AWS Data Engineering Associate: 20-Hour Hands-On Certification Readiness Program
Prepare for the AWS Certified Data Engineer – Associate (DEA-C01) certification through a structured, instructor-led 10-day program covering AWS data ingestion, data lakes, AWS Glue, Spark, PySpark, Kinesis, Athena, Redshift, DynamoDB, Step Functions, Lake Formation, security, monitoring and production-oriented data engineering.
The AWS Data Engineering Associate 20-Hour Hands-On Certification Readiness Program is designed for learners who want to build practical AWS data engineering skills while preparing for the AWS Certified Data Engineer – Associate (DEA-C01) certification.
The program is delivered over 10 days × 2 hours in an instructor-led format and combines conceptual learning, guided hands-on activities, architecture discussions and certification-focused preparation.
AWS describes DEA-C01 as a certification that validates the ability to implement data pipelines, select and manage data stores, operationalize and monitor data workflows, ensure data quality, and apply security and governance practices.
1 What Is AWS Data Engineering?
AWS data engineering focuses on designing, building, operating and securing data pipelines and data platforms on Amazon Web Services.
A modern AWS data engineer may work with object storage, data lakes, streaming systems, ETL frameworks, analytical databases, serverless query engines, metadata catalogs, orchestration services and cloud security controls.
☁️ Why Learn AWS Data Engineering?
Organizations increasingly need reliable pipelines that can ingest data from applications, databases and streaming sources, transform that information at scale and make it available for analytics and business workloads.
AWS provides a broad collection of services for these workloads, making AWS data engineering a valuable skill area for data engineers, cloud engineers, analytics engineers and DevOps professionals working with data platforms.
2 AWS Certified Data Engineer – Associate (DEA-C01)
The AWS Certified Data Engineer – Associate (DEA-C01) certification validates knowledge and skills related to implementing data pipelines, data stores, data operations, data quality, security and governance on AWS.
AWS identifies the target candidate as someone with approximately 2–3 years of data engineering experience and recommends 1–2 years of hands-on experience with AWS services. This means certification preparation should ideally be supported by practical cloud and data engineering practice.
Data Ingestion
Learn batch, incremental, streaming and event-driven ingestion patterns.
Data Transformation
Build scalable ETL pipelines with Glue, Spark and PySpark.
Data Stores
Understand S3, Athena, Redshift, DynamoDB and data lake architectures.
Security & Governance
Apply IAM, encryption, Lake Formation and data governance concepts.
3 DEA-C01 Exam Details
| Exam Detail | AWS Certified Data Engineer – Associate |
|---|---|
| Exam Code | DEA-C01 |
| Certification Level | Associate |
| Exam Duration | 130 minutes |
| Questions | 65 questions |
| Question Types | Multiple choice and multiple response |
| Passing Score | 720 / 1000 |
| Exam Price | $150 USD plus applicable taxes |
| Testing Options | Pearson VUE testing center or online proctored exam |
| Certification Validity | 3 years |
AWS currently lists DEA-C01 as a 130-minute, 65-question Associate-level exam with multiple-choice and multiple-response questions. The exam fee is listed as $150 USD, with applicable taxes potentially added, and the certification is valid for three years.
AWS certification pricing and exam policies can change. Always verify the current exam information before registering.
View the official AWS Certified Data Engineer – Associate page
4 DEA-C01 Exam Domains
The official DEA-C01 exam guide organizes the certification around four major content domains.
| Domain | Weighting | Core Focus |
|---|---|---|
| Domain 1 | 34% | Data ingestion and transformation |
| Domain 2 | 26% | Data store management |
| Domain 3 | 22% | Data operations and support |
| Domain 4 | 18% | Data security and governance |
These four domains align closely with the practical structure of this Eduarn readiness program, from ingestion and ETL through data stores, operations, monitoring, security and governance.
5 AWS Data Engineering Associate Learning Objectives
- Design batch, streaming and event-driven data ingestion patterns on AWS.
- Build S3-based data lakes and select appropriate storage and data modelling approaches.
- Develop ETL pipelines using AWS Glue, Spark and PySpark.
- Catalogue, query and govern datasets using Glue Data Catalog, Athena and Lake Formation.
- Build analytical data stores using Amazon Redshift and understand DynamoDB access patterns.
