The Essential Guide to

Transactional, Analytical

and Hybrid Data

Learn how unifying different types of data on one platform simplifies your AI architecture and helps you power all your workloads, both old and new.

Many businesses still operate with data spread across disconnected systems. Transactional systems capture what’s happening right now, while analytical systems explain what happened in the past. Hybrid workloads blend the two, aiming to give modern apps both speed and historical context.

The gaps between these systems create a problem for AI systems and agents that need fast access to both real-time signals and historical knowledge to deliver accurate, relevant responses. The solution requires a shift to focus on unifying transactional, analytical and hybrid workloads on a unified platform. This approach can reduce complexity, simplify governance and streamline access, bringing your data together in a way that makes it ready for AI to use and unlocks new use cases across your organization.

In this guide, we’ll explore:

• The roles of transactional, analytical and hybrid data
  
  
• The advantages of integrating different types of data workloads on a single cloud data platform
  
  
• How an integrated platform approach supports AI success
  
  
• How Snowflake Unistore, Snowflake Postgres and Integrated Analytics help you realize the power of all your data to build the apps and AI agents that move your business forward