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Kevin McGinley

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Designing a Modern Application Data Stack

Today's massive datasets represent an unprecedented opportunity for organizations to build data-intensive applications. With this report, product leads, architects, and others who deal with applications and application development will explore why a cloud data platform is a great fit for data-intensive applications. You'll learn how to carefully consider scalability, data processing, and application distribution when making data app design decisions. Cloud data platforms are the modern infrastructure choice for data applications, as they offer improved scalability, elasticity, and cost efficiency. With a better understanding of data-intensive application architectures on cloud-based data platforms and the best practices outlined in this report, application teams can take full advantage of advances in data processing and app distribution to accelerate development, deployment, and adoption cycles. With this insightful report, you will: Learn why a modern cloud data platform is essential for building data-intensive applications Explore how scalability, data processing, and distribution models are key for today's data apps Implement best practices to improve application scalability and simplify data processing for efficiency gains Modernize application distribution plans to meet the needs of app providers and consumers About the authors: Adam Morton works with Intelligen Group, a Snowflake pure-play data and analytics consultancy. Kevin McGinley is technical director of the Snowflake customer acceleration team. Brad Culberson is a data platform architect specializing in data applications at Snowflake.

Architecting Data-Intensive SaaS Applications

Through explosive growth in the past decade, data now drives significant portions of our lives, from crowdsourced restaurant recommendations to AI systems identifying effective medical treatments. Software developers have unprecedented opportunity to build data applications that generate value from massive datasets across use cases such as customer 360, application health and security analytics, the IoT, machine learning, and embedded analytics. With this report, product managers, architects, and engineering teams will learn how to make key technical decisions when building data-intensive applications, including how to implement extensible data pipelines and share data securely. The report includes design considerations for making these decisions and uses the Snowflake Data Cloud to illustrate best practices. This report explores: Why data applications matter: Get an introduction to data applications and some of the most common use cases Evaluating platforms for building data apps: Evaluate modern data platforms to confidently consider the merits of potential solutions Building scalable data applications: Learn design patterns and best practices for storage, compute, and security Handling and processing data: Explore techniques and real-world examples for building data pipelines to support data applications Designing for data sharing: Learn best practices for sharing data in modern data applications