- Orchestrate data pipelines using Step Functions, Lambda and AWS workflow services.
- Secure data using IAM, KMS, Secrets Manager, encryption and fine-grained permissions.
- Monitor, troubleshoot and optimize AWS data pipelines for reliability, performance and cost.
6 AWS Data Engineering Architecture
A modern AWS data platform typically combines ingestion, storage, transformation, cataloguing, analytics, orchestration, security and monitoring services.
🏗️ Example AWS Data Platform Flow
Data Sources → Kinesis / DMS / AppFlow → Amazon S3 Data Lake → AWS Glue → Glue Data Catalog → Athena / Redshift → Analytics
Orchestration can be added through Step Functions and Lambda, while IAM, KMS, Lake Formation, CloudWatch and CloudTrail provide security, governance and operational visibility.
Ingestion
Kinesis, Firehose, DMS, AppFlow and S3.
Storage
S3 data lakes with partitioning and lifecycle management.
Processing
Glue, Spark, PySpark, Lambda and EMR.
Analytics
Athena, Redshift and analytical data models.
Governance
IAM, KMS, Lake Formation, CloudTrail and data quality.
7 Detailed AWS Data Engineering Course Modules
The following 13-module structure provides a practical path from AWS data engineering fundamentals to DEA-C01 certification readiness.
| Module | Key Topics | Hands-On Activity | Time |
|---|---|---|---|
| 1. AWS & Data Engineering Foundations | AWS architecture, Regions, Availability Zones, IAM, VPC fundamentals, Console, CLI, SDK/Boto3, S3, Glue, Redshift, Kinesis and Athena. | Configure AWS CLI, create/use an IAM role and access S3 using CLI or Boto3. | 1.5 hr |
| 2. Amazon S3 & Data Lake Foundations | Buckets, objects, storage classes, prefixes, data lakes, CSV, JSON, Parquet, ORC, partitioning, compression, versioning, lifecycle, archival and encryption. | Create raw, processed and curated S3 zones and configure versioning and lifecycle controls. | 1.5 hr |
| 3. Batch Data Ingestion | Batch and incremental ingestion, AWS DMS, Full Load, CDC, AppFlow, database and SaaS ingestion. | Guided DMS/AppFlow ingestion workflow into S3 and validate landed data. | 1.5 hr |
| 4. Streaming & Event-Driven Ingestion | Kinesis Data Streams, shards, producers, consumers, Firehose, buffering and Lambda integration. | Generate streaming events and deliver them through Kinesis/Firehose to S3. | 2 hr |
| 5. AWS Glue & Data Catalog | Glue architecture, Data Catalog, databases, tables, Crawlers, classifiers, partitions and schema inference. | Crawl S3 datasets, create catalog tables and inspect/query metadata. | 1.5 hr |
| 6. ETL with Glue, Spark & PySpark | ETL/ELT, Glue jobs, Spark, DataFrames, DynamicFrames, filtering, joins, deduplication, schema handling, Parquet and partitioned output. | Build a Glue PySpark job that cleans, transforms and converts raw data into partitioned Parquet. | 2.5 hr |
| 7. Lambda & EMR Processing Patterns | Lambda transformations, event processing, triggers, EMR architecture, Spark on EMR and service selection. | Trigger Lambda from an S3 event and review an EMR processing architecture. | 1 hr |
| 8. Athena, Redshift & Data Warehousing | Athena SQL, Redshift, Redshift Serverless, COPY, Spectrum, lake vs warehouse, performance and modelling. | Query curated S3 data with Athena and load/query analytical data in Redshift. | 2 hr |
| 9. Data Modelling & NoSQL | Dimensional modelling, star/snowflake schemas, partition strategies, compression, schema evolution and DynamoDB access patterns. | Design a dimensional model and complete a DynamoDB access-pattern exercise. | 1 hr |
| 10. Orchestration & Pipeline Automation | Step Functions, state machines, Lambda automation, Glue workflows, triggers, retries and error handling. | Orchestrate Glue ETL using Step Functions with success and failure paths. | 1.5 hr |
| 11. Security, Governance & Data Quality | IAM least privilege, KMS, encryption, Secrets Manager, Lake Formation, fine-grained access and data quality. | Configure Lake Formation permissions and define Glue Data Quality rules. | 1.5 hr |
| 12. Operations, Monitoring & Optimization | CloudWatch, CloudTrail, SNS alerting, troubleshooting, retries, performance tuning and cost optimization. | Configure monitoring/alerting and diagnose a failed or degraded pipeline. | 1 hr |
| 13. Capstone & DEA-C01 Exam Readiness | Architecture review, service selection, DEA-C01 domain mapping, troubleshooting and exam-readiness guidance. | Integrate and validate the secure and monitored AWS data platform built throughout the program. | Included |
8 Module 1: AWS & Data Engineering Foundations
Start by understanding the AWS environment and the role of core services in a data engineering architecture.
