What is zero-ETL and what problem does it solve?

Summary

• Zero-ETL eliminates manual pipelines between operational and analytical databases. Instead of building CDC pipelines, ETL jobs, and batch syncs, zero-ETL makes data available to both workloads from a single copy.
• The problem: fragmented systems. OLTP (transaction databases) and OLAP (analytics) have conflicting needs. Apps require low-latency writes; analytics require large scans. Keeping them in sync traditionally required expensive, error-prone pipeline engineering.
• The solution: one storage layer for both. Lakebase (the operational database) and the lakehouse share the same underlying storage, so data written by the app is instantly queryable by analytics without replication.
• One copy means less work. With unified storage and governance through Unity Catalog, you avoid schema drift, silent failures, redundant sync jobs, and constant maintenance.
• Enabled by Lake Transactional/Analytical Processing (LTAP). LTAP combines serverless Postgres with the lakehouse to unify operational and analytical workloads on shared storage.

What is zero-ETL and what problem does it solve?

Zero-ETL means running operational applications and analytics workloads against a single copy of data, without manual pipelines to sync between them. Traditional architectures force a choice between two databases: one for applications (OLTP) and one for analytics (OLAP). Keeping them synchronized requires building ETL jobs, CDC pipelines, and custom sync logic. This work is error-prone, expensive, and never-ending.
Zero-ETL eliminates this burden. Data written by your application becomes immediately available to analytics queries without any intermediate pipeline or replication.

The core problem: separate systems, constant sync work

Operational databases (OLTP) and analytics platforms (OLAP) have fundamentally different performance requirements:

System	Needs	Consequence
OLTP (app database)	Low-latency reads/writes; high concurrency	Poor for bulk analytical queries
OLAP (analytics)	Large sequential scans; aggregations	Too slow for application queries

Historically, this meant two separate systems. Syncing them required one or more of:

• Manual ETL: scheduled pipelines extracting, transforming, and loading data into the warehouse, introducing hours of lag.
• CDC complexity: Change Data Capture systems (Debezium, Kafka) streaming changes in real-time, adding infrastructure and operational load.
• Schema drift: when the app schema changes, the warehouse schema must too, risking silent data failures.
• Fragmented governance: separate access controls, audit logs, and lineage tracking across two systems, making troubleshooting hard.

The solution: shared storage, unified access

Instead of two separate databases, zero-ETL uses one underlying storage layer that both operational and analytical systems query directly.
Lakebase (Databricks' serverless Postgres) stores operational data in open, columnar format on your object storage. The lakehouse queries the same data without copying. Key points:

• Applications write to Lakebase using standard Postgres connections. A high-performance operational cache serves low-latency reads and writes.
• Data is transcoded into shared storage within minutes.
• Analytics query the lakehouse using Spark or SQL against the same data.
• Both use Unity Catalog governance: one access model, one audit log, one lineage view.

Result: no ETL, no CDC, no pipelines, no delays, no schema drift.

LTAP: the architecture

Lake Transactional/Analytical Processing (LTAP) is Databricks' zero-ETL architecture:

1. Lakebase: fully managed serverless Postgres, separating compute and storage.
2. Lakehouse: analytics platform (Spark, SQL) on shared storage.
3. Synced Tables and Change Data Feed: automatic sync for reverse-ETL and operationalization.
4. Unity Catalog: unified security, audit, and lineage.

With LTAP, an app writes to Lakebase. Within minutes, the same data is queryable from the lakehouse. Analytics get fresh data without copying; Lakebase performance is unaffected.

Use cases for zero-ETL

• Feature stores: serve ML features from operational data without stale copies.
• Agent memory and RAG: store and query agent state instantly.
• Reverse-ETL: publish analytics results to power app features, no pipelines.
• Real-time dashboards: query operational data directly.
• Compliance: one copy with unified lineage for audit trails.

Getting started

1. Set up Lakebase as your operational database.
2. Enable Synced Tables if you need bidirectional sync.
3. Query from the lakehouse against the shared storage.
4. Govern with Unity Catalog.

FAQs

Is zero-ETL only for new applications?

No. Migrate an existing app database to Lakebase and get zero-ETL benefits immediately. Your app connects unchanged.

Does zero-ETL hurt app performance?

No. Lakebase separates compute and storage. App queries hit a fast cache; analytics queries run on shared storage separately, with no interference.

How fresh is the data for analytics?

Lakebase automatically transcodes to shared storage within minutes. For sub-minute updates on specific tables, use Synced Tables.

How do schema changes work?

Both app and analytics queries read from the same logical data model in Unity Catalog, so updates propagate instantly without coordination.