---
title: "OLAP"
description: "2 posts about OLAP, summarised, each linking to the original."
---

# OLAP
> 2 posts about OLAP, summarised, each linking to the original.

## Articles

### [OLTP vs. OLAP: Choosing Between Transactional and Analytical Databases](https://yomu.fyi/post/oltp-vs-olap-choosing-between-transactional-and-analytical-databases.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Oct 14, 2026
- Type: Explainer

OLTP handles high-volume, low-latency transactions on current operational data for online banking, e-commerce, and inventory, while OLAP handles complex analytical queries on large historical datasets for financial analysis and business intelligence. OLTP databases ensure data integrity through ACID compliance and use normalized row-oriented tables; OLAP systems use denormalized dimensional models and columnar storage for efficient scans and aggregations. Data pipelines such as ETL or CDC tools like Debezium connect OLTP and OLAP systems, but latency and pipeline complexity rise with analytical freshness requirements. For mixed workloads, HTAP or lakehouse architectures can reduce synchronization overhead and analytical lag. The choice between OLTP and OLAP depends on the workload’s read-write pattern, latency needs, and data volume, with TPC-C and TPC-H as reference benchmarks.


### [What is HTAP? A plain-English guide to hybrid transactional/analytical processing](https://yomu.fyi/post/what-is-htap-a-plain-english-guide-to-hybrid-transactional-analytical.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Oct 14, 2026
- Type: Explainer

HTAP (Hybrid Transactional/Analytical Processing) brings transaction processing and analytics together so applications can run analytical queries on current operational data without waiting for batch data pipelines. This architecture addresses the delay caused by traditional separation of OLTP and OLAP systems, where data had to be copied into a data warehouse before analysis. HTAP designs vary: converged systems use a single database engine for both workloads, while composed systems use separate engines connected through shared storage or replication. The central challenge is providing analytical access to fresh operational data without compromising transactional performance and reliability. For workloads needing independent scaling, Lake Transactional/Analytical Processing (LTAP) offers a unified storage layer under one governance model, reducing the need for change data capture and replication pipelines.