- AWS Regions and Availability Zones
- IAM users, roles and policies
- VPC fundamentals
- AWS Management Console
- AWS CLI
- AWS SDK and Boto3
- S3, Glue, Redshift, Kinesis and Athena overview
The goal is not simply to memorize commands, but to become comfortable interacting with AWS resources programmatically.
9 Module 2: Amazon S3 & Data Lake Foundations
Amazon S3 is a foundational component of many AWS data lake architectures. This module focuses on organizing data into logical zones and selecting efficient storage formats and lifecycle strategies.
Raw Zone
Store source data with minimal transformation.
Processed Zone
Clean, transform and standardize datasets.
Curated Zone
Prepare trusted datasets for analytics and consumption.
Understand when to use formats such as CSV, JSON, Parquet and ORC, and how partitioning, compression, lifecycle policies and encryption influence cost and performance.
10 Module 3: Batch Data Ingestion
Batch ingestion is important when data is transferred periodically from databases, SaaS platforms or other source systems into an AWS data platform.
AWS DMS
Understand full-load and change data capture concepts.
AWS AppFlow
Learn managed SaaS and application data ingestion.
S3 Ingestion
Land source files into structured data lake zones.
The practical objective is to understand which ingestion mechanism best matches the source system, frequency, volume and latency requirements.
11 Module 4: Streaming & Event-Driven Data Ingestion
Not every workload can wait for scheduled batch processing. Streaming architectures support applications that need data to move continuously through an ingestion pipeline.
⚡ Streaming Pipeline Example
Event Producer → Kinesis Data Streams → Kinesis Data Firehose → Amazon S3 → Analytics
Lambda can be integrated into event-driven workflows for lightweight processing and automation.
You will work with streaming events and understand concepts such as shards, producers, consumers, buffering and delivery.
12 Module 5: AWS Glue & Data Catalog
AWS Glue provides managed capabilities for data integration, cataloguing and ETL. The Glue Data Catalog helps organize metadata about datasets so that data can be discovered and queried consistently.
Crawl S3 datasets, create catalog tables, inspect schemas, understand partitions and query metadata.
Cataloguing is particularly important in data platforms because storing data is only one part of the problem. Engineers also need reliable metadata, schemas and discovery mechanisms.
13 Module 6: ETL with AWS Glue, Spark & PySpark
This is one of the core hands-on sections of the program. You will build a scalable ETL pipeline using AWS Glue and PySpark.
Clean
Remove invalid records and standardize incoming data.
Transform
Filter, join, deduplicate and reshape datasets.
Convert
Convert source data into efficient Parquet datasets.
Partition
Organize output for efficient downstream querying.
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("EduarnDataEngineering") \
.getOrCreate()
df = spark.read.option("header", True).csv(
"s3://example-bucket/raw/"
)
clean_df = df.dropDuplicates()
clean_df.write \
.mode("overwrite") \
.partitionBy("year") \
.parquet("s3://example-bucket/processed/")The goal is to understand the data transformation workflow rather than simply copy code. You should be able to explain why a transformation is required and how its design affects scalability and cost.
14 Module 7: Lambda & EMR Processing Patterns
Different workloads require different processing services. A strong data engineer should understand when to choose Lambda, Glue or EMR.
| Service | Typical Use |
|---|---|
| Lambda | Lightweight event processing, automation and short-running transformations. |
| AWS Glue | Managed serverless ETL and data integration workloads. |
| Amazon EMR | Large-scale distributed processing using frameworks such as Spark. |
Learn service-selection scenarios. The important question is often not "Can this service process the data?" but "Which AWS service is the most appropriate for this workload?"
15 Module 8: Athena, Redshift & Data Warehousing
Data engineers must understand how data is consumed after it has been ingested and transformed.
