The AWS Certified Data Engineer - Associate course is an intensive program that teaches participants how to design, implement, and manage data solutions on AWS. It covers key topics like data ingestion, transformation, and security using AWS services such as Kinesis, Redshift, and S3. This course equips IT professionals with practical skills to handle data pipelines and optimize processing on the AWS platform.
Instructor
AWS Certified Data Engineer - Associate Training Course
Curriculum
Boost your cloud career with AWS Certified Solutions Architect - Associate Training. Gain hands-on skills & expertise to design secure, scalable AWS solutions.
The AWS Certified Data Engineer - Associate course provides in-depth training on designing, implementing, and managing data solutions on AWS. It covers essential topics such as data ingestion, transformation, and security using services like Kinesis, Redshift, and S3. Participants will gain practical skills in handling data pipelines and optimizing data processing on the AWS platform.
Prerequisites:
Basic understanding of cloud computing concepts and AWS services.
Familiarity with data storage and ingestion processes.
Proficiency in SQL and database concepts.
Fundamental programming knowledge, preferably in Python or a similar language.
Experience with data manipulation and transformation.
Familiarity with core AWS services such as S3, Lambda, and IAM.
Target Audience:
Data Engineers
Data Architects
Database Administrators
ETL Developers
Cloud Solutions Architects
Big Data Professionals
DevOps Engineers
Machine Learning Engineers
IT Managers
Cloud Engineers
Data Analysts
System Administrators
Data Scientists
Software Engineers focusing on data operations
IT Professionals transitioning to cloud data management
Learning Objectives:
Data Ingestion:
Read and ingest data from sources like Kinesis, MSK, and Redshift.
Implement batch ingestion and configure schedulers and event triggers.
Manage data distribution through throttling and fan-in/fan-out strategies.
Transform and Process Data:
Optimize container usage for data performance.
Integrate and transform data from multiple sources using AWS services like EMR, Glue, and Lambda.
Convert data formats and debug transformation failures.
Choose and Configure Data Stores:
Implement and configure storage solutions including Redshift, DynamoDB, and S3.
Integrate AWS Transfer Family for data migration.
Utilize advanced query and view capabilities with Redshift Federated Queries and Spectrum.
Data Cataloging and Management:
Use Glue Data Catalog and Hive Metastore to build and reference data catalogs.
Synchronize partitions and manage data lifecycle policies in S3 and DynamoDB.
Course Outline
Day 1
Module 1: Introduction to Data Engineering on AWS
Overview of AWS Data Services
Key Concepts in Data Engineering
AWS Well-Architected Framework for Data
Module 2: Data Ingestion
AWS Data Ingestion Services (e.g., Kinesis, S3)
Real-time Data Streaming with Amazon Kinesis
Batch Data Ingestion with AWS Glue
Hands-on Lab: Implementing Data Ingestion Pipelines
Module 3: Data Storage
Storage Options on AWS (S3, Redshift, RDS, DynamoDB)
Data Lake Architecture with Amazon S3
Best Practices for Data Storage and Security
Hands-on Lab: Setting Up a Data Lake on AWS
Day 2
Module 4: Data Processing
ETL and ELT Processes
Using AWS Glue for ETL
Real-time Processing with AWS Lambda and Kinesis
Hands-on Lab: Building a Data Processing Pipeline
Module 5: Data Analysis and Visualization
Data Warehousing with Amazon Redshift
Analyzing Data with Amazon Athena
Visualization with Amazon QuickSight
Hands-on Lab: Data Analysis and Visualization with AWS Tools
Module 6: Machine Learning Integration
Introduction to Machine Learning on AWS
Integrating Amazon SageMaker with Data Pipelines
Hands-on Lab: Building a Simple ML Model with Amazon SageMaker
Day 3
Module 7: Data Security and Governance
Data Encryption and Key Management
Access Control with IAM and Lake Formation
Data Governance Best Practices
Hands-on Lab: Implementing Data Security and Governance
Module 8: Monitoring and Optimization
Monitoring Data Workloads with CloudWatch and AWS X-Ray
Performance Optimization Techniques
Cost Management and Optimization
Hands-on Lab: Monitoring and Optimizing a Data Pipeline
Module 9: Capstone Project
End-to-End Data Engineering Project
Building a Complete Data Pipeline from Ingestion to Visualization
Applying Best Practices and Optimization Techniques
The AWS Certified Data Engineer - Associate course provides in-depth training on designing, implementing, and managing data solutions on AWS. It covers essential topics such as data ingestion, transformation, and security using services like Kinesis, Redshift, and S3. Participants will gain practical skills in handling data pipelines and optimizing data processing on the AWS platform.
