AWS Data Engineer Certification - Hands-On Resources Guide

Overview

The AWS Certified Data Engineer - Associate (DEA-C01) exam validates your ability to implement data pipelines, monitor and troubleshoot issues, and optimize cost and performance. This guide outlines the critical AWS services you need hands-on experience with.

Exam Details:

  • Duration: 130 minutes
  • Questions: 65 total (50 scored, 15 unscored)
  • Question Types: Multiple choice and multiple response
  • Prerequisites: 2-3 years data engineering experience, 1-2 years AWS hands-on experience

Exam Domains & Key Services

Domain 1: Data Ingestion and Transformation (30%)

Focus: Ingesting, transforming data, and orchestrating pipelines with programming concepts

Critical Services for Hands-On Practice:

1. AWS Glue

  • Glue ETL Jobs (Python/Spark)
  • Glue Data Catalog (metadata management)
  • Glue Crawlers (schema discovery)
  • Glue Workflows (orchestration)
  • Glue Studio (visual ETL)
  • Hands-on: Build ETL pipelines, transform data, catalog schemas

2. Amazon EMR (Elastic MapReduce)

  • EMR Clusters (Spark, Hadoop, Hive)
  • EMR Serverless
  • EMR Notebooks
  • Hands-on: Process large datasets, run Spark jobs, optimize cluster configurations

3. AWS Lambda

  • Lambda Functions (data transformation)
  • Event-driven processing
  • Hands-on: Transform data on-the-fly, trigger ETL jobs

4. Amazon Kinesis

  • Kinesis Data Streams (real-time streaming)
  • Kinesis Data Firehose (batch delivery)
  • Kinesis Data Analytics (stream processing)
  • Hands-on: Ingest streaming data, process in real-time, deliver to destinations

5. AWS Step Functions

  • State machines (workflow orchestration)
  • Error handling and retries
  • Hands-on: Orchestrate multi-step data pipelines

6. Amazon EventBridge

  • Event-driven architecture
  • Rule-based routing
  • Hands-on: Trigger pipelines based on events

Domain 2: Data Store Management (26%)

Focus: Choosing optimal data stores, designing data models, cataloging schemas, and managing data lifecycles

Critical Services for Hands-On Practice:

1. Amazon S3

  • S3 Buckets (data lake storage)
  • S3 Lifecycle Policies (data lifecycle management)
  • S3 Storage Classes (cost optimization)
  • S3 Event Notifications
  • Hands-on: Store data, implement lifecycle policies, optimize storage costs

2. Amazon Redshift

  • Redshift Clusters (data warehouse)
  • Redshift Spectrum (query S3 data)
  • Redshift Federated Queries
  • Redshift Serverless
  • Hands-on: Design schemas, optimize queries, use Spectrum for data lakes

3. Amazon DynamoDB

  • Tables and indexes (NoSQL)
  • Global Tables (multi-region)
  • Streams (change data capture)
  • Hands-on: Design data models, optimize performance, use streams

4. Amazon RDS

  • Relational databases (PostgreSQL, MySQL, etc.)
  • Read replicas
  • Backup and restore
  • Hands-on: Manage relational data, optimize queries

5. Amazon Aurora

  • Serverless Aurora
  • Aurora Global Database
  • Hands-on: Use for OLTP workloads

6. AWS Glue Data Catalog

  • Metadata management
  • Schema versioning
  • Hands-on: Catalog data across services

7. Amazon OpenSearch Service

  • Search and analytics
  • Log analytics
  • Hands-on: Index and search data

Domain 3: Data Operations and Support (24%)

Focus: Operationalizing, maintaining, monitoring pipelines, analyzing data, and ensuring data quality

Critical Services for Hands-On Practice:

1. Amazon Athena

  • Serverless SQL queries (S3 data)
  • Query optimization
  • Workgroups and result caching
  • Hands-on: Query data lakes, optimize performance, manage costs

2. AWS CloudWatch

  • Metrics and alarms
  • Logs (CloudWatch Logs)
  • Dashboards
  • Hands-on: Monitor pipelines, set up alarms, troubleshoot issues

3. AWS Glue Data Quality

  • Data quality rules
  • Data profiling
  • Hands-on: Ensure data quality in pipelines

4. Amazon QuickSight

  • Data visualization
  • Dashboards
  • SPICE (in-memory calculation)
  • Hands-on: Create visualizations and dashboards

5. AWS Data Pipeline

  • Pipeline orchestration
  • Scheduling
  • Hands-on: Schedule and manage data pipelines

6. Amazon SageMaker

  • Data Wrangler (data preparation)
  • Feature Store
  • Hands-on: Prepare data for ML workloads

Domain 4: Data Security and Governance (20%)

Focus: Implementing authentication, authorization, encryption, privacy, governance, and logging

Critical Services for Hands-On Practice:

1. AWS IAM (Identity and Access Management)

  • Roles and policies
  • Service roles
  • Cross-service access
  • Hands-on: Configure permissions, implement least privilege

2. AWS Lake Formation

  • Data lake governance
  • Fine-grained access control
  • Data catalog security
  • Hands-on: Govern data lake access, implement security

3. AWS KMS (Key Management Service)

  • Encryption keys
  • Encryption at rest
  • Encryption in transit
  • Hands-on: Encrypt data, manage keys