Amazon Athena
Query data directly in S3 using serverless SQL.
Amazon Redshift
Build analytical data warehouse workloads.
Redshift Spectrum
Work with data stored externally in S3.
You will query curated S3 data with Athena and explore analytical workloads using Redshift, including concepts such as COPY, distribution, sorting and dimensional modelling.
16 Module 9: Data Modelling & NoSQL
Choosing a data store is a fundamental data engineering decision. The correct choice depends on workload requirements, access patterns, scale, latency, consistency and analytical needs.
| Concept | What to Understand |
|---|---|
| Dimensional Modelling | Fact tables, dimension tables and analytical schemas. |
| Star Schema | Simple analytical structure optimized for reporting. |
| Snowflake Schema | Normalized dimensions and modelling trade-offs. |
| DynamoDB | Partition keys, sort keys and access-pattern design. |
| Schema Evolution | Managing changes to datasets and data structures. |
AWS's DEA-C01 Data Store Management domain specifically covers choosing data stores, data cataloguing, data lifecycle management, data models and schema evolution.
17 Module 10: Orchestration & Pipeline Automation
Production data pipelines require more than individual ETL jobs. They need scheduling, dependencies, retries, failure handling and reliable workflow orchestration.
🔄 Example Orchestrated Pipeline
Start → Validate Input → Run Glue ETL → Validate Output → Success
If a processing step fails, the workflow can follow a failure path, retry the operation or trigger an alert.
You will work with AWS Step Functions and understand how Lambda, Glue workflows, event-driven triggers and retry strategies can be combined into resilient data pipelines.
18 Module 11: Security, Governance & Data Quality
Data engineering must include security from the beginning. Data platforms can contain sensitive business and customer information, making authentication, authorization, encryption, governance and auditability essential.
IAM
Apply least-privilege authentication and authorization.
KMS
Understand encryption key management.
Lake Formation
Apply fine-grained permissions to data resources.
Data Quality
Validate datasets with quality rules and checks.
DEA-C01 includes security and governance tasks covering authentication, authorization, encryption and masking, audit logging, privacy and governance.
19 Module 12: Operations, Monitoring & Optimization
Building a pipeline is only the beginning. Production data engineering requires continuous monitoring, troubleshooting, performance optimization and cost management.
- Monitor pipeline logs and metrics.
- Configure CloudWatch alarms.
- Use CloudTrail for audit visibility.
- Design appropriate retry strategies.
- Diagnose failed Glue and Kinesis workloads.
- Investigate Redshift performance issues.
- Optimize S3, Athena, Glue and Redshift costs.
- Validate data quality and pipeline reliability.
AWS identifies monitoring, troubleshooting, optimization and data quality as important parts of the DEA-C01 role.
20 Module 13: Capstone & DEA-C01 Exam Readiness
The final module brings the individual services together into an end-to-end architecture and connects the hands-on work to DEA-C01 certification objectives.
🏆 Capstone Architecture
Source Systems → DMS / Kinesis / AppFlow → S3 Data Lake → Glue ETL → Glue Data Catalog → Athena / Redshift → Step Functions → CloudWatch → Security & Governance
The capstone is designed to reinforce service selection, architecture reasoning, security, monitoring, troubleshooting and certification readiness.
21 Hands-On Projects Included in the Program
Practical exercises help transform AWS service knowledge into usable data engineering skills.
🪣 S3 Data Lake
Build raw, processed and curated S3 zones with lifecycle, versioning and encryption controls.
📥 Batch Ingestion
Build a guided ingestion workflow using DMS or AppFlow.
⚡ Streaming Pipeline
Generate streaming events and deliver them to S3 using Kinesis services.
⚙️ Glue PySpark ETL
Clean, transform, deduplicate and convert raw data into partitioned Parquet.
📊 Analytics Layer
Query curated data using Athena and explore analytical workloads with Redshift.
🔄 Orchestrated Pipeline
Build a Step Functions workflow with success, retry and failure paths.
22 AWS Data Engineering Certification Roadmap
AWS Fundamentals → SQL & Python → S3 → Data Lakes → Ingestion → Glue → Spark/PySpark → Athena → Redshift → DynamoDB → Orchestration → Security → Monitoring → Projects → DEA-C01
☁️ AWS Fundamentals
Learn IAM, networking, storage, compute and core AWS concepts.
