AWS Certified Data Engineer Associate DEA-C01

Associate DEA-C01

AWS Certified Data Engineer – Associate validates skills in core data-related AWS services, data ingestion and transformation, pipeline orchestration, programming concepts, data modelling, lifecycle management, and data quality — the complete toolkit for modern cloud data engineering.

Data Ingestion ETL Pipelines AWS Glue Amazon Kinesis Apache Spark Data Quality Pipeline Orchestration Data Security Amazon Redshift Data Governance

Exam Domains

Four DEA-C01 Exam Domains

Ingestion and transformation, data store management, operations, and security — the four pillars of cloud data engineering.

Domain 01

Data Ingestion & Transformation

34%

exam weight

›Throughput and latency characteristics for AWS ingest services (Kinesis, MSK, DMS)
›Streaming vs batch ingestion patterns
›AWS Glue ETL and DataBrew transformations
›Apache Spark processing on EMR
›Intermediate staging and replay-ability

Domain 02

Data Store Management

26%

exam weight

›Storage platform selection (S3, Redshift, DynamoDB, RDS, OpenSearch)
›Data formats (CSV, Parquet, ORC, JSON, Avro)
›Data modelling and schema evolution
›Data cataloguing with AWS Glue Data Catalog
›Lifecycle management (hot/warm/cold/archive tiers)

Domain 03

Data Operations & Support

22%

exam weight

›Maintaining and troubleshooting data pipelines
›CI/CD for data pipelines (CodePipeline, CodeBuild)
›CloudWatch monitoring and alerting
›Performance tuning with SQL optimisation and EMR tuning
›Automated data processing with Lambda and Step Functions

Domain 04

Data Security & Governance

18%

exam weight

›IAM roles and resource-based policies for data services
›VPC networking for data security
›Authentication (password, certificate, role-based)
›KMS encryption (client-side, server-side)
›Data privacy (PII, sovereignty), audit logging with CloudTrail and Macie

Why DEA-C01

The Data Engineer's AWS Credential

Data engineering is one of the fastest-growing specialisations in cloud computing. DEA-C01 validates your ability to build, maintain, and secure production data pipelines on AWS — from ingestion to analytics.

Associate Cert — Hands-On Practitioner

DEA-C01 requires practical experience with AWS data services. It's designed for data engineers, ETL developers, and analytics engineers building production pipelines on AWS.

Full Pipeline Coverage — Source to Insight

DEA-C01 covers the complete data engineering lifecycle: ingestion with Kinesis/DMS, processing with Glue/EMR, storage with Redshift/S3, and analytics with Athena/QuickSight.

Programming & CI/CD for Data

DEA-C01 is unique in covering SQL, Python, Spark, and CI/CD with CodePipeline — ensuring you can build automated, testable, production-grade data pipelines.

Security & Governance Built In

With 18% of exam weight on security, DEA-C01 ensures you understand encryption, IAM, Lake Formation, Macie, data masking, and compliance for regulated data environments.

AWS's Newest Associate Certification

Launched in 2024, DEA-C01 is AWS's newest Associate certification — organisations are actively seeking certified data engineers, making early certification highly valuable.

Pathway to the Highest-Paid Data Roles

Data platform engineers and data governance engineers with DEA-C01 command ₹14–35 LPA — among the highest Associate-level salaries in cloud computing.

Curriculum

17-Module DEA-C01 Programme

The most comprehensive DEA-C01 curriculum available — covering all four domains across 17 focused modules from ingestion to governance.

Throughput and latency characteristics for AWS ingest services
Batch vs streaming ingestion patterns and use case selection
Replay-ability of pipelines — Kinesis retention and DMS rollback
Stateful vs stateless transactions in stream processing
Kinesis Data Streams and Firehose hands-on configuration

ETL pipeline creation based on data requirements
Volume, velocity, and variety handling in cloud pipelines
Apache Spark on EMR — RDD, DataFrame, and Structured Streaming
AWS Glue transforms — map, filter, join, aggregate
Intermediate staging locations and checkpoint strategies
Data cleansing — null handling, type casting, deduplication

AWS Step Functions for workflow orchestration (state machines)
Event-driven architecture with Amazon EventBridge rules
Scheduled vs dependency-based pipeline execution patterns
Serverless workflows with Lambda triggers and SQS/SNS
Error handling, retry logic, and dead-letter queues

CI/CD for data pipelines (CodePipeline, CodeBuild, CodeDeploy)
SQL for data transformations (SELECT, JOIN, aggregation, window functions)
Infrastructure as Code with AWS CDK and CloudFormation
Distributed computing fundamentals — partitioning, shuffling, skew
Python scripting for Glue jobs and Lambda functions

Storage platform characteristics and selection decision framework
S3 vs Redshift vs DynamoDB vs RDS — workload matching
Data formats (CSV, Parquet, ORC, Avro) — when to use each
Access patterns — transactional, analytical, time-series
Managing locks and transactions in Redshift and RDS

