Intermediate Level

AWS Certified Data Engineer Associate 2026

Build production data pipelines on AWS — ETL, streaming, orchestration, and data lakes — and earn the DEA-C01 certification.

★ 4.8 (300 learners) 60 hrs total 13 modules 3 capstones

AWS EC2 S3 IAM VPC Lambda +8 more

Only 7 seats left · Next batch starts 12 Sept 2026

Download Curriculum

60 hrs: of content
13 modules: in curriculum
19 projects: hands-on builds
Intermediate: difficulty

Curriculum covers

Frameworks you'll master

14 tools & frameworks

AWS EC2 S3 IAM VPC Lambda RDS CloudFormation EKS ECS SageMaker Apache Spark Docker Grafana

Course Curriculum

13 modules · 60 hours · 24 live sessions

Storage 3 sessions

Intro: Storage
[Important] AWS Console UI Update
Set up an AWS Billing Alarm
Amazon S3
Amazon S3 Hands On
Amazon S3 Security Bucket Policy
Amazon S3 Security Bucket Policy – Hands On
Amazon S3 – Versioning
Amazon S3 – Replication
Amazon S3 Replication – Notes
Amazon S3 Storage Classes
Amazon S3 Express One Zone
Addendum: S3 Express One Zone
Amazon S3 Lifecycle Rules
Amazon S3 Event Notifications
Amazon S3 – Performance
Amazon S3 – Encryption
About DSSEKMS
Amazon S3 Default Encryption
Amazon S3 Access Points
Amazon S3 Object Lambda
Amazon S3 Storage Lens
Amazon EBS
Amazon EBS Elastic Volumes
Amazon EFS
Amazon EFS vs. Amazon EBS
AWS Backup

Database 4 sessions

Intro: Database
Amazon DynamoDB
Amazon DynamoDB in Big Data
Amazon DynamoDB Throughput (RCU & WCU)
Amazon DynamoDB Throughput (RCU & WCU) Hands On
Amazon DynamoDB Basic APIs
Amazon DynamoDB Indexes (LSI & GSI)
Amazon DynamoDB PartiQL
Amazon DynamoDB Accelerator (DAX)
Amazon DynamoDB Streams
Amazon DynamoDB Time To Live (TTL)
Amazon DynamoDB Patterns with S3
Amazon DynamoDB Security
Amazon RDS
Shared and exclusive locks in RDS
Amazon RDS Best Practices
Amazon Document DB
Amazon Memory DB for Redis
Amazon Keyspaces (for Apache Cassandra)
Amazon Neptune
Amazon Neptune Query Languages
Amazon Timestream
Amazon Redshift Intro & Architecture
Redshift Spectrum and Performance Tuning
Redshift Durability and Scaling
Redshift Distribution Styles
Redshift Data Flows and the COPY command
Redshift Integration / WLM / Vacuum
Redshift Resizing
RA3 Nodes, CrossRegion Data Sharing, Redshift ML
Redshift Security
Redshift Serverless
Redshift Materialized Views
Redshift Data Sharing / Data Shares
Redshift Lambda UDF
Redshift Federated Queries
Redshift System Tables and System Views
Redshift Data API

Migration & Transfer 1 sessions

Intro: Migration and Transfer
Application Discovery Service & Application Migration Service
AWS Database Migration Service (AWS DMS)
AWS DataSync
AWS Snow Family
AWS Transfer Family

Compute 1 sessions

Intro: Compute
EC2 in Big Data
EC2 Gravitonbased instances
AWS Lambda
Lambda Integration Part 1
Lambda Integration Part 2
AWS Lambda File Systems Mounting
AWS SAM
AWS SAM with DynamoDB Hands On
AWS Batch

Containers 1 sessions

Intro: Containers
What is Docker?
Amazon ECS
Amazon ECS Create Cluster Hands On
Amazon ECS Create Service Hands On
Amazon ECR
Amazon EKS

