AWS Certified Data Engineer Associate 2026 – Hands On!

Students : 96,335
Duration : 21.5 hours
Avg Rating : 4.6 (12,514 ratings)
Original Price : $119.99
Discount Price : $13
Instructor / Provider : Frank Kane, Stephane Maarek, Udemy
Course Type : Self Paced Online Course. Lifetime Access
Coupon : Click on ENROLL NOW to apply discount code

You’ll Learn How to

• Increase your chances of passing the AWS Certified Data Engineer – Associate (DEA-C01) exam.
• Design and implement robust data pipelines on AWS to ingest, store, and transform data.
• Select and design optimal data stores, data models, schemas, and data lifecycles.
• Operationalize and orchestrate data pipelines using tools like EventBridge, Airflow, AWS Step Functions, and more.
• Apply security, governance, and privacy best practices to AWS-based data solutions.
• Build data lakes using S3, Glue, Redshift, and other AWS services.
• Process both batch and streaming data with services like Kinesis, EMR, Lambda, and containers.

More Courses by Stephane Maarek

This course navigates the complex landscape of AWS data engineering by diving deep into an extensive range of AWS tools and technologies, including:

• Streaming & Transformation: Work with Amazon Kinesis and Amazon Managed Streaming for Apache Kafka (MSK).
• Message Queues: Use Simple Queue Service (SQS) for queuing and managing messages.
• Orchestration: Automate pipelines with Amazon AppFlow, EventBridge, AWS Step Functions, and Managed Workflows for Apache Airflow (MWAA).
• Data Migration: Transition data seamlessly with AWS Database Migration Service (DMS), DataSync, Snow Family, and Transfer Family.
• Data Lakes: Store massive datasets with S3 and manage their lifecycles efficiently.
• Transactional Databases: Optimize queries with DynamoDB, DocumentDB, Keyspaces, and MemoryDB.
• Serverless Integration: Connect systems using AWS Lambda for scalable solutions.
• Unstructured Data: Make unstructured data accessible with AWS Glue, Glue DataBrew, and Lake Formation.
• Batch Data Processing: Process data at scale with Elastic MapReduce (EMR), including Apache Spark.
• Machine Learning at Scale: Implement advanced algorithms using Amazon SageMaker.
• Search & Analysis: Analyze massive datasets with Amazon OpenSearch (formerly Elasticsearch).
• Data Querying: Query S3-based data lakes using Amazon Athena.
• Data Warehousing: Host large-scale data warehouses with Amazon Redshift and Redshift Spectrum.
• Relational Database Integration: Integrate smaller datasets with big data using Relational Database Service (RDS).
• Security & Governance: Ensure data security with encryption, KMS, Macie, Secrets Manager, IAM, and more.
• Management Tools: Govern and monitor your systems with CloudFormation, CloudTrail, CloudWatch, AWS Config, and more.

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