Creating and managing the AWS Glue Data Catalog
Classifiers, crawlers, and automated schema discovery
Metadata management and business glossary
Data classification by sensitivity, domain, and ownership
Integrating Lake Formation for tag-based governance

Hot, warm, cold, and archive storage tier definitions
S3 Intelligent Tiering and lifecycle rule configuration
Cost-optimised retention policies for data engineering teams
Legal, regulatory, and GDPR deletion requirements
Data protection with S3 versioning and MFA delete

Data modelling concepts (star schema, snowflake, data vault)
Structured vs semi-structured vs unstructured data modelling
Indexing and partitioning strategies for query performance
Compression techniques (Snappy, GZIP, ZSTD) for storage
Schema evolution with AWS Glue Schema Registry

EMR scripting (PySpark, HiveQL, Pig Latin)
Amazon Redshift stored procedures and UDFs
AWS Glue scripts — DynamicFrame operations and transforms
Lambda triggers for event-driven and scheduled processing
Repeatable pipeline automation with parameterised jobs

Provisioned vs serverless analytics tradeoffs (Athena, Redshift Serverless)
Athena SQL queries, federated queries, and CTAS patterns
Amazon QuickSight visualisation and SPICE dataset refresh
Data aggregation techniques (rolling averages, grouping, pivoting)
Data cleansing at query time with Athena views

CloudWatch metrics and alarms for Glue jobs, EMR, and Kinesis
AWS Glue job metrics, bookmarks, and DPU optimisation
Performance tuning for EMR (executor tuning, shuffle partitions)
CloudTrail for data access logging and audit trails
Amazon Macie for continuous sensitive data monitoring

Data sampling techniques for large-scale quality checks
Data validation dimensions (completeness, consistency, accuracy, integrity)
Data profiling with AWS Glue DataBrew
Handling data skew in Spark partitions
AWS Glue Data Quality rules and recommendations

VPC security for data services — subnets and security groups
Managed vs unmanaged data service authentication differences
Password-based, certificate-based, and role-based authentication
AWS managed vs customer managed IAM policies
IAM database authentication for RDS and Aurora

Role-Based Access Control (RBAC) for data services
Attribute-Based Access Control (ABAC) with resource tags
Policy-based authorisation with IAM inline and managed policies
AWS Lake Formation fine-grained column and row access
Cross-account data access with AWS Resource Access Manager

KMS encryption for Glue, EMR, Redshift, and S3
Client-side vs server-side encryption (SSE-S3, SSE-KMS, SSE-C)
Data anonymisation techniques (tokenisation, masking, key salting)
Amazon Macie for automated PII detection and alerts
Amazon Redshift dynamic data masking

Application-level vs access-level logging strategies
Centralised logging with CloudWatch Logs and S3 log aggregation
CloudTrail data event logging for S3 and DynamoDB operations
AWS Config rules for configuration compliance monitoring
Log retention policies and log archival to Glacier

PII protection strategies — identification, classification, masking
Data sovereignty and residency requirements (GDPR, regional compliance)
AWS data governance tools (Lake Formation, Macie, Glue Catalog)
GDPR Article 17 right to erasure and data pipeline implications
Building a data governance framework on AWS

Course Snapshot

17 Topics

Full DEA-C01 domains

38+ Hours

Total learning time

Associate Cert

Mid-level AWS

Tech Support

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Career Outcomes

DEA-C01 Careers in Data Engineering

Data engineering is one of the highest-growth, highest-paying cloud specialisations. DEA-C01 opens doors to data engineer, analytics engineer, and data platform roles across every industry.

Amazon

AWS Data

Flipkart

Data Eng.

PhonePe

Data Platform

Razorpay

Analytics

Data Engineer

Build and maintain data pipelines on AWS using Glue, Kinesis, EMR, and Redshift for enterprise analytics workflows.

₹10–28 LPA

ETL / ELT Developer

Design and implement extract, transform, and load pipelines using Glue, Spark, and Lambda with data quality checks.

₹8–22 LPA

Analytics Engineer

Bridge data engineering and analytics — build data models, curated datasets, and dbt/Athena transformation layers.

₹10–26 LPA

Data Platform Engineer

Architect and maintain cloud data platforms — Redshift, EMR, S3 lakes — with security, governance, and CI/CD.

₹14–35 LPA

Data Governance Engineer

Implement data cataloguing, Lake Formation policies, encryption, audit logging, and PII protection for enterprise data.

₹12–30 LPA

Topics

38+ hrs

Training Hours

Associate

Cert Level

DEA-C01

AWS Certified

Ready to Become an AWS Data Engineer?

DEA-C01 is AWS's definitive credential for data engineers. 17 modules, 4 domains, and a complete toolkit for building production data pipelines — from Kinesis ingestion to Redshift analytics and Lake Formation governance.

Data Engineer

Associate DEA-C01

Four DEA-C01 Exam Domains

The Data Engineer's AWS Credential

17-Module DEA-C01 Programme

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DEA-C01 Careers in Data Engineering

Ready to Become an AWS Data Engineer?