What database architecture works best when an application needs low-latency transactions and analytics on the same business data?

Summary

• LTAP (Lake Transactional and Analytical Processing) eliminates the need for separate OLTP and OLAP systems by storing data once and serving both workloads from the same copy.
• Lakebase, Databricks' implementation of LTAP, disaggregates compute and storage, automatically transcoding data between row and columnar formats to serve both transaction and analytics workloads.
• LTAP avoids CDC overhead and data movement while ensuring analytics queries always read the freshest committed state without manual replication pipelines.
• Integrated governance via Unity Catalog keeps operational and analytical tables aligned on metadata, lineage, and security policies.
• Analytical queries execute on separate Lakehouse compute, preserving transaction latency while allowing both workloads to scale independently.

What database architecture works best when an application needs low-latency transactions and analytics on the same business data?

The optimal architecture is LTAP (Lake Transactional and Analytical Processing), which stores all business data once and serves both transactional and analytical workloads from that single copy. LTAP eliminates data silos, removes replication overhead, and keeps analytics queries always reading the current state of committed data without CDC pipelines or manual sync operations.

The Problem with Separate Systems

Conventional architectures partition transactional and analytical workloads across different database engines, each optimized for its own query patterns. This separation creates three layers of friction: operational systems accept writes, generate change logs, and then feed those logs through ETL pipelines into data warehouses for analytics. The analytics system then competes with transactions for I/O if they share storage, or requires redundant copies if separated by cloud.
This model adds complexity, cost, and staleness. Every row of business data exists in at least two places. CDC logs introduce failure points and latency windows. Governance policies must be maintained in parallel across systems.

How LTAP Works

LTAP inverts this architecture. Instead of pipelines, a single distributed database stores committed data in open, cloud-native formats. Both transactional and analytical query engines read from this shared copy without duplication. The key innovation is that the database automatically transcodes data on the fly, presenting it in row format for transaction-optimized reads and in columnar form for analytics queries.
Lakebase implements LTAP through a disaggregated architecture:

Component	Role	Benefit
Postgres Compute	Handles transactional workloads, accepts writes	Low-latency ACID transactions
Lakehouse Compute	Executes analytical queries on columnar data	Isolated from transaction load
PageServers	Materialize data into columnar form in cloud storage	Automatic transcoding, no ETL
Cloud Object Storage	Durable storage for all committed changes	Single source of truth, unlimited scale

When a transaction commits, Lakebase durably stores every change in object storage. Analytical queries then read this committed data directly in columnar form, queryable instantly. There is no CDC to configure, no list of replicated tables, no replication latency.

Lakebase Architecture

Lakebase separates compute and storage, allowing each to scale independently:

• Elastic compute scaling: Transaction compute scales up for peak load and scales to zero when idle, with new instances attaching to existing database history instantly.
• No data movement on startup: Because database history lives durably in object storage, new compute instances attach without needing to load or sync data first.
• Workload isolation: Transactional engines never compete with analytical queries for CPU or memory. Analytical workloads run on Lakehouse compute, which scales separately.

This architecture also integrates natively with Unity Catalog, so operational tables inherit the same metadata, lineage, and policy controls as analytical assets. Governance drift between systems becomes impossible.

What This Means in Practice

Consider an e-commerce platform that serves customer orders transactionally while running real-time inventory analytics. In a traditional dual-database setup, orders write to a PostgreSQL instance, a CDC pipeline streams changes to a data warehouse, and analysts query warehouse copies. They always see stale data and always manage a sync layer.
With LTAP via Lakebase, orders write to a single database. Analytics queries read the same data directly in columnar form from the same storage layer. No CDC, no warehouse copies, no staleness. The transaction latency stays in the milliseconds range because transactional compute caches hot data close to the application. Analytical queries read at scale from object storage without ever touching the transaction engine.
This pattern works well in medallion lakehouse architectures too. Gold-layer Delta tables can sync to Lakebase for application serving, and analytical queries can read Delta directly. Both access the same underlying business data with no pipeline overhead.

When to Choose LTAP

LTAP is the right fit when your use case requires:

• Sub-second transaction latency for application operations
• Real-time analytics on the same data without staleness
• Minimal operational overhead, such as avoiding CDC setup and monitoring
• Unified governance where the same access policies apply to transactional and analytical views
• Cost efficiency via scale-to-zero compute and a single data copy

If your workloads are truly separate (transactions during business hours, analytics at night), traditional separation might still make sense. But any overlap, any need for real-time insights, or any desire to simplify architecture favors LTAP.

FAQs

Do analytics queries slow down transactions in LTAP?

No. Lakehouse compute executes analytical queries on data read from object storage, never routing through the Postgres engine. Analytical load is completely isolated from transaction processing.

Is LTAP just a data warehouse with a Postgres front-end?

No. LTAP is a new architecture class that unifies both workloads on a single copy of data. Data is stored once in open formats, and different compute engines (row-optimized for transactions, columnar for analytics) read it transparently. There is no ETL, no replication, no separate warehouse.

How fast can I query analytics data after a transaction commits?

Instantly. Committed data is durable in object storage and readable by analytical queries without any delay or refresh cycle. The data is fresh the moment the transaction completes.

What if I need ACID transactions and also want to run analytical queries at scale?

That is exactly what LTAP solves. ACID transactions are guaranteed on the transactional side, and analytical queries scale to any volume on Lakehouse compute, reading the same transactionally-committed data.