Amazon Kinesis Analytics enables the analysis of data streams using standard SQL queries. It lets you interact with streaming data in real time using SQL and to build fully managed and elastic stream processing applications that process data for real-time visualizations and alarms.
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Preface
- Cloud 101 – Understanding the Basics
- Designing Cloud Applications
- Introducing AWS Components
- AWS components
- Amazon compute-related services
- Amazon storage-related services
- Amazon database-related services
- Amazon messaging-related services
- Amazon networking and content delivery services
- Amazon management tools
- Amazon security, identity, and compliance services
- Amazon analytics-related services
- Amazon machine learning/AI-related services
- Other Amazon services
- Managing costs on AWS cloud
- Application development environments
- Setting up the AWS infrastructure
- Summary
- AWS components
- Designing for and Implementing Scalability
- Defining scalability objectives
- Designing scalable application architectures
- Leveraging AWS infrastructure services for scalability
- Evolving architecture against increasing loads
- Event handling at scale
- Setting up Auto Scaling
- Summary 
- Designing for and Implementing High Availability
- Designing for and Implementing Security
- Defining security objectives
- Understanding the security responsibilities
- Best practices in implementing AWS security
- Setting up security
- Summary
- Deploying to Production and Going Live
- Designing a Big Data Application
- Introducing big data applications
- Analyzing streaming data with Amazon Kinesis
- Best practices for serverless big data applications
- Best practices for using Amazon EMR
- Best practices for distributed machine learning and predictive analytics
- Security overview for big data applications
- Configuring and using EMR-Spark clusters
- Summary
- Implementing a Big Data Application
- Deploying a Big Data System
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Using Amazon Kinesis Analytics
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