Data and Backend Engineering
Data and backend engineering is the work that makes everything built on top of your data trustworthy: getting it in reliably, storing it so it can be queried quickly, and making sure the same question returns the same answer everywhere it is asked. It is usually the least visible part of an automation project and the most common reason one fails.
The symptoms
You recognize this work by what people complain about. A report takes hours to run. Two dashboards disagree and nobody can say which is right. A pipeline broke three weeks ago and the first sign was a number that looked slightly low. Someone maintains a spreadsheet because they do not trust the system.
None of those are presented as data problems. They arrive as “the system is slow” or “the numbers are wrong”, and the fix is upstream of where the complaint lands.
Measure first, then re-engineer
Performance work without a baseline is guesswork with a budget. The runtime and the query profile get recorded before anything changes, so the improvement can be shown rather than claimed.
During a prior enterprise engagement, before Datasmarts existed, re-engineering the query layer of a critical data process brought its runtime from 2 hours to 9 minutes, with the database layer left in place. The work was in finding which part of the layer was actually responsible, which is where most of the time in this kind of project goes.
Reliability is a feature of the pipeline
An ingestion pipeline that silently delivers less data is worse than one that stops. On a filings pipeline covering 44 sources, the run recorded zero polling failures. That matters because a missing source does not announce itself downstream. It shows up as a total that is a little too low, and nobody checks a total that looks plausible.
So pipelines get monitoring on the pipeline, not only on the output: what was expected, what arrived, and an alert when those disagree.
This is the foundation, not the project
Nobody wants to buy data engineering. It is worth doing when it is blocking something you do want, and the honest sequence is to find that out during the assessment rather than halfway through building on top of it.
The problem
The automation you want sits on top of data that is slow, scattered across systems, or does not agree with itself from one report to the next.
The outcome
A data layer that is fast and consistent enough that the systems built on top of it can be trusted without a manual check.
What you get
- Ingestion pipelines that pull from your sources on a schedule and fail loudly when a source changes
- A query layer tuned against the access patterns you actually have, not the ones the schema assumed
- Schema and data model work, so the same question returns the same answer in every system
- Backend services and APIs that the rest of your tooling can build on
- Monitoring on the pipeline itself, so a silent gap in the data is visible before someone reports it
- Documentation of where each number comes from, which is what makes reporting defensible
How the work runs
Trace the numbers
Measure before changing
Re-engineer the layer that is actually slow
Make failure visible
Related work
2 hours
runtime of a critical data process before the work
From 2 hours to 9 minutes: re-engineering an enterprise data process
a multi-billion dollar enterprise client
roughly $77M
political TV ad spend tracked and attributed
Turning FCC filings into roughly $77M of tracked political ad spend
a US political advisory practice
Questions about this service
Our reports are slow. Is that a database problem or an infrastructure problem?
More often the query layer than the hardware, and it is worth measuring before buying anything. During a prior enterprise engagement, before Datasmarts existed, re-engineering the query layer of a critical data process cut its runtime from 2 hours to 9 minutes, with the database layer left in place. Scaling up would have cost money and hidden the actual problem.
Why do two of our systems report different numbers for the same thing?
Almost always a definition problem rather than a data problem. Two systems count slightly different populations, or cut the period at different boundaries, and both are internally correct. Tracing each number back to its source and writing the definitions down is the fix, and it usually has to happen before any automation on top of that data is worth building.
Can you work with the stack we already have?
Yes, and that is the default. Replacing a working data store is expensive and rarely the reason things are slow. We work with the databases, warehouses, and services you run, and recommend a change only where there is a specific cost or reliability reason we can point at.
Do we need this before we can do anything with AI?
Sometimes, and it is worth finding out early. A retrieval system built on data that contradicts itself will produce confident, wrong answers, and the failure will look like an AI problem. If an assessment finds the data layer is the blocker, fixing it is the first project rather than a prerequisite nobody budgeted for.
Not sure which of these you need?
That is the usual starting point, and it is what the assessment is for. Tell us which process is costing you the most time and we will tell you whether it is worth automating.