Course Content
Data Storage
Understanding and utilizing Amazon storage services like Amazon S3, Amazon Redshift, and Amazon DynamoDB for efficient data storage.
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Amazon S3 (Simple Storage Service)
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Amazon EBS (Elastic Block Store)
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Amazon EFS (Elastic File System)
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Databases
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Amazon RDS (Relational Database Service)
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Amazon DynamoDB
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Amazon Aurora:
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Amazon Redshift:
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Data Ingestion
Strategies and tools for collecting and importing data from various sources into the Amazon ecosystem.
Data Transformation
Techniques and tools for transforming raw data into a format suitable for analysis and processing, such as using AWS Glue, Apache Spark, or AWS Data Pipeline.
Data Catalogs
Building and managing data catalogs using tools like AWS Glue Data Catalog to provide a centralized view of data assets.
Data Modeling
Designing and implementing data models to support the analytical and reporting needs of the organization, collaborating with data architects and analysts
Data Warehousing
Building and optimizing data warehouses using Amazon Redshift, including schema design, data distribution, and query performance tuning.
Streaming Data Processing:
Handling and processing real-time streaming data using frameworks like Apache Kafka, Amazon Kinesis, or AWS Lambda.
Big Data Technologies
Leveraging Amazon EMR (Elastic MapReduce), Amazon Athena, and AWS Glue to process and analyze large volumes of data.
Data Pipeline Orchestration
Designing and managing data pipelines using AWS Data Pipeline or AWS Step Functions for efficient data flow across various stages.
Monitoring and Performance Optimization:
Monitoring data pipelines, storage, and processing systems, and optimizing performance using monitoring tools and techniques.
Machine Learning Integration:
Integrating machine learning capabilities into data pipelines using services like Amazon SageMaker or AWS Glue for advanced data analysis and predictions.
Data Versioning and Lineage:
Tracking data versioning and lineage information to understand the origin and impact of data changes using tools like AWS Glue DataBrew or third-party solutions.
Data Security:
Implementing data security measures such as encryption, access controls, and data classification using AWS services like AWS Key Management Service (KMS) or AWS Identity and Access Management (IAM).
Data Migration:
Planning and executing data migration strategies to move data from on-premises environments to AWS using services like AWS Database Migration Service (DMS).
Serverless Data Processing:
Leveraging AWS Lambda, AWS Glue, and other serverless services for data processing tasks without the need for infrastructure management.
Data Archiving and Backup:
Implementing data archiving and backup strategies using services like Amazon Glacier, AWS Backup, or other AWS storage solutions.
AWS – Data Engineering
About Lesson

Amazon Redshift is a fully managed data warehousing service designed for high-performance analytics and reporting. It allows you to analyze vast amounts of structured data using SQL queries. Redshift uses columnar storage and parallel query execution to deliver fast query performance. It scales horizontally, allowing you to dynamically add or remove compute resources based on workload demands. Redshift integrates with other AWS services like S3 and offers advanced features such as automatic compression, workload management, and encryption at rest.

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