🐍 Python & SQL
Build practical programming and data-querying foundations.
🪣 S3 & Data Lakes
Learn object storage, file formats, partitions and lifecycle management.
📥 Data Ingestion
Learn batch, incremental, streaming and event-driven ingestion.
⚙️ Glue & PySpark
Build scalable ETL pipelines and transform datasets.
📊 Analytics
Learn Athena, Redshift and analytical data modelling.
🔐 Security & Operations
Learn IAM, KMS, governance, monitoring, quality and optimization.
🏆 DEA-C01
Review exam domains, practice scenarios and prepare for certification.
23 10-Day AWS Data Engineering Study Plan
| Day | Focus | Hands-On Outcome |
|---|---|---|
| Day 1 | AWS & Data Engineering Foundations | AWS CLI, IAM role and S3 access |
| Day 2 | S3 & Data Lake Foundations | Raw, processed and curated S3 zones |
| Day 3 | Batch Data Ingestion | DMS/AppFlow ingestion workflow |
| Day 4 | Streaming & Event-Driven Ingestion | Kinesis/Firehose streaming pipeline |
| Day 5 | Glue & Data Catalog | S3 crawler and catalog tables |
| Day 6 | Glue, Spark & PySpark ETL | Partitioned Parquet ETL pipeline |
| Day 7 | Lambda, EMR, Athena & Redshift | Processing and analytical workloads |
| Day 8 | Data Modelling & NoSQL | Dimensional model and DynamoDB exercise |
| Day 9 | Orchestration, Security & Governance | Step Functions and Lake Formation workflows |
| Day 10 | Operations, Capstone & DEA-C01 Readiness | End-to-end architecture and exam preparation |
24 AWS Data Engineering Skills You Should Build
Data Ingestion
Batch, CDC, streaming and event-driven architectures.
ETL
Glue, Spark, PySpark, transformations and data quality.
Data Stores
S3, Redshift, Athena and DynamoDB.
Orchestration
Step Functions, Lambda, workflows and retries.
Security
IAM, KMS, encryption, Secrets Manager and governance.
Operations
CloudWatch, CloudTrail, troubleshooting and optimization.
25 AWS Data Engineer Skills Employers Look For
- AWS data engineering architecture
- Amazon S3 and data lakes
- SQL and analytical querying
- Python and Boto3
- AWS Glue
- Apache Spark and PySpark
- Amazon Kinesis
- AWS DMS
- Amazon Athena
- Amazon Redshift
- DynamoDB
- Data modelling
- Data cataloguing
- Pipeline orchestration
- IAM and cloud security
- Data quality and governance
- CloudWatch monitoring
- Cost and performance optimization
26 AWS Data Engineering Career Paths
AWS data engineering skills can support multiple technology career paths, depending on your experience and specialization.
Data Engineer
Design, build and maintain scalable data pipelines.
Cloud Data Engineer
Build cloud-native data platforms on AWS.
Data Platform Engineer
Develop reusable platforms for data ingestion and analytics.
Analytics Engineer
Build analytical data models and consumption layers.
Cloud Engineer
Combine cloud infrastructure with data platform engineering.
Data Security Engineer
Focus on governance, permissions, encryption and compliance.
27 AWS Data Engineering Certification vs Hands-On Experience
Certification can validate knowledge against a defined set of objectives, but practical data engineering requires experience designing, building and troubleshooting real pipelines.
Certification
Validates structured knowledge of AWS data engineering.
Hands-On Skills
Demonstrate that you can build and operate pipelines.
Projects
Give you portfolio evidence of practical engineering ability.
Experience
Develop production troubleshooting and architecture skills.
Use certification preparation as a structured learning path, but spend time building real pipelines, querying real datasets, troubleshooting failures and understanding architectural trade-offs.
28 AWS Data Engineering Projects for Your Portfolio
- Build a three-zone S3 data lake.
- Create a batch ingestion pipeline using AWS DMS.
- Build a SaaS ingestion workflow with AWS AppFlow.
- Create a Kinesis streaming pipeline.
- Build a Glue crawler and centralized Data Catalog.
- Create a Glue PySpark ETL pipeline.
- Convert CSV data into partitioned Parquet datasets.
- Build an Athena analytical query layer.
- Create a Redshift analytical warehouse.
