Data platform lead
Owns the lakehouse, and now owns a separate serving database too.
LATENCY · REAL-TIME SERVING
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
SRE or backend engineer
Owns the API that needs millisecond reads and the pipeline that feeds it.
Product engineer
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.
1system, not two
p95 < 2slatency at 1,000+ QPS
0data copies for the fast path
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.
TALK TO US
Book a demo on your own workloads
Reach out to book a demo, share challenges you’re facing, and tell us how this fits into what you’re currently working on or thinking about.
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