Operational databases: How they work and when to use them
Summary
The article differentiates between Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) models, highlighting their distinct purposes: OLTP for real-time transactional data storage and updates, and OLAP for business intelligence and analytical reporting. Traditional OLTP systems, optimized for write-heavy, row-level operations with normalized schemas, fall short for modern AI and real-time analytical workloads due to limitations like data silos, lack of unstructured data support, rigid schemas, and scalability challenges. Modern data applications require unified operational and analytical capabilities, support for diverse data types including vector data, built-in governance, and elastic scalability. Databricks Lakebase addresses these gaps by offering a hybrid solution with separate storage and compute, low-cost durable storage, elastic serverless Postgres, instant branching, and unified transactional and analytical workloads, built on the Databricks Platform using Delta Lake, Mosaic AI, and Unity Catalog.
Key takeaway
For CTOs and VPs of Engineering evaluating data infrastructure for AI-driven applications, traditional OLTP systems are insufficient. You should consider hybrid solutions like Databricks Lakebase that unify operational and analytical workloads, support diverse data types including vectors, and offer elastic scalability to avoid data silos and enable real-time AI applications. This approach streamlines data ingestion and makes fresh operational data immediately available for advanced analytics and AI.
Key insights
Modern data applications require unified operational and analytical capabilities beyond traditional OLTP systems.
Principles
- Separate storage and compute for scalability.
- Unify transactional and analytical workloads.
- Support diverse data types, including vector data.
Method
Connect existing OLTP systems via CDC or streaming pipelines into Delta Lake to make operational data immediately available for SQL analytics, BI, ML, and AI agents.
In practice
- Implement real-time fraud detection.
- Power AI agents with live inventory data.
- Develop copilots using up-to-date account information.
Topics
- OLTP Systems
- OLAP Systems
- AI Applications
- Databricks Lakebase
- Lakehouse Architecture
Best for: CTO, VP of Engineering/Data, Director of AI/ML, Data Engineer, AI Architect, MLOps Engineer
Related on AIssential
Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.