- Design a DynamoDB access pattern.
- Build a Step Functions data workflow.
- Implement IAM and Lake Formation permissions.
- Add CloudWatch monitoring and alerting.
- Build an end-to-end AWS data engineering capstone.
29 How to Prepare for AWS DEA-C01
- Read the current AWS DEA-C01 exam guide.
- Understand all four content domains.
- Build a strong foundation in AWS services.
- Practice S3 and data lake architectures.
- Build ingestion and ETL pipelines.
- Practice Glue, Spark and PySpark.
- Learn Athena, Redshift and DynamoDB use cases.
- Study orchestration and event-driven workflows.
- Practice security, governance and data quality scenarios.
- Learn monitoring, troubleshooting and cost optimization.
- Build an end-to-end capstone.
- Review weak areas before scheduling the exam.
AWS provides an official DEA-C01 exam guide containing the exam domains, task statements and in-scope AWS services. The exam guide should be treated as the primary reference when preparing for the certification.
30 Common AWS Data Engineer Certification Mistakes
- Studying only service definitions without building pipelines.
- Ignoring the official DEA-C01 exam domains.
- Focusing only on S3 and Glue while ignoring operations.
- Not understanding batch vs streaming ingestion.
- Memorizing service names instead of learning service selection.
- Ignoring data modelling and storage decisions.
- Skipping security and governance topics.
- Not practicing troubleshooting scenarios.
- Ignoring cost and performance optimization.
- Preparing without hands-on AWS experience.
AWS periodically reviews certification exams and publishes revisions when exam content changes. Check the official DEA-C01 exam guide before beginning or finalizing your certification preparation.
31 What Do People Search for About AWS Data Engineering?
This program is designed around common learning and certification-intent searches related to AWS data engineering, data pipelines, AWS Glue, data lakes and DEA-C01 preparation.
🔎 Popular AWS Data Engineering Searches
AWS Data Engineering AWS Data Engineer certification AWS Certified Data Engineer Associate AWS DEA-C01 DEA-C01 certification DEA-C01 exam DEA-C01 study guide AWS Data Engineer Associate AWS Data Engineer certification 2026 AWS data engineering course AWS data engineering training AWS data engineering roadmap AWS data engineer roadmap AWS data engineer certification cost DEA-C01 exam cost DEA-C01 exam questions AWS Glue certification AWS Glue ETL AWS Glue PySpark AWS data lake Amazon S3 data lake AWS data pipeline AWS ETL pipeline AWS Kinesis data engineering AWS DMS data engineering Amazon Athena data engineering Amazon Redshift data engineering DynamoDB data engineering AWS Step Functions data pipeline AWS Lake Formation AWS data governance AWS data engineering projects AWS data engineer career learn AWS data engineering AWS data engineer hands-on course AWS data engineer interview preparation AWS data engineering certification roadmap32 Is AWS Data Engineer Associate Certification Worth It?
The AWS Certified Data Engineer – Associate can be a useful credential for professionals targeting data engineering, cloud data and analytics-oriented roles on AWS.
Its value is strongest when the certification is combined with practical experience in data ingestion, ETL, data stores, orchestration, security, monitoring and cloud architecture.
Do not learn AWS data engineering only to answer certification questions. Learn how to design a reliable pipeline, explain service-selection decisions, troubleshoot failures and optimize data workloads for performance and cost.
33 AWS Data Engineering vs Traditional Data Engineering
| Area | AWS-Oriented Data Engineering | Traditional Focus |
|---|---|---|
| Infrastructure | Cloud-managed services | Often self-managed infrastructure |
| Storage | S3-based object storage and data lakes | File systems and database storage |
| ETL | Glue, Spark, PySpark and serverless processing | Dedicated ETL servers and frameworks |
| Streaming | Kinesis and event-driven AWS services | Streaming platforms and custom infrastructure |
| Analytics | Athena, Redshift and AWS analytics services | Traditional warehouses and database platforms |
| Operations | CloudWatch, CloudTrail and managed-service monitoring | Infrastructure and application monitoring systems |
34 AWS Data Engineering Career Roadmap
AWS Fundamentals → SQL → Python → S3 → Data Lakes → ETL → Glue → Spark/PySpark → Analytics → Data Modelling → Orchestration → Security → Monitoring → Projects → DEA-C01 → Data Engineering Experience
Once you have the core AWS data engineering foundation, expand into advanced distributed processing, infrastructure automation, data governance, analytics engineering, cloud architecture and platform engineering according to your career goals.
