Data Lake on AWS: S3, Glue, Athena, Lake Formation & EMR Overview
The Data Lake on AWS: S3, Glue, Athena, Lake Formation & EMR Training Course provides a comprehensive introduction to designing, building, securing, processing, and querying modern data lakes using Amazon Web Services (AWS).
The course focuses on the core AWS services commonly used in a data lake architecture, including Amazon S3 for scalable data storage, AWS Glue for data discovery and ETL processing, Amazon Athena for serverless SQL analytics, AWS Lake Formation for centralized data lake governance and access control, and Amazon EMR for large-scale distributed data processing.
Participants will learn the complete data lake lifecycle—from ingesting and organizing raw data through cataloging, transformation, governance, analytics, and optimization. The course also introduces data lake architecture patterns, data formats, partitioning, security, monitoring, and cost optimization to help participants build scalable and maintainable AWS data platforms.
Duration 5 Days – 35 hrs.
Objectives
- Explain data lake concepts, architecture, components, and common use cases.
- Understand the role of AWS services within a modern data lake architecture.
- Design scalable data lake storage architectures using Amazon S3.
- Organize data into raw, processed, curated, and consumption layers.
- Apply S3 security, encryption, lifecycle, versioning, and access-control features.
- Use AWS Glue Data Catalog to discover and organize datasets.
- Create and manage AWS Glue crawlers, databases, and tables.
- Build ETL and data transformation workflows using AWS Glue.
- Understand the use of Apache Spark within AWS Glue and Amazon EMR.
- Query data directly in Amazon S3 using Amazon Athena and SQL.
- Optimize Athena queries using partitioning and columnar data formats.
- Build and manage governed data lakes using AWS Lake Formation.
- Implement fine-grained access controls for data lake resources.
- Understand Amazon EMR architecture and distributed data processing concepts.
- Process large-scale datasets using EMR and Apache Spark.
- Integrate S3, Glue, Athena, Lake Formation, and EMR into an end-to-end architecture.
- Apply AWS security, monitoring, performance, and cost optimization practices.
- Design a practical AWS data lake solution based on business and technical requirements.
Target Audience
- Data Engineers
- Cloud Engineers
- AWS Engineers
- Data Architects
- Cloud Architects
- Solutions Architects
- Database Administrators
- Database Developers
- ETL Developers
- Big Data Engineers
- Business Intelligence Developers
- Data Analysts with cloud/data engineering responsibilities
- DevOps Engineers supporting data platforms
- Application Developers working with AWS data services
- IT Professionals responsible for cloud-based data platforms
- Technical Leads and Managers involved in data lake initiatives
- Professionals transitioning into AWS data engineering and big data roles
Prerequisites
- Basic understanding of cloud computing concepts.
- Basic familiarity with AWS or equivalent cloud platforms.
- Basic knowledge of databases, tables, schemas, and data processing concepts.
- Working knowledge of SQL.
- Basic understanding of structured, semi-structured, and unstructured data.
- Basic familiarity with Python is beneficial for AWS Glue and Spark exercises.
- Familiarity with ETL/ELT concepts is helpful but not mandatory.
- Basic Linux or command-line knowledge is beneficial but not required.
Course Outline
Day 1 – Data Lake Fundamentals and Amazon S3
Module 1: Introduction to Modern Data Lakes
- What is a data lake?
- Data lakes vs. data warehouses
- Data lake vs. lakehouse concepts
- Structured, semi-structured, and unstructured data
- Common data lake use cases
- Data lake architecture principles
- Data ingestion, storage, processing, governance, and consumption
- Batch vs. streaming data
- Data lake challenges and best practices
Module 2: AWS Data Lake Architecture
- Overview of the AWS analytics ecosystem
- Core components of an AWS data lake
- Amazon S3
- AWS Glue
- Amazon Athena
- AWS Lake Formation
- Amazon EMR
- Supporting AWS services
- Data producers and consumers
- Raw, processed, curated, and analytics zones
- Designing scalable data lake architectures
Module 3: Amazon S3 for Data Lake Storage
- Amazon S3 concepts
- Buckets, objects, prefixes, and metadata
- S3 storage architecture
- Organizing data lake buckets and prefixes
- Data lake folder and naming conventions
- S3 storage classes
- Versioning
- Lifecycle management
- Object tagging
- Data retention and archival strategies
Module 4: Data Formats, Partitioning, and S3 Optimization
- CSV, JSON, Avro, ORC, and Parquet
- Row-oriented vs. columnar formats
- Choosing appropriate data formats
- Compression techniques
- Partitioning strategies
- Data organization for analytics
- Small-file considerations
- Performance and cost considerations
Module 5: Securing Amazon S3 Data
- AWS Identity and Access Management fundamentals
- IAM users, roles, and policies
- S3 bucket policies
- Block Public Access
- Encryption at rest and in transit
- AWS Key Management Service integration
- S3 access control considerations
- Logging and auditing
- Data protection best practices
Day 2 – AWS Glue and Data Catalog
Module 6: Introduction to AWS Glue
- AWS Glue architecture and components
- Serverless data integration concepts
- AWS Glue Data Catalog
- Glue databases and tables
- Metadata management
- Schema discovery
- Integration with S3 and analytics services
Module 7: AWS Glue Crawlers and Data Catalog