Prerequisites:
Basic understanding of cloud computing concepts and AWS services.
Familiarity with data storage and ingestion processes.
Proficiency in SQL and database concepts.
Fundamental programming knowledge, preferably in Python or a similar language.
Experience with data manipulation and transformation.
Familiarity with core AWS services such as S3, Lambda, and IAM.
Target Audience:
Data Engineers
Data Architects
Database Administrators
ETL Developers
Cloud Solutions Architects
Big Data Professionals
DevOps Engineers
Machine Learning Engineers
IT Managers
Cloud Engineers
Data Analysts
System Administrators
Data Scientists
Software Engineers focusing on data operations
IT Professionals transitioning to cloud data management
Learning Objectives:
Data Ingestion:
Read and ingest data from sources like Kinesis, MSK, and Redshift.
Implement batch ingestion and configure schedulers and event triggers.
Manage data distribution through throttling and fan-in/fan-out strategies.
Transform and Process Data:
Optimize container usage for data performance.
Integrate and transform data from multiple sources using AWS services like EMR, Glue, and Lambda.
Convert data formats and debug transformation failures.
Choose and Configure Data Stores:
Implement and configure storage solutions including Redshift, DynamoDB, and S3.
Integrate AWS Transfer Family for data migration.
Utilize advanced query and view capabilities with Redshift Federated Queries and Spectrum.
Data Cataloging and Management:
Use Glue Data Catalog and Hive Metastore to build and reference data catalogs.
Synchronize partitions and manage data lifecycle policies in S3 and DynamoDB.
Course Outline
Day 1
Module 1: Introduction to Data Engineering on AWS
Overview of AWS Data Services
Key Concepts in Data Engineering
AWS Well-Architected Framework for Data
Module 2: Data Ingestion
AWS Data Ingestion Services (e.g., Kinesis, S3)
Real-time Data Streaming with Amazon Kinesis
Batch Data Ingestion with AWS Glue
Hands-on Lab: Implementing Data Ingestion Pipelines
Module 3: Data Storage
Storage Options on AWS (S3, Redshift, RDS, DynamoDB)
Data Lake Architecture with Amazon S3
Best Practices for Data Storage and Security
Hands-on Lab: Setting Up a Data Lake on AWS
Day 2
Module 4: Data Processing
ETL and ELT Processes
Using AWS Glue for ETL
Real-time Processing with AWS Lambda and Kinesis
Hands-on Lab: Building a Data Processing Pipeline
Module 5: Data Analysis and Visualization
Data Warehousing with Amazon Redshift
Analyzing Data with Amazon Athena
Visualization with Amazon QuickSight
Hands-on Lab: Data Analysis and Visualization with AWS Tools
Module 6: Machine Learning Integration
Introduction to Machine Learning on AWS
Integrating Amazon SageMaker with Data Pipelines
Hands-on Lab: Building a Simple ML Model with Amazon SageMaker
Day 3
Module 7: Data Security and Governance
Data Encryption and Key Management
Access Control with IAM and Lake Formation
Data Governance Best Practices
Hands-on Lab: Implementing Data Security and Governance
Module 8: Monitoring and Optimization
Monitoring Data Workloads with CloudWatch and AWS X-Ray
Performance Optimization Techniques
Cost Management and Optimization
Hands-on Lab: Monitoring and Optimizing a Data Pipeline
Module 9: Capstone Project
End-to-End Data Engineering Project
Building a Complete Data Pipeline from Ingestion to Visualization
Applying Best Practices and Optimization Techniques
Presentation and Review of the Capstone Project
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