Architecting Data-Intensive SaaS Applications

Table of contents

1. 1. Data Applications and Why They Matter
   1. Data Applications Defined
      1. Customer 360
      2. IoT
      3. Machine Learning and Data Science
      4. Application Health and Security
      5. Embedded Analytics
   2. Summary
2. 2. What to Look For in a Modern Data Platform
   1. Benefits of Cloud Environments
      1. Cloud-First Versus Cloud-Hosted
      2. Choice of Cloud Service Providers
   2. Support for Relational Databases
   3. Benefits of Relational Databases
   4. Separation of Storage and Compute
   5. Data Sharing
   6. Workload Isolation
   7. Additional Considerations
      1. Reliability
      2. Extensibility
   8. Summary
3. 3. Building Scalable Data Applications
   1. Design Considerations for Data Applications
      1. Design Patterns for Storage
      2. Design Patterns for Compute
      3. Design Patterns for Security
   2. Summary
4. 4. Data Processing
   1. Design Considerations
      1. Raw Versus Conformed Data
      2. Data Lakes and Data Warehouses
      3. Schema Evolution
      4. Other Trade-offs
   2. Best Practices for Data Processing
      1. ETL Versus ELT
      2. Schematization
      3. Loading Data
      4. Serverless Versus serverful
      5. Batch Versus Streaming
   3. Summary
5. 5. Data Sharing
   1. Data Sharing Approaches
      1. Sharing by Copy
      2. Sharing by Reference
   2. Design Considerations
      1. Sharing Data with Users
      2. Getting Feedback from Users
   3. Data Sharing in Snowflake
   4. Snowflake Data Marketplace
   5. Snowflake Secure Data Sharing in Action: Braze
   6. Summary
6. 6. Summary and Further Reading