DataVue runs a consumer credit data platform: wide database tables with thousands of attributes per record, hundreds of millions of records, and a platform plus a customer-facing SaaS reporting layer served off the same engine. Because the data is regulated, an architectural question that stays a convenience for most teams becomes a requirement here. DataVue has to be able to say exactly where every piece of data is processed, and by whom.
DataVue already ran its analytics on SingleStore. The one piece of custom logic that never had a clean home was the set of match keys at the center of how the platform connects records, and Python UDFs finally gave it one.
The shape of DataVue's workload
DataVue helps lenders, mortgage originators, auto finance providers, and other financial firms use credit and demographic data to reach consumers who are likely to convert. At the storage layer, the workload has a distinctive shape:
Wide bureau-style tables carrying thousands of attributes per consumer record, spanning credit, behavior, and offers.
Data volume in the multi-terabyte range, growing as new bureau relationships come online.
Two demanding workloads on one engine: the DataVue platform itself, and a customer-facing SaaS reporting layer.
On the previous stack of MySQL with a Redis cache, this architecture started to bend as the company grew. Data analytics on wide tables could run for minutes or longer, ingest jobs stretched into days, and a practical ceiling of around a thousand columns per database table forced awkward schema workarounds.
Moving to SingleStore relieved that pressure. Analytics that once took minutes came back in a fraction of the time, a representative ingest that had run for over an hour and a half dropped to a few minutes, and the team could finally model their data the way the business actually looked, without fighting a column ceiling.
That gave DataVue an engine fast enough to answer any question. It did not, on its own, give them a clean way to run their most important custom logic at scale.
The hard part: match keys on huge, wide tables
DataVue's work depends on matching. The platform has to join messy client lists to bureau data, build suppression files, and track who has already converted. Exact string matching does not survive contact with real data, because names get misspelled, people move, and the same address turns up in a dozen formats.
To absorb that noise, the team maintains several match keys. One uses a Soundex-style phonetic encoding, derived from a function originally standardized for postal address matching. The idea is simple to state: take a set of name and address fields, reduce them to a phonetic identifier, and let two records that describe the same person at the same place line up on that key even when the spelling differs.
The operational reality is heavier than the algorithm suggests. The central credit table is roughly four thousand columns wide, and joins to related datasets push the effective attribute surface past five thousand. The phonetic key is computed by the UDF across hundreds of millions of records every month, when a fresh bureau file lands, and again at a smaller scale every day, on files customers upload for self-service use cases.
Before Python UDFs, this single piece of logic lived in two places at once: a Python implementation in the external batch pipelines, and a matching SQL stored procedure inside SingleStore. One algorithm, two codebases, and two recurring costs.
The first cost was drift. Every change to the Python version had to be hand-translated into SQL and revalidated against real data. A difference in control flow, type handling, or one unhandled edge case is exactly the kind of thing that stays invisible until it surfaces in a reconciliation report weeks later.
The second was the price of running heavy string logic as pure SQL on the main cluster. The Soundex-based key is the most expensive of the set, and to finish the monthly run inside its window, the team had been scaling their SingleStore cluster up at the start of each month and back down afterward. They were sizing a production cluster around a few days of peak work, and paying for the boundary between the external Python jobs and the in-database procedure every time data crossed it.
Why the obvious fixes didn't fit
DataVue weighed the three standard options and ruled each one out for a specific reason.
Push more logic into SQL. Non-trivial business functions written in SQL become hard to read and harder to test. The Soundex computation is already a dense stack of case expressions, and the more it grows, the fewer people are willing to touch it. You also keep paying for that compute on the main cluster even though the job runs only a few days a month.
Run the Python in a separate service. This removes the duplication but pushes large slices of a regulated dataset out of the platform to be processed elsewhere and brought back. For DataVue that is the wrong trade: more credentials, more network paths, more monitoring across stacks, and a far harder answer when an auditor asks where the data was processed and by whom.
Move the work to an external feature store or compute platform. Reasonable when the data already lives there. DataVue's credit tables were already in SingleStore, so copying them out would only add latency, storage cost, and moving parts the team had no appetite for.
What DataVue wanted was narrower than any of these: keep the wide tables in SingleStore, keep one canonical copy of the key logic in a Python UDF, and run that UDF efficiently against hundreds of millions of records on a monthly batch.
What SingleStore Python UDFs do
Python UDFs were built for exactly this shape of problem. You write an ordinary Python function, annotate it with a decorator, and let the platform turn the function into a UDF service that SQL can call. When the notebook runs against a database, SingleStore registers the Python UDF and spins up a small set of containers in the same region as the engine. From then on, SQL calls it like any built-in function in the catalog.
Under the hood, the engine streams the input columns those containers need in batches, collects the results, and continues executing the query. The data never leaves the platform, and the team does not have to think about the plumbing.
For DataVue, that was the missing option: take the Python implementation of the match key the team already trusted, and run it inside the data platform instead of beside it.
What changed
One implementation, one source of truth. The key generation logic now lives in a single Python codebase, in the language the team prefers, backed by one set of tests, whether it is invoked from the monthly batch or an ad hoc query. An entire category of work, translating to SQL and reconciling the two versions against real data, simply went away.
The heavy compute moved off the main cluster without leaving the platform. The monthly run now executes on autoscaled containers that exist only while the keys are being computed. The engine handles storage and analytics; the UDF layer handles the heavy string processing. Monthly key-generation runtime fell by roughly half, with no permanent increase in cluster size and no more scaling the production cluster up and down around a few days of peak work.
Processing stayed observable and on-platform. The team watches container CPU and memory and reads logs in real time, inside the same platform, and the data stays where they can account for it end to end. For a company that answers regular, detailed questions from risk and compliance, keeping that visibility in one place matters as much as the speed.
There is one more payoff, and it compounds. This is now a repeatable pattern. The same Python UDF behind the phonetic key can carry the internal reporting transformations that still run in external batch jobs today, or any other custom logic that does not fit cleanly into SQL but belongs close to the data.
Closing thoughts
For a regulated-data platform, the appeal of Python UDFs is not only speed or cost. It is that you stop trading off between keeping your code close to the data and keeping it in the language you actually work in, without giving up the ability to say exactly where your data is processed. DataVue came away with one implementation, lower cost, and a cleaner compliance story at the same time. The takeaway is simple: with the right database primitives, teams no longer have to choose between keeping code close to their data and writing it in the language they actually use.
This post reflects an ongoing collaboration between the DataVue and SingleStore engineering teams. Performance figures describe DataVue's production workload and will continue to be validated as the pattern rolls out across additional jobs.
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