What you'll learn
Select the correct ingestion service for a described throughput and latency requirement (Kinesis Data Streams vs Firehose vs MSK)
Build and optimize AWS Glue jobs, crawlers, and DynamicFrames
Design S3-based data lakes with correct storage classes and lifecycle policies
Model Redshift distribution and sort keys for query performance
Choose between DynamoDB, RDS, and Aurora for a given access pattern
Orchestrate pipelines with Step Functions, MWAA, and EventBridge
Monitor and troubleshoot pipelines using CloudWatch and CloudTrail
Apply Lake Formation fine-grained permissions and KMS encryption
Requirements
2-3 years of general data engineering experience (recommended by AWS)
1-2 years of hands-on AWS experience (recommended by AWS)
Basic SQL proficiency
Familiarity with core AWS concepts: IAM, VPC, S3
Comfort reading AWS CLI commands and JSON/YAML configs
Description
This course contains the use of artificial intelligence.
AWS Certified Data Engineer - Associate (DEA-C01) is one of the fastest-growing credentials in the AWS ecosystem, validating the exact skill hiring managers want: choosing the right AWS service and pattern for a described data-pipeline requirement. This course, built by instructor Aseem Mankotia, is exam-focused preparation structured around AWS's own officially published domain weightings - Data Ingestion and Transformation (34%), Data Store Management (26%), Data Operations and Support (22%), and Data Security and Governance (18%) - so your study time maps directly onto how the real exam is scored.
Every chapter follows a strict no-fluff format: the exam objective, the heavily-tested concepts inside it, a hands-on AWS walkthrough (Kinesis Firehose to S3, a Glue job and crawler, an Athena query against a data lake, a Redshift load, Lake Formation permissions, KMS encryption), the classic exam traps, and scenario-style practice questions using the MOST cost-effective / LEAST operational overhead framing AWS is known for. You will drill the recurring decision pairs the exam relentlessly tests: Kinesis Data Streams vs Firehose vs MSK, Glue ETL vs EMR vs Lambda, Redshift vs Athena vs EMR for query, S3 storage classes and lifecycle policies, DynamoDB vs RDS/Aurora, Step Functions vs MWAA for orchestration, and Lake Formation fine-grained access vs plain IAM.
This course pairs naturally with the AWS Certified Machine Learning Engineer - Associate (MLA-C01) and AWS Solutions Architect Associate courses in this catalog for candidates building a broader AWS data and ML portfolio. The final chapter is a full 65-question exam simulation with a time-management strategy for the 130-minute format and a multiple-response elimination technique. The exam fee is approximately USD 150 and varies by region - always confirm current pricing on the official AWS site before scheduling.
AI content disclosure: This course was produced with the assistance of artificial intelligence tools. Lecture narration is AI-voice generated, and lecture scripts, slides, and practice questions were drafted with AI assistance, then reviewed and curated by the instructor for technical accuracy and alignment with the official DEA-C01 exam guide.
Who this course is for
Data engineers, ETL developers, analytics engineers, and cloud practitioners who build and operate data pipelines on AWS.
Candidates preparing for the AWS Certified Data Engineer - Associate (DEA-C01) exam who want scenario-based, service-selection practice.
A strong complement to the AWS Machine Learning Engineer - Associate (MLA-C01) and Solutions Architect Associate courses in this catalog.



https://rapidgator.net/file/85dc762f0c01c17bb1ba221bbf2b214a/AWS_Certified_Data_Engineer_Associate_DEA-C01_Prep.rar.html