---
title: "OLTP"
description: "3 posts about OLTP, summarised, each linking to the original."
---

# OLTP
> 3 posts about OLTP, summarised, each linking to the original.

## Articles

### [OLTP is expensive, you can ClickHouse it.](https://yomu.fyi/post/oltp-is-expensive-you-can-clickhouse-it.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Oct 15, 2026
- Type: Problem & solution

PostgreSQL’s ACID guarantees cause substantial write amplification: each write flushes WAL for durability, copies rows via MVCC for isolation, and updates every affected index, amplifying 100 bytes of row change into tens of kilobytes of I/O. Costs scale linearly with data size, and even sharded Postgres offers only linear scaling with added coordination overhead, so growing datasets drive rising bills. The post proposes offloading settled, rarely-changing data to ClickHouse, whose columnar compression shrinks datasets 10-30x and whose cloud offering costs about 5x less than comparable Postgres infrastructure while also reducing Postgres compute load through fewer indexes. ClickHouse builds indexes in background batches and runs aggregations at roughly 100x lower cost than Postgres. ClickHouse Cloud provides WalShadow for millisecond-latency CDC and pg\_clickhouse FDW, enabling an application pattern that cuts the cost of serving settled data by an order of magnitude.


### [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.