maps to the time the exam spends.

You work through ingestion and transformation across Amazon Kinesis Data Streams, Amazon Data Firehose, Amazon MSK, AWS DMS, AWS Glue, Amazon EMR, AWS Lambda, AWS Step Functions, and Amazon MWAA; data store management on Amazon S3, Amazon Redshift, Amazon DynamoDB, Amazon Aurora and RDS, Amazon OpenSearch Service, and the open table formats; operations and support with Amazon CloudWatch, AWS Glue Data Quality, Spark tuning, and reprocessing patterns; and finally security and governance with AWS IAM, AWS Lake Formation, AWS KMS, Amazon Macie, and AWS CloudTrail. Every lesson ends with its key terms and further reading, so the course works as a reference on the second pass as well as a syllabus on the first.

DEA-C01 asks scenario questions in which several answers are technically workable and one fits the stated constraint, so the writing concentrates on the reasoning — which service solves which problem, and what breaks when you pick the wrong one. Pair each module with an AWS account and build what you read about. Certifym is an independent study publisher and is not affiliated with, endorsed by, or sponsored by Amazon Web Services, Inc.

Module 1: Data Ingestion and Transformation 34% of exam

The heaviest domain on DEA-C01 by a wide margin, and the one the rest of the exam assumes. You start with the shape of a pipeline — batch against streaming, and what each choice commits you to — then work through the ingestion services: Amazon Kinesis Data Streams, Amazon Data Firehose, Amazon Managed Service for Apache Flink, Amazon MSK, AWS DMS with change data capture, and the transfer services AWS AppFlow, AWS Transfer Family, and AWS DataSync.The second half is the storage and transformation decisions that separate near-identical answers on this exam: file format choice across Parquet, Avro, ORC, and JSON; compression codecs and splittability; partitioning strategy; the small-files problem; and then the transformation and orchestration engines themselves — AWS Glue Spark and DataBrew, Amazon EMR, AWS Lambda, AWS Step Functions, and Amazon Managed Workflows for Apache Airflow.

1.1 Pipeline shapes: batch, micro-batch, and streaming 5 min Free preview
1.2 Amazon Kinesis Data Streams 5 min 🔒
1.3 Amazon Data Firehose and buffered delivery 5 min 🔒
1.4 Stateful stream processing with Apache Flink 5 min 🔒
1.5 Amazon MSK and Apache Kafka ingestion 5 min 🔒
1.6 AWS DMS and change data capture 5 min 🔒
1.7 Bulk and file transfer: AppFlow, Transfer Family, DataSync 5 min 🔒
1.8 File formats: Parquet, ORC, Avro, JSON, and CSV 5 min 🔒
1.9 Compression codecs and splittability 5 min 🔒
1.10 Partitioning strategy on the data lake 5 min 🔒
1.11 The small-files problem and compaction 5 min 🔒
1.12 AWS Glue Spark ETL jobs 5 min 🔒
1.13 AWS Glue DataBrew and low-code transformation 5 min 🔒
1.14 Amazon EMR for large-scale transformation 5 min 🔒
1.15 AWS Lambda in data pipelines 5 min 🔒
1.16 Orchestrating pipelines with AWS Step Functions 5 min 🔒
1.17 Amazon MWAA and Apache Airflow 5 min 🔒

Module 2: Data Store Management 26% of exam

Matching the store to the workload. You begin with the selection framework itself, then go deep on Amazon S3 as the lake foundation, its storage classes and lifecycle rules, and the AWS Glue Data Catalog and crawlers that make objects in a bucket behave like tables. Amazon Athena follows, because how a query engine reads the catalog is what makes or breaks a lake design.The rest of the module covers the purpose-built stores — Amazon Redshift and its distribution and sort key decisions, Redshift Spectrum and federated query, Amazon DynamoDB single-table modeling, Amazon Aurora and RDS, and Amazon OpenSearch Service — and closes with the open table formats Apache Iceberg, Delta Lake, and Apache Hudi, and with data modeling and schema evolution across all of them.

2.1 Choosing the right data store 5 min 🔒
2.2 Amazon S3 as the data lake foundation 5 min 🔒
2.3 S3 storage classes and lifecycle management 5 min 🔒
2.4 The AWS Glue Data Catalog and crawlers 5 min 🔒
2.5 Amazon Athena and querying the lake 5 min 🔒
2.6 Amazon Redshift architecture and provisioning 5 min 🔒
2.7 Redshift distribution styles, sort keys, and workload management 5 min 🔒
2.8 Redshift Spectrum, federated query, and data sharing 5 min 🔒
2.9 Amazon DynamoDB modeling for data engineers 5 min 🔒
2.10 Amazon Aurora and Amazon RDS in a data pipeline 5 min 🔒
2.11 Amazon OpenSearch Service for search and log analytics 5 min 🔒
2.12 Open table formats: Apache Iceberg, Hudi, and Delta Lake 5 min Free preview
2.13 Data modeling and schema evolution 5 min 🔒

Module 3: Data Operations and Support 22% of exam

Running pipelines in production rather than building them. Amazon CloudWatch metrics, logs, and dashboards for data workloads; alerting through Amazon SNS and Amazon EventBridge; AWS Glue Data Quality and its DQDL rules for row counts, freshness, and completeness; and error handling, retries, and failure isolation in AWS Step Functions.The module then turns to the work that separates an engineer who can build a pipeline from one who can keep it correct: reprocessing patterns and idempotency, Spark tuning on AWS Glue and Amazon EMR including broadcast joins, shuffle, and spill, troubleshooting failed jobs from the evidence available, Amazon Redshift query monitoring, cost control, serving analysis to consumers, and automating deployment of the pipeline itself.

