- Introduction to AWS Data Analytics:
- Overview of the workshop objectives
- Explanation of the importance of data analytics in modern business operations
- Introduction to AWS services for data analytics
- Data Collection and Storage:
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- Introduction to AWS data storage services (Amazon S3, Amazon Redshift, Amazon DynamoDB etc)
- Best practices for data collection and storage
- Data Transformation and Processing:
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- Introduction to AWS data processing and transformation services (AWS Glue, Amazon EMR, AWS Lambda etc)
- Overview of ETL (Extract, Transform, Load) processes
- Data Analysis and Visualization:
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- Introduction to AWS analytics and visualization services (Amazon Athena, Amazon QuickSight etc)
- Creating dashboards and visualizations
- Real-time Data Analytics:
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- Introduction to real-time data analytics with AWS services (Amazon Kinesis, Amazon Elasticsearch Service etc)
- Use cases for real-time analytics
- Machine Learning for Data Analytics:
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- Introduction to AWS machine learning services (Amazon SageMaker, Amazon Comprehend etc)
- Overview of machine learning concepts for data analytics
- Optimizing Data Analytics Workloads:
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- Best practices for optimizing performance and cost-effectiveness of data analytics workloads on AWS
- Monitoring and managing data analytics workloads
- Security and Compliance:
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- Overview of security best practices for data analytics on AWS
- Introduction to AWS security and compliance services (AWS Identity and Access Management, AWS Key Management Service etc)
- Case Studies and Best Practices:
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- Real-world case studies of organizations leveraging AWS for data analytics
- Best practices for successful implementation of data analytics solutions on AWS
Q&A session and discussion on challenges and solutions