In the digital age, data is the lifeblood of organizations, fueling insights, innovations, and informed decision-making. Amazon Web Services (AWS), a trailblazer in cloud computing, offers a game-changing solution known as AWS Data Engineering. This cloud-based data management powerhouse empowers organizations to harness, store, process, and dissect vast volumes of data within a secure, scalable, and dynamic environment. By providing the infrastructure and tools for building and managing data pipelines, lakes, and warehouses, AWS Data Engineering emerges as an indispensable asset for modern enterprises.
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Introduction to Data Engineering on AWS
What is Data Engineering?
Overview of AWS services for data engineering
Data engineering use cases on AWS
Data storage options on AWS
AWS Big Data and Analytics Services
Introduction to AWS big data and analytics services
Data ingestion concepts
AWS services for data ingestion
AWS Kinesis Data Streams
AWS Kinesis Data Firehose
AWS Database Migration Service
Data Storage and Management
Data storage and management concepts
AWS services for data storage and management
Amazon S3 Glacier
Data processing concepts
AWS services for data processing
AWS Step Functions
Data analytics concepts
AWS services for data analytics
Amazon Elasticsearch Service
Data visualization concepts
AWS services for data visualization
Tableau on AWS
Power BI on AWS
Data Governance and Security
Data governance and security concepts
AWS services for data governance and security
Advanced Data Engineering Concepts
Advanced data engineering concepts
ETL best practices
Data pipeline orchestration
Data lake architecture
Machine Learning on AWS
Introduction to machine learning
AWS services for machine learning
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