35 Final AWS Data Engineering Certification Roadmap
Learn AWS → Master S3 → Build Data Lakes → Learn Batch & Streaming → Master Glue → Practice Spark/PySpark → Learn Athena & Redshift → Understand DynamoDB → Orchestrate Pipelines → Secure Data → Monitor & Optimize → Build Projects → Prepare DEA-C01 → Grow as a Data Engineer
AWS data engineering combines cloud infrastructure, data architecture, programming, distributed processing, analytics, security and operations. A structured certification-readiness program can help organize these skills into a practical learning journey.
The Eduarn AWS Data Engineering Associate 20-Hour Hands-On Certification Readiness Program is designed around this progression, with instructor-led learning, guided hands-on activities and an end-to-end capstone.
For the latest official certification information, exam requirements and current DEA-C01 objectives, always refer to AWS Certification resources.
36 Frequently Asked Questions About AWS Data Engineer Associate
What is AWS Certified Data Engineer – Associate?
AWS Certified Data Engineer – Associate is an AWS Associate-level certification that validates skills related to implementing data pipelines, managing data stores, operating data workflows, ensuring data quality and applying security and governance.
What is the AWS Data Engineer Associate exam code?
The current exam code is DEA-C01.
How long is the DEA-C01 exam?
AWS currently lists the exam duration as 130 minutes.
How many questions are on DEA-C01?
The exam contains 65 questions, using multiple-choice and multiple-response formats.
What is the passing score for DEA-C01?
AWS currently lists a minimum passing score of 720 out of 1000.
How much does the AWS Data Engineer Associate exam cost?
AWS currently lists the Associate-level exam price at $150 USD, with applicable taxes potentially added.
How long is AWS Data Engineer certification valid?
The AWS Certified Data Engineer – Associate certification is currently valid for three years.
What are the DEA-C01 exam domains?
The four domains are Data Ingestion and Transformation, Data Store Management, Data Operations and Support, and Data Security and Governance.
What AWS services should I learn for DEA-C01?
Key services in this program include Amazon S3, AWS Glue, AWS Glue Data Catalog, Kinesis, Firehose, DMS, AppFlow, Athena, Redshift, DynamoDB, Lambda, Step Functions, Lake Formation, IAM, KMS, Secrets Manager, CloudWatch and CloudTrail.
Is AWS Glue important for the Data Engineer Associate exam?
AWS Glue is an important data integration and ETL technology for AWS data engineering. Learners should understand Glue architecture, crawlers, the Data Catalog, ETL jobs and Spark/PySpark processing.
Do I need Python for AWS Data Engineering?
Python is highly useful for practical AWS data engineering, especially when working with Boto3, PySpark and automation. AWS notes that programming concepts are part of the DEA-C01 exam role, although the exam is not intended to test programming-language-specific syntax.
Is AWS Data Engineer Associate suitable for beginners?
The certification is an Associate-level credential and AWS describes the target candidate as having substantial data engineering and AWS experience. Beginners can use DEA-C01 as a learning target, but should build AWS, SQL, Python and data engineering fundamentals before attempting the exam.
What should I learn before DEA-C01?
Build foundations in AWS, SQL, Python, data engineering, data storage, ETL, data modelling and cloud security. Hands-on practice with S3, Glue and analytical data services is particularly valuable.
Can AWS Data Engineer certification help with a data engineering career?
It can provide a structured way to validate AWS data engineering knowledge. However, employers also value practical projects, programming, SQL, cloud architecture, troubleshooting and production experience.
What should I do after AWS Data Engineer Associate?
Continue building production-oriented data engineering experience, deepen your AWS architecture skills and consider advanced specialization in data platforms, analytics, security, machine learning or cloud architecture.
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AWS Data Engineering • AWS Certified Data Engineer Associate • DEA-C01 • AWS Data Engineer Certification • AWS Glue • AWS Glue PySpark • Amazon S3 Data Lake • AWS Kinesis • AWS DMS • Amazon Athena • Amazon Redshift • DynamoDB • AWS Step Functions • Lake Formation • AWS Data Governance • Cloud Data Engineering • Data Engineering Career
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