Today, every application is a data application. Data applications process large amounts of rapidly changing data to implement many use cases including customer 360, application health and security analytics, IoT, machine learning, and embedded analytics.
Building data applications that scale cost-effectively and deliver great user experiences requires teams to make key technical decisions early in the development cycle. Download this report to learn how to make these and other important decisions for your specific application.
You will learn:
- The common use cases, including data science and machine learning (ML), and the key challenges each
presents - What to look for in a data platform for building data applications, including a review of the cloud-first and cloud-hosted models
- Design patterns to optimize compute and storage in multi-tenant data applications, including advice on when to pool or isolate resources for tenants
- How to build data pipelines for data ingestion, including the tradeoffs between ETL and ELT
- Best practices for sharing data securely with customers and partners, including a review of the sharing-by-reference and sharing-by-copy models
This O’Reilly report is written for product managers, architects, and engineering teams that are building data intensive SaaS applications. It uses the Snowflake Data Cloud to illustrate best practices.