How does the system stay consistent when transactions and analytics read the same data?

Summary

• Lakebase stores data once in open columnar formats (Delta and Iceberg as Parquet), eliminating separate OLTP and OLAP copies that could drift apart.
• Analytical queries achieve transactional consistency by fetching a log sequence number (LSN) from Postgres, reading bulk data from object storage, and merging recent unmaterialized changes.
• Storage-level transcoding automatically converts Postgres row format to columnar form as data is materialized, with no external pipeline required.
• Both transactional and analytical workloads read from the same physical storage, so consistency is guaranteed by design.

How does the system stay consistent when transactions and analytics read the same data?

Lakebase eliminates consistency drift by storing data once in open columnar formats, with both transactional and analytical workloads reading from that single copy. Because there is only one source of truth, transactions and analytics cannot read different versions of the same data.
Traditional systems maintain separate operational and analytical copies kept in sync by external processes like CDC or batch ETL. These pipelines inevitably introduce lag and create opportunities for data drift. Lakebase takes a different approach: the storage layer itself is the unification point. As your application writes data through Postgres transactions, the storage layer automatically transcodes those writes into columnar Parquet files in object storage. When analytics queries arrive, they read directly from those same files. Because both read the same physical storage, consistency is built into the architecture.

What is transactional consistency?

Transactional consistency means that all queries, whether transactional or analytical, see a snapshot of the database at a specific point in time. ACID compliance guarantees that transactions appear atomic: other queries never see partial results or intermediate states. For analytics to run correctly on live operational data, they must see the same consistent snapshot that transactions rely on.

Key capabilities to evaluate

Capability	Why it matters
Single storage copy	Eliminates data drift by removing the need to synchronize separate OLTP and OLAP systems
Snapshot isolation	Guarantees analytics see a consistent view of data at a specific LSN, with no partial or intermediate states
Storage-level transcoding	Automatically converts row format to columnar without an external process that could fail or lag
No replication latency	Updates are visible to analytics as they materialize to the lake, no batch windows or polling
Columnar format	Enables fast analytical scans while preserving exact Postgres value representation

How Databricks approaches consistency

Databricks Lakebase uses a three-step process to keep analytics consistent with transactions.
When an analytical query arrives, it first fetches the log sequence number (LSN) from Postgres. The LSN marks a position in the write-ahead log and acts as a consistency point in time. This is a cheap metadata lookup that costs almost nothing. Next, the analytical engine reads the bulk of data from the columnar Parquet files in object storage as of that LSN. Because these files are already in columnar format optimized for analytics, this scan is fast. Finally, for very recent changes not yet materialized to the lake (those that arrived after the LSN was captured), the engine fetches them from the pageserver and merges them on top. This merge ensures the query sees all committed data up to the LSN.
Under the hood, the storage layer handles transcoding. As the pageserver materializes pages from the write-ahead log into object storage, it automatically converts them from Postgres row format to columnar Parquet form. This transcoding preserves the exact representation of every value, ensuring that analytics see the same data your application wrote. Because this happens asynchronously as part of normal storage operation, there is no separate process to manage or debug.
For implementation details, see the Lakebase and LTAP overview documentation.

Use cases

Consistency matters whenever analytics must inform real-time decisions.

• Real-time fraud prevention: Detect suspicious patterns in live transactions without waiting for a batch window or risking inconsistent snapshots.
• Inventory decisioning: Sync analytics on current stock levels with order-processing transactions so recommendations never oversell.
• Operational dashboards: Monitor live application health and user activity with the confidence that analytics reflect the current state.
• Risk dashboards: For financial institutions, track live exposure with guarantees that you are not seeing a partial or stale view of transactions.

FAQs

What prevents data from drifting between OLTP and OLAP systems?

There is no separate OLAP system to drift. Lakebase stores data once in open formats. Both workloads read from that single copy. Since there is only one source of truth, drift is impossible by design.

Is the consistency real-time?

Yes. Analytics read up-to-the-second data. The storage layer continuously materializes new writes to columnar form. As soon as a transaction commits, analytics can read it (though unmaterialized data within the current LSN window is fetched directly from the pageserver).

How does this compare to CDC or log-based replication?

CDC and log-based replication are external processes that continuously poll the database and copy changes to another system. This introduces complexity, cost, and the risk of lag or failure. LTAP's storage-layer transcoding is built into the write path itself. There is nothing to opt into, configure, or monitor. Every table automatically exists in both operational and analytical form.

Does analytics load affect transaction consistency?

No. Analytics run on separate compute (Lakehouse compute) and read from object storage, never from the Postgres instance. This isolation means heavy analytics cannot interfere with transaction processing or consistency guarantees.