4. Amazon Macie

  • Data discovery
  • PII detection
  • Compliance monitoring
  • Hands-on: Discover sensitive data

5. AWS Secrets Manager

  • Secret management
  • Rotation
  • Hands-on: Secure credentials and API keys

6. AWS CloudTrail

  • API logging
  • Audit trails
  • Hands-on: Track API calls, audit access

7. VPC & Security Groups

  • Network isolation
  • Security groups
  • NAT Gateways
  • Hands-on: Secure network access

Hands-On Practice Priorities

Must-Have Experience (Critical):

  1. AWS Glue - Build complete ETL pipelines
  2. Amazon S3 - Data lake storage and lifecycle management
  3. Amazon Redshift - Data warehousing and Spectrum
  4. Amazon Kinesis - Real-time data streaming
  5. Amazon Athena - Query data lakes
  6. AWS Lambda - Serverless data transformation
  7. AWS IAM - Security and access control

Highly Recommended:

  1. Amazon EMR - Big data processing
  2. AWS Step Functions - Pipeline orchestration
  3. AWS CloudWatch - Monitoring and logging
  4. AWS Glue Data Catalog - Metadata management
  5. Amazon DynamoDB - NoSQL data modeling

Good to Have:

  1. AWS Lake Formation - Data governance
  2. Amazon QuickSight - Visualization
  3. Amazon EventBridge - Event-driven architecture
  4. AWS KMS - Encryption

Recommended Hands-On Projects

Project 1: Batch ETL Pipeline

  • Services: S3, Glue (Crawler, ETL Job, Catalog), Redshift
  • Goal: Ingest CSV/JSON from S3, transform with Glue, load into Redshift
  • Skills: ETL design, schema management, data cataloging

Project 2: Real-Time Streaming Pipeline

  • Services: Kinesis Data Streams, Kinesis Data Firehose, Lambda, S3, Athena
  • Goal: Stream data, transform in real-time, store in S3, query with Athena
  • Skills: Streaming architecture, real-time processing

Project 3: Data Lake Analytics

  • Services: S3, Glue Catalog, Athena, QuickSight
  • Goal: Build a data lake, catalog data, query with Athena, visualize with QuickSight
  • Skills: Data lake architecture, serverless analytics

Project 4: Multi-Source Data Pipeline

  • Services: RDS, DynamoDB, Kinesis, Glue, Redshift, Step Functions
  • Goal: Ingest from multiple sources, orchestrate with Step Functions, load into warehouse
  • Skills: Multi-source integration, orchestration

Project 5: Data Quality & Monitoring

  • Services: Glue Data Quality, CloudWatch, Lambda, SNS
  • Goal: Implement data quality checks, monitor pipelines, alert on failures
  • Skills: Data quality, operational monitoring

Project 6: Secure Data Lake

  • Services: S3, Lake Formation, IAM, KMS, Glue Catalog, Athena
  • Goal: Implement fine-grained access control, encryption, governance
  • Skills: Security, governance, compliance

Learning Resources

Official AWS Resources

  • AWS Training: AWS Certified Data Engineer - Associate learning path
  • AWS Documentation: Service-specific documentation
  • AWS Workshops: Hands-on workshops for data engineering
  • AWS Well-Architected Framework: Data Analytics Lens

Practice Platforms

  • AWS Free Tier: Practice with free tier limits
  • AWS Skill Builder: Official training courses
  • AWS Hands-On Tutorials: Step-by-step guides

Recommended Study Approach

  1. Week 1-2: Master S3, Glue, and Redshift fundamentals
  2. Week 3-4: Build batch ETL pipelines
  3. Week 5-6: Implement streaming pipelines with Kinesis
  4. Week 7-8: Focus on security and governance (IAM, Lake Formation)
  5. Week 9-10: Practice monitoring, troubleshooting, and optimization
  6. Week 11-12: Review all domains, take practice exams

Key Concepts to Master

Data Ingestion Patterns

  • Batch vs. streaming ingestion
  • Change Data Capture (CDC)
  • Event-driven ingestion
  • API-based ingestion

Data Transformation

  • ETL vs. ELT patterns
  • Data validation and cleansing
  • Schema evolution
  • Data partitioning strategies

Data Storage

  • Data lake vs. data warehouse
  • Storage formats (Parquet, ORC, JSON)
  • Partitioning and bucketing
  • Storage lifecycle management

Orchestration

  • Workflow design
  • Error handling and retries
  • Dependency management
  • Scheduling strategies

Monitoring & Troubleshooting

  • Pipeline monitoring
  • Performance optimization
  • Cost optimization
  • Debugging failed jobs

Security Best Practices

  • Encryption at rest and in transit
  • Least privilege access
  • Data masking and PII handling
  • Audit logging

Exam Tips

  1. Focus on Integration: Understand how services work together
  2. Cost Optimization: Know when to use which service for cost efficiency
  3. Performance: Understand scaling, partitioning, and optimization techniques
  4. Security: IAM roles, encryption, and governance are critical
  5. Troubleshooting: Know common issues and solutions
  6. Best Practices: Follow AWS Well-Architected principles

Additional Notes

  • Programming: Python and SQL are essential
  • Spark: Understanding Spark concepts helps with Glue and EMR
  • SQL: Strong SQL skills needed for Redshift, Athena, and RDS
  • Architecture: Understand data pipeline architectures and patterns

Last Updated: January 2025
Exam Code: DEA-C01