Analytics 5 sessions

Intro: Analytics
AWS Glue
Glue, Hive, and ETL
Modifying the Glue Data Catalog from ETL Scripts
Running ETL Jobs with Bookmarks
Glue Costs and AntiPatterns
AWS Glue Studio
AWS Glue Data Quality
AWS Glue DataBrew
Handling PII in DataBrew Transformations
AWS Glue Workflows
AWS Lake Formation
AWS Lake Formation Data Filters
Amazon Athena
Athena and Glue, Costs, and Security
Athena Performance
Athena ACID Transactions
Apache Iceberg and Athena / EMR / Glue Integration
Athena FineGrained Access to AWS Glue Data Catalog
Apache Spark
Athena, Glue, and S3 Data Lakes Hands On
Athena and CREATE TABLE AS SELECT (CTAS)
Spark Integration with Kinesis and Redshift
Spark Integration with Athena
Athena Federated Queries
Amazon EMR
EMR, AWS integration, and Storage
EMR Promises; Intro to Hadoop
EMR Serverless; EMR on EKS
Amazon Kinesis Data Streams
Amazon Kinesis Data Streams Producers
Amazon Kinesis Data Streams Consumers
Amazon Kinesis Data Streams Hands On
Amazon Kinesis Data Streams Enhanced Fan Out
Amazon Kinesis Data Streams Scaling
Amazon Kinesis Data Streams Handling Duplicates
Amazon Kinesis Data Streams Security
Amazon Data Firehose
Kinesis Data Stream Troubleshooting and Performance Tuning
Kinesis Data Analytics / Amazon Managed Service for Apache Flink (MSAF)
Update: Kinesis Data Analytics EOL
Kinesis Analytics Costs; RANDOM_CUT_FOREST
Amazon MSK
Amazon MSK Connect
Amazon MSK Serverless
Amazon Kinesis vs. Amazon MSK
Amazon OpenSearch Service
Amazon OpenSearch Service, Pt. 2
OpenSearch Index Management and Designing for Stability
Amazon OpenSearch Service Performance
Amazon OpenSearch Serverless
Amazon Quicksight
Quicksight Pricing and Dashboards; ML Insights
Quicksight Calculated Fields and LevelAware Calculations

Application Integration 1 sessions

Intro: Application Integration
Amazon SQS
Amazon Kinesis Data Streams vs. Amazon SQS
Amazon SQS Dead Letter Queues
Amazon SNS
Amazon SNS with SQS Fan Out
Amazon SNS Hands On
AWS Step Functions
AWS Step Functions: State Machines and States
Amazon AppFlow
Amazon EventBridge
Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
Full Data Engineering Pipelines

Security, Identity and Compliance 2 sessions

Intro: Security, Identity, and Compliance
Principle of Least Privilege
Data Masking and Anonymization
Key Salting
Preventing Backups or Replication to Disallowed AWS Regions
IAM Introduction: Users, Groups, Policies
IAM Users & Groups Hands On
AWS Console Simultaneous Signin
IAM Policies
IAM MFA
IAM Roles
Encryption 101
AWS KMS
Amazon Macie
AWS Secrets Manager
AWS WAF
AWS Shield
AWS Services Security Deep Dive Part 1
AWS Services Security Deep Dive Part 2
AWS Services Security Deep Dive Part 3

Networking & Content Delivery 1 sessions

Intro: Networking and Content Delivery
VPC, Subnets, Internet Gateway, NAT Gateway
NACL, Security Groups, VPC Flow Logs
VPC Peering, Endpoints, VPN, Direct Connect
VPC Cheat Sheet & Closing Comments
AWS Private Link
What is DNS?
Amazon Route 53
Amazon Route 53 Registering Domains Hands On
Amazon Route 53 Creating DNS Records Hands On
Amazon CloudFront
Amazon CloudFront S3 as Origin Hands On
Amazon CloudFront ALB/EC2 as an Origin
Amazon CloudFront Cache Invalidation

Management & Governance 1 sessions

Intro: Management and Governance
Amazon CloudWatch Metrics
Amazon CloudWatch Logs
Amazon CloudWatch Logs Unified Agent
Amazon CloudWatch Alarms
Amazon CloudTrail
AWS CloudTrail Lake
AWS Config
CloudWatch vs. CloudTrail vs. Config
AWS CloudFormation
SSM Parameter Store
AWS WellArchitected Framework & Tool
Amazon Managed Grafana
Amazon Datazone

Machine Learning 1 sessions

Intro: Machine Learning
Amazon SageMaker AI
SageMaker Feature Store
SageMaker ML Lineage Tracking
SageMaker Data Wrangler

Developer Tools 1 sessions

Intro: Developer Tools
AWS Access Keys, CLI & SDK
AWS CLI Setup on Windows
AWS CDK
AWS CodeDeploy
IMPORTANT: CodeCommit Discontinuation
AWS CodeCommit
AWS CodeBuild
AWS CodePipeline

