Yes, an operational database is the official answer for low latency on your lakehouse
For low latency performance, traditional guidance points teams to copying data from Delta into Postgres. Adding a compute engine to serve the Delta lakehouse directly removes the second system need.
THE PROBLEM
For low-latency serving on small tables, the recommended path is to copy the data into an operational database such as Postgres rather than serve it from Databricks or Delta, which means running and syncing a second system for the fast path.
THE PROBLEM ANATOMY
Who it hits
Owns the lakehouse, and now owns a separate serving database too.
Owns the API that needs millisecond reads and the pipeline that feeds it.
Owns the feature that depends on data the lakehouse cannot serve fast enough.
The situation
A team wants to expose a low-latency API over a few small tables that already live in the lakehouse. The recommended path is to move that data into an operational database, because the warehouse is not built for millisecond point reads.
That adds a second store to provision, a change-data-capture pipeline to keep it current, and two copies of the truth to reconcile. The lakehouse remains the source, and the fast path lives somewhere else.
The specific challenges
- Two systems: the fast path needs a separate database beside the lakehouse
- A sync pipeline: change data capture has to keep the copy current
- Duplicated truth: the same data lives in two places that can drift
- Freshness lag: the operational copy trails the lakehouse by the pipeline delay
WHAT IT COSTS
PRACTICAL
- Extra infrastructure: a serving database and a CDC job to build and run
- Reconciliation work: engineers chase mismatches between the two copies
BUSINESS
- Slower delivery: the serving feature waits on a pipeline, not a query
- Higher spend: a second system is provisioned for what the lakehouse already holds
EMOTIONAL
- Ownership creep: the platform team runs plumbing it never wanted
- Fragile confidence: two copies mean two chances for the number to be wrong
THE WAY OUT
A lakehouse compute engine with high-concurrency serving
Swap in a pilot of e6data's query engine alongside the existing lakehouse to:
Serve from the lakehouse: point the API at the engine and read the open tables directly, so the fast path uses the same data as everything else. The tables and catalog stay in place. What is removed is the separate operational database.
Drop the sync: with one system serving reads, there is no change-data-capture pipeline to build or reconcile. The existing ingestion into the lake stays. What gets returned is a single copy of the truth.
Keep the stack: Iceberg, Delta, and Hudi tables stay put, access controls carry over, and adoption is an endpoint change rather than a migration, needing only fast validation of the required integration mapping. What is added is low-latency serving on the tables already governed.
The result is a low-latency read path on the lakehouse itself, with no second store to sync. Setup and connection details are in the query engine documentation.
See how the Query engine serves the lakehouse Share your problem, talk with a founding team member →Book a demo on your own workloads
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