- Creating Glue databases
- Configuring crawlers
- Data stores and crawler targets
- Classifiers
- Schema inference
- Creating and updating catalog tables
- Working with partitions
- Managing metadata
- Querying cataloged datasets
Module 8: ETL and Data Transformation with AWS Glue
- ETL and ELT concepts
- AWS Glue ETL jobs
- Job configuration
- Data sources and targets
- Transforming datasets
- AWS Glue and Apache Spark
- Working with DynamicFrames and DataFrames
- Filtering and mapping data
- Handling schema changes
- Writing transformed data to Amazon S3
Module 9: Building Data Pipelines with AWS Glue
- Designing ETL pipelines
- Job parameters
- Job bookmarks
- Incremental processing
- Glue triggers
- Workflow concepts
- Scheduling jobs
- Error handling
- Logging and troubleshooting
- Performance considerations
Day 3 – Serverless Analytics with Amazon Athena
Module 10: Introduction to Amazon Athena
- Serverless interactive analytics
- Athena architecture
- Integration with Amazon S3
- Integration with AWS Glue Data Catalog
- Athena workgroups
- Query execution and results
- Supported data formats
Module 11: Querying Data Lakes Using SQL
- Creating databases and external tables
- Querying S3 datasets
- Filtering and aggregating data
- Joins and subqueries
- Working with JSON and semi-structured data
- Views
- CTAS – Create Table As Select
- INSERT INTO operations
- Working with partitioned datasets
Module 12: Athena Performance and Cost Optimization
- Understanding Athena query scanning
- Partition pruning
- Columnar formats
- Compression
- Optimizing file sizes
- Reducing unnecessary data scans
- Query optimization practices
- Workgroup configuration
- Query limits and resource considerations
- Monitoring query usage and cost
Module 13: Integrating S3, Glue, and Athena
- S3 as the data layer
- Glue as the metadata layer
- Athena as the SQL analytics layer
- End-to-end data discovery and querying
- Updating schemas and partitions
- Managing changing datasets
- Building reusable analytical datasets
Day 4 – AWS Lake Formation and Data Lake Governance
Module 14: Introduction to AWS Lake Formation
- Data lake governance challenges
- AWS Lake Formation architecture
- Lake Formation concepts
- Data lake administrators
- Data locations
- Databases and tables
- Integration with AWS Glue Data Catalog
- Governed access to data
Module 15: Building a Governed Data Lake
- Registering Amazon S3 locations
- Configuring data lake administrators
- Creating databases and tables
- Managing permissions
- Granting and revoking access
- IAM permissions vs. Lake Formation permissions
- Data access workflows
- Centralized data governance
Module 16: Fine-Grained Data Access Control
- Database-level permissions
- Table-level permissions
- Column-level access
- Row-level and cell-level filtering concepts
- Data filters
- Role-based data access
- Least-privilege principles
- Cross-account data sharing concepts
Module 17: Data Lake Security and Governance Best Practices
- IAM integration
- Encryption with AWS KMS
- Protecting sensitive information
- Logging and auditing
- AWS CloudTrail considerations
- Data classification concepts
- Governance policies
- Security architecture patterns
- Compliance considerations
- Data lifecycle governance
Day 5 – Amazon EMR and End-to-End Data Lake Architecture
Module 18: Introduction to Amazon EMR
- Big data processing concepts
- Distributed computing fundamentals
- Amazon EMR architecture
- EMR clusters
- Primary, core, and task nodes
- EMR deployment options
- Amazon EC2-based EMR
- EMR Serverless overview
- Integration with Amazon S3
- Common EMR workloads
Module 19: Apache Spark on Amazon EMR
- Apache Spark architecture
- Spark applications and jobs
- RDD, DataFrame, and Dataset concepts
- Reading data from Amazon S3
- Transforming large datasets
- Filtering and aggregation
- Joining datasets
- Writing processed data to S3
- Working with Parquet
- Partitioning output data
- Spark performance considerations
Module 20: EMR Operations and Optimization
- Selecting cluster configurations
- Compute and storage considerations
- Scaling concepts
- Managed scaling
- Spot and On-Demand capacity considerations
- Logging and monitoring
- Amazon CloudWatch integration
- Troubleshooting EMR workloads
- Performance optimization
- Cost optimization strategies
Module 21: End-to-End AWS Data Lake Integration
- Data ingestion into Amazon S3
- Raw data organization
- Metadata discovery with AWS Glue
- Data transformation using Glue or EMR
- Creating curated datasets
- Data governance with Lake Formation
- SQL analytics with Athena
- Data consumption patterns
- Integrating the major AWS data lake services
Module 22: Designing a Production-Ready AWS Data Lake
- Data lake architecture patterns
- Storage layer design
- Metadata and catalog architecture
- Processing layer selection
- Glue vs. EMR workload considerations
- Analytics and consumption layer
- Security and governance architecture
- Scalability and resiliency
- Monitoring and operational considerations
- Performance optimization
- Cost management
- Data lake design best practices
Module 23: Integrated AWS Data Lake Scenario
- Designing an end-to-end data lake
- Creating an S3 data organization strategy
- Cataloging datasets with AWS Glue
- Transforming raw data into curated datasets
- Processing large-scale data with EMR/Spark
- Governing access through Lake Formation
- Querying curated datasets using Athena
- Reviewing security, performance, and cost considerations
- Reviewing the complete AWS data lake architecture
