---
title: "Operational databases: How they work and when to use them"
description: "Operational databases, or online transaction processing (OLTP) databases, support day-to-day operations by processing real-time CRUD transactions with low latency, high concurrency, and ACID guarantees. The guide contrasts them with data warehouses and OLAP systems, which favor historical analysis, complex aggregations, and read-heavy workloads, while describing ETL, CDC, and operational data stores in the OLTP-to-OLAP pipeline. It argues that traditional OLTP systems can struggle with real-time analytics and AI because of siloed data, structured-data constraints, rigid schemas, scaling limits, and missing governance features. Databricks Lakebase is presented as a hybrid solution using separate storage and compute, serverless Postgres, elastic scaling, branching, shared lakehouse storage, and open formats. The proposed workflow connects existing OLTP systems through CDC or streaming into Delta Lake for fresh SQL, BI, ML, and AI access."
---

# Operational databases: How they work and when to use them

[Databricks](https://yomu.fyi/company/databricks) · Databricks Staff · Apr 24, 2026

**Type:** Explainer

## Summary

Operational databases, or online transaction processing (OLTP) databases, support day-to-day operations by processing real-time CRUD transactions with low latency, high concurrency, and ACID guarantees. The guide contrasts them with data warehouses and OLAP systems, which favor historical analysis, complex aggregations, and read-heavy workloads, while describing ETL, CDC, and operational data stores in the OLTP-to-OLAP pipeline. It argues that traditional OLTP systems can struggle with real-time analytics and AI because of siloed data, structured-data constraints, rigid schemas, scaling limits, and missing governance features. Databricks Lakebase is presented as a hybrid solution using separate storage and compute, serverless Postgres, elastic scaling, branching, shared lakehouse storage, and open formats. The proposed workflow connects existing OLTP systems through CDC or streaming into Delta Lake for fresh SQL, BI, ML, and AI access.

## Context

Modern applications require real-time analytics, flexible access to structured, semi-structured, unstructured, and vector data, elastic scaling, governance, and low-latency integration with AI and ML workflows. Traditional OLTP systems are described as limited by siloed data, rigid schemas, scaling challenges, and insufficient support for some AI and governance requirements.

## Approach / What changed

The guide presents Databricks Lakebase as a way to bridge operational and analytical workloads through separate storage and compute, serverless Postgres, elastic scaling, database branching and cloning, shared lakehouse storage, and open formats. It recommends connecting existing OLTP systems through CDC or streaming pipelines into Delta Lake.

## Takeaways

- OLTP databases prioritize current-state data, high-frequency row-level CRUD operations, high concurrency, millisecond latency, and ACID guarantees.
- OLTP systems favor normalized schemas and write-heavy workloads, while OLAP systems use denormalized schemas and complex, read-heavy aggregations over historical data.
- Lakebase is described as providing serverless Postgres compute with lake-backed storage, allowing operational data to become available for SQL analytics, BI, ML workflows, and AI agents through CDC or streaming.

**Tags:** [Databricks](https://yomu.fyi/topic/databricks), [Delta Lake](https://yomu.fyi/topic/delta-lake), [Lakehouse](https://yomu.fyi/topic/lakehouse), [Postgres](https://yomu.fyi/topic/postgres), [Streaming](https://yomu.fyi/topic/streaming)

- Source: [Databricks](https://www.databricks.com/blog/operational-databases)
- Source URL: https://www.databricks.com/blog/operational-databases
- Ingested by Yomu: 2026-08-31T03:41:15.637Z

[Read original post](https://www.databricks.com/blog/operational-databases)