3.1 Monitoring pipelines with Amazon CloudWatch 5 min 🔒
3.2 Alerting and event routing with SNS and EventBridge 5 min 🔒
3.3 Data quality with AWS Glue Data Quality and DQDL 5 min 🔒
3.4 Error handling, retries, and failure isolation 5 min 🔒
3.5 Reprocessing, backfill, and idempotent pipelines 5 min 🔒
3.6 Tuning Spark on AWS Glue and Amazon EMR 6 min 🔒
3.7 Troubleshooting failed jobs from the available evidence 6 min 🔒
3.8 Monitoring and maintaining Amazon Redshift 5 min 🔒
3.9 Cost optimization for data workloads 5 min 🔒
3.10 Serving analysis and supporting consumers 5 min 🔒
3.11 Automating pipeline deployment and infrastructure 5 min 🔒

Module 4: Data Security and Governance 18% of exam

Authentication and authorization through AWS IAM roles and policies and AWS IAM Identity Center; fine-grained access to the lake with AWS Lake Formation, including row-, column-, and cell-level filters; encryption at rest and in transit with AWS KMS customer managed keys; and network isolation using VPC endpoints, security groups, and private subnets.The module closes with the governance side: data classification and PII discovery with Amazon Macie, audit trails through AWS CloudTrail and service-level audit logging, retention and tamper-evidence with Amazon S3 Object Lock and lifecycle policies, and controlled sharing and lineage across accounts.

4.1 IAM roles and policies for data workloads 5 min 🔒
4.2 IAM Identity Center, federation, and human access 5 min 🔒
4.3 AWS Lake Formation and fine-grained access control 5 min 🔒
4.4 Encryption at rest and in transit with AWS KMS 5 min 🔒
4.5 Network isolation, VPC endpoints, and private connectivity 5 min 🔒
4.6 Data classification and PII discovery with Amazon Macie 5 min 🔒
4.7 Audit trails with AWS CloudTrail and service logging 5 min 🔒
4.8 Retention, immutability, and backup for data platforms 5 min 🔒
4.9 Governed sharing, lineage, and the data mesh model 5 min 🔒

Frequently asked questions about the DEA-C01 training course

Is the DEA-C01 training course free?

Yes. The course costs nothing to read and opens once you are signed in to a free Certifym account, no payment and no card.

How is the course structured?

One module per official DEA-C01 exam domain, in the order AWS lists them, with each module weighted to the domain’s published percentage, so ingestion and transformation gets the largest share at 34% and security and governance the smallest at 18%. Within a module the material is broken into short lessons, followed by key terms and further reading.

Does this replace AWS’s official training?

No. AWS publishes the DEA-C01 exam guide and free digital courses on AWS Skill Builder, and those are the authoritative sources. This course is an independent study companion written to be read quickly and paired with practice questions, not a substitute for the official exam guide.

Do I need data engineering experience before starting?

AWS targets candidates with roughly two to three years of data engineering experience and one to two years of hands-on AWS. Prior AWS experience helps but is not required, and there is no prerequisite certification. If the AWS platform itself is unfamiliar, read the CLF-C02 cloud practitioner course first; if the data concepts are the unfamiliar part, that gap is the harder one to close from reading alone.

What should I do after finishing the course?

Build the pipeline end to end and break it deliberately. Land streaming and batch data in S3, transform it with Glue or EMR, catalog it, query it through Athena, then apply Lake Formation column filters and confirm they hold. Feeding a malformed batch through and watching where the failure surfaces teaches the operations domain faster than rereading it.

Is the course current with the latest DEA-C01 exam guide?

The course is built against the current DEA-C01 exam guide and its four domains at 34%, 26%, 22%, and 18%. AWS can revise an exam guide at any time; download the current version from aws.amazon.com before you schedule.

Trademark notice & independence. Certifym.net is operated by Certifym Exam Services, LLC and is not affiliated with, endorsed by, or sponsored by Amazon Web Services, Inc. AWS, Amazon Web Services, and the AWS Certified Data Engineer – Associate credential are trademarks of Amazon.com, Inc. or its affiliates. Use of these marks is solely to identify the certification for which these study materials are intended. The DEA-C01 exam and its domain structure are the property of Amazon Web Services, Inc.; candidates should download the official, current exam guide directly from aws.amazon.com.

All course content, questions, answers, and explanations on Certifym are original content created for study purposes. They are not actual AWS training materials or examination questions and are not represented as such. Studying with these materials does not guarantee a passing result on any live certification exam. Exam requirements, format, domain weights, pricing, and recertification policies are set by Amazon Web Services and may change; always verify current details on aws.amazon.com before scheduling your exam.