Everything Else 1 sessions

Intro: Everything Else
AWS Budgets
AWS Cost Explorer
Amazon API Gateway
Amazon API Gateway Hands On

Download Full Curriculum

After this course, you will

Skills you carry to every job

Ingest batch and streaming data using Kinesis, MSK, DMS, and AWS Glue ingestion patterns
Design and implement serverless ETL pipelines with AWS Glue, Lambda, and Step Functions
Build and optimise data lakes on S3 with Lake Formation governance and Glue Data Catalog
Deploy and tune Amazon Redshift clusters for analytical workloads at petabyte scale
Orchestrate complex multi-step data workflows using Apache Airflow on Amazon MWAA
Monitor pipeline health and data quality using CloudWatch, Glue Data Quality, and custom metrics
Apply column-level encryption, VPC isolation, and fine-grained Lake Formation permissions
Pass the AWS Certified Data Engineer Associate DEA-C01 exam

Hands-on learning

What you'll build

3 industry capstone projects · built from scratch · yours to keep

Automated ETL Orchestration Platform

Build an end-to-end data pipeline orchestrated by Amazon MWAA (Apache Airflow): ingest data from RDS via DMS into S3, trigger Glue ETL jobs for transformation, load results into Redshift, and send Slack alerts on pipeline failures using Lambda and SNS.

Amazon MWAA AWS Glue DMS S3 Amazon Redshift Lambda SNS CloudWatch

Streaming Data Lakehouse

Create a lakehouse architecture combining a streaming S3 data lake (Parquet via Kinesis Firehose) with a Redshift Serverless warehouse. Use Glue Crawlers to keep the catalog current, Athena for ad-hoc queries, and Redshift Spectrum to query S3 directly from the warehouse.

Kinesis Firehose S3 AWS Glue Athena Redshift Serverless Lake Formation KMS

Data Quality & Observability Framework

Implement a data quality gate in a Glue ETL job using Glue Data Quality rules. Publish quality metrics to CloudWatch, trigger EventBridge rules on quality failures, and auto-quarantine bad records to a separate S3 prefix. Build a CloudWatch dashboard to track pipeline SLA compliance.

AWS Glue Glue Data Quality CloudWatch EventBridge S3 Lambda Grafana

Plus 16 guided mini-projects across the curriculum — Q&A bots, resume screeners, SQL helpers, and more.

Batch Schedule

⚡ Seats filling fast — only 7 remaining across all batches

Available Batches
Batch Type	Schedule	Duration	Next Start Date	Availability
Weekend	Sat & Sun, 10am–1pm	10 weeks	12 Sept 2026	Available
Weekday	Mon–Fri, 7–9pm	8 weeks	14 Sept 2026	Available
Fast-track	Mon–Sun, 6 hours/day	2 weeks	21 Sept 2026	Available

Frequently Asked Questions

What background do I need for the DEA-C01 exam?

2+ years of data engineering experience and familiarity with SQL, Python or Spark, and basic AWS services (S3, IAM, Lambda) are recommended. Associate-level AWS certification helps but is not mandatory.

How does the DEA-C01 compare to the Data Analytics Specialty?

The DEA-C01 is an Associate-level exam focused on building and operating pipelines. The DAS-C01 Specialty goes broader into analytics and visualisation. DEA-C01 is the better starting point for data engineers new to AWS certifications.

Is Apache Spark covered?

Yes. Glue ETL jobs run on Spark, so the course covers Spark concepts, Glue DynamicFrames vs DataFrames, job bookmarks, and PySpark transformations in the context of AWS Glue.

What is the exam format?

85 questions (multiple choice and multiple response) in 170 minutes, with a passing score of 720 out of 1000. The exam tests designing, building, securing, and monitoring data pipelines on AWS.

Does the course cover data lake security in depth?

Yes. Lake Formation column- and row-level security, tag-based access control, KMS encryption at rest, S3 VPC gateway endpoints, and Macie for PII detection are all covered with hands-on labs.

What monitoring tools are covered for data pipelines?

CloudWatch metrics and alarms, Glue job run history, EventBridge rules for pipeline events, CloudTrail for audit logging, and Grafana dashboards connected to CloudWatch are all included.

Ready to Get Started?