Aviation data at the scale and speed the industry actually runs at.
We build and modernise the data platforms behind airlines, travel businesses, and aviation data companies, where the volume is enormous, the answers are commercial commitments, and the value of a data point starts falling the moment it is captured.
The constraint
Aviation data has a shape that most high-volume data does not. It arrives continuously from aircraft, systems, and third parties on different clocks and in different formats, it is worth most in the minutes after it is captured and steadily less afterwards, and the decisions taken on it, a fare, a route, a maintenance call, are commercial commitments rather than dashboard readings. A platform that treats it as a nightly batch is not slow, it is producing answers about a situation that has already changed.
One of the data platforms we have built at this scale, and the decisions it turned on.
A real-time aviation data platform
We were asked to take on a platform another team had built and grown, by then processing enormous daily volumes of flight data on servers billed the same whether the platform was busy or not. The system worked. It had simply outgrown every assumption it was built on, and the bill grew whether or not the business did.
Infrastructure was sized to the work. A monolith on fixed servers became event-driven services that scale on demand, with infrastructure defined as code and releases that roll back. Idle capacity stopped being something anyone paid for, and capacity planning stopped being a manual job.
And cost that looked like infrastructure turned out to be decisions. Architecture and coding standards are decisions, and left unmade at the start they get made accidentally by whoever is closest to the deadline. At this volume those accidents were paid for on every run. Setting them cost nothing to buy.
We also work on airline pricing data at scale, and on platforms we cannot name. The full set is in our case studies.
What we would expect to deal with on your platform
Not a service list. Four problems that show up on most aviation data platforms, and the decision we made when each showed up on ours.
Data arrives on different clocks. A telemetry reading, a schedule change, and a pricing update are not three points on one timeline, and treating them as though they were produces confident answers about a situation that never existed. Each input keeps the time it was true separately from the time it arrived.
A late answer is not a slow answer, it is a different answer. Insight that arrives after the decision window has closed is not degraded, it is unusable, which is why batch was the problem rather than the batch size.
Volume that arrives in waves cannot be met with capacity provisioned for the peak. Paying for the busiest hour all year is how this class of platform becomes unaffordable at exactly the point it becomes valuable.
A backfill is bought once and read forever. History has to be stored rather than re-requested, because model training and analysis are both read-heavy against the same data and re-fetching it is the largest avoidable cost in a platform like this.
Where the cost actually goes
The obvious half is infrastructure. Always-on servers sized for a peak that arrives rarely, replaced by services that scale to demand and are billed only while they run.
The half nobody looks at is the code. At millions of records a day, how the processing handles memory and repetition matters more than the servers it runs on. Choices that were entirely reasonable at the original scale become the largest line in the run at this one, and they never appear as a line item because they arrive labelled as infrastructure.
And the part that makes it repeatable. Neither half required buying anything. That is the test for whether a cost reduction is an engineering result or a procurement one, and it is the reason the same saving is available on most platforms of this age and size.
Reliability at this volume
Infrastructure as code, so the environment is reproducible rather than remembered, and a release has a tested way back.
Observability that answers where the time went, because at this volume a slow pipeline is a cost question before it is a performance one.
A pipeline that fails to a known state. A partial run that nobody notices is worse than a failed one that alerts, because the second is a delay and the first is a report somebody acts on.
Migration in stages, while the current system runs. Nobody replatforms an aviation data pipeline by switching it off, and the ability to run both and cut over by area is what makes the project possible at all.
Which trust services criteria this work is judged on
Availability, which on a data platform means the pipeline keeps up rather than the service responds. A platform that is technically up and eight hours behind has failed at the only thing it exists to do.
Processing integrity, which is the criterion this work lives on. A record that arrived must be processed exactly once, in the state it arrived in, and a result must be traceable to the inputs that produced it. At this volume, silent partial processing is the failure mode that costs most, because nothing errors and the numbers are simply wrong.
Confidentiality, over commercially sensitive pricing and operational data, where an airline's fare structure and load factors are competitive information.
The boundary, stated plainly. The control set is designed against the criteria rather than mapped to them afterwards. That is a statement about how a platform was built, not a claim to hold an attestation, and where a client needs one, the work is theirs to complete with an auditor. We don't claim certifications we don't hold.
What the platform actually does
Each figure stated once, here, with its scope.
How you'd work with us
Pick the level of ownership that suits you.
Staff augmentation
Add senior data engineers to your team.
Dedicated team
A committed team that runs like your own.
Full delivery
Hand over the build and we deliver it end to end.
Frequently asked questions
Can you handle aviation-scale data volumes in real time?
Yes. The platform described above processes flight data continuously as it arrives, with event-driven services that scale to the wave rather than the peak, so real time holds on the busiest day as well as the quietest.
Can you modernise an existing batch pipeline without switching it off?
Yes, and it is the only way we would do it. Migration runs in stages while the current system keeps operating, with both running side by side and cutover by area, so the business never loses the answer it depends on while the platform underneath is replaced.
How do you reduce both processing time and cost?
Both halves. Infrastructure moves from always-on servers sized for a rare peak to services billed only while they run. And the code itself is treated as a cost surface, because at millions of records a day, how the processing handles memory and repetition matters more than the servers it runs on. Neither half requires buying anything, which is why the result is repeatable.
Which cloud technologies do you use?
The platform above runs on AWS, event-driven serverless services and containers, with infrastructure defined in code. The architecture is the decisions rather than the service names, and we pick the stack for the workload rather than the other way round.
Running aviation or travel data that has outgrown its platform?
Tell us what you're running, and we'll tell you honestly how we'd approach it.