DEA-C01: AWS Certified Data Engineer – Associate

DEA-C01: AWS Certified Data Engineer – Associate

Offered by Linux Training

The DEA-C01: AWS Certified Data Engineer – Associate course at Linux Training is designed for aspiring data engineers, IT professionals, and developers who want to build expertise in designing, building, and managing data pipelines on AWS.

This course focuses on working with AWS data services, data ingestion, transformation, storage, and analytics, enabling learners to handle large-scale data processing and build efficient, scalable data solutions in the cloud.

Course Overview

This program provides a comprehensive understanding of data engineering workflows on AWS, helping learners design end-to-end data pipelines and manage data efficiently across cloud environments.

What You Will Learn

  • Fundamentals of Data Engineering
  • AWS Data Services Overview (S3, Glue, Redshift, etc.)
  • Data Ingestion and Pipeline Design
  • Data Transformation and Processing
  • Data Storage and Data Lakes
  • Data Warehousing Concepts
  • Data Security and Governance
  • Monitoring and Optimization

Why Choose This Course?

  • Industry-recognized AWS certification (DEA-C01)
  • High-demand data engineering skills
  • Hands-on training with real-world scenarios
  • Focus on scalable and modern data solutions
  • Guidance from experienced trainers

Career Opportunities

After completing this course, you can explore roles such as:

  • Data Engineer
  • Cloud Data Engineer
  • ETL Developer
  • Big Data Engineer (Entry Level)
  • Data Platform Engineer

Who Can Join?

  • IT professionals and developers
  • Data analysts looking to upgrade skills
  • Students interested in data engineering
  • Anyone with basic cloud or database knowledge

Build Your Career in Data Engineering with AWS

Join Linux Training and gain the skills needed to design and manage scalable data solutions in the cloud using AWS.

DEA-C01: AWS Certified Data Engineer – Associate

Modules

1. Data Ingestion and Transformation - 34%

Perform data ingestion

  • Read data from streaming sources (for example, Amazon Kinesis, Amazon MSK, DynamoDB Streams, AWS DMS, AWS Glue, Amazon Redshift)
  • Read data from batch sources (for example, Amazon S3, AWS Glue, Amazon EMR, AWS DMS, Amazon Redshift, AWS Lambda, Amazon AppFlow)
  • Implement appropriate configuration options for batch ingestion
  • Consume data APIs
  • Set up schedulers using EventBridge, Apache Airflow, or time-based schedules
  • Set up event triggers (for example, S3 Event Notifications, EventBridge)
  • Call a Lambda function from Kinesis
  • Create allowlists for IP addresses
  • Implement throttling and overcome rate limits
  • Manage fan-in and fan-out for streaming
  • Describe replayability of ingestion pipelines
  • Define stateful and stateless transactions

    • Transform and process data

  • Optimize container usage (EKS, ECS)
  • Connect to data sources (JDBC, ODBC)
  • Integrate data from multiple sources
  • Optimize processing costs
  • Use transformation services (EMR, Glue, Lambda, Redshift)
  • Transform data formats (for example, CSV to Parquet)
  • Troubleshoot transformation failures
  • Create data APIs using AWS services
  • Define volume, velocity, and variety
  • Integrate LLMs for data processing

    • Orchestrate data pipelines

  • Use orchestration services (Lambda, EventBridge, MWAA, Step Functions, Glue workflows)
  • Build scalable, resilient pipelines
  • Implement serverless workflows
  • Use notification services (SNS, SQS)

    • Apply programming concepts

  • Optimize code for runtime efficiency
  • Configure Lambda for concurrency
  • Use programming languages (Python, SQL, Scala, R, Java, Bash, PowerShell)
  • Apply software engineering best practices
  • Use Infrastructure as Code (CloudFormation, CDK)
  • Use AWS SAM for deployment
  • Use storage volumes in Lambda
  • Describe CI/CD processes
  • Define distributed computing
  • Describe data structures and algorithms

    • 2. Data Store Management - 26%

    Choose a data store

  • Implement storage services based on cost/performance (Redshift, EMR, RDS, DynamoDB, Kinesis)
  • Configure storage services for access patterns
  • Apply services to use cases (for example, HNSW, MemoryDB)
  • Integrate migration tools (AWS Transfer Family)
  • Implement migration methods (Redshift Spectrum, federated queries)
  • Manage locks
  • Manage open table formats (Apache Iceberg)
  • Describe vector index types (HNSW, IVF)

    • Understand data cataloging systems

  • Use data catalogs to consume data
  • Build catalogs (Glue Data Catalog, Hive metastore)
  • Discover schemas using crawlers
  • Synchronize partitions
  • Create connections for cataloging
  • Manage business catalogs (SageMaker Catalog)

    • Manage the lifecycle of data

  • Perform load/unload operations (S3 ↔ Redshift)
  • Manage S3 lifecycle policies
  • Expire data using lifecycle rules
  • Manage versioning and TTL
  • Delete data based on requirements
  • Ensure resiliency and availability

    • Design data models and schema evolution

  • Design schemas (Redshift, DynamoDB, Lake Formation)
  • Handle schema changes
  • Perform schema conversion (AWS SCT, DMS)
  • Establish data lineage
  • Apply indexing, partitioning, compression best practices
  • Describe vectorization concepts

    • 3. Data Operations and Support - 22%

    Automate data processing

  • Orchestrate pipelines (MWAA, Step Functions)
  • Troubleshoot workflows
  • Call SDKs for AWS services
  • Process data using EMR, Redshift, Glue
  • Maintain data APIs
  • Prepare data (DataBrew, SageMaker)
  • Query data (Athena)
  • Use Lambda for automation
  • Manage events (EventBridge)

    • Analyze data

  • Visualize data (QuickSight, DataBrew)
  • Clean data (Lambda, Athena, notebooks)
  • Use SQL for querying
  • Use Athena notebooks with Spark
  • Describe provisioned vs serverless tradeoffs
  • Define aggregation, grouping, pivoting

    • Maintain and monitor pipelines

  • Extract logs for audits
  • Deploy monitoring solutions
  • Send alerts using notifications
  • Troubleshoot performance issues
  • Track API calls using CloudTrail
  • Use CloudWatch Logs
  • Analyze logs (Athena, OpenSearch, EMR)

    • Ensure data quality

  • Run data quality checks
  • Define quality rules (DataBrew)
  • Investigate consistency
  • Describe sampling techniques
  • Implement data skew handling

    • 4. Data Security and Governance - 18%

    Apply authentication mechanisms

  • Update VPC security groups
  • Create IAM roles, policies, endpoints
  • Manage credentials (Secrets Manager)
  • Set up IAM roles for services
  • Apply IAM policies
  • Differentiate managed vs unmanaged services
  • Use domains and projects in SageMaker

    • Apply authorization mechanisms

  • Create custom IAM policies
  • Store credentials securely
  • Manage database access
  • Use Lake Formation permissions
  • Apply role-based and attribute-based access
  • Follow least privilege principles

    • Ensure data encryption and masking

  • Apply masking and anonymization
  • Use AWS KMS for encryption
  • Configure cross-account encryption
  • Enable encryption in transit

    • Prepare logs for audit

  • Use CloudTrail for API tracking
  • Store logs in CloudWatch
  • Use CloudTrail Lake
  • Analyze logs (Athena, OpenSearch)
  • Integrate logging services

    • Understand data privacy and governance

  • Grant permissions for data sharing
  • Implement PII detection (Macie)
  • Apply data privacy strategies
  • Track configuration changes (AWS Config)
  • Maintain data sovereignty
  • Manage access via SageMaker Catalog
  • Describe governance frameworks

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