Data platform architecture
Build data platforms that scale with your business.
We design and build data platforms on AWS, with Redshift or Snowflake at the warehouse layer and dbt doing the transformation, so scattered source data becomes a governed layer your whole company can query.
Fixed-scope engagements. Written scope and price before work starts.
What is data platform architecture?
Data platform architecture is the design of how data moves from source systems into a governed, queryable layer: ingestion, storage, transformation, and access, so that engineering, analytics, and AI teams can trust the same numbers.
The challenge
Your data should be an asset, not a bottleneck.
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Data lives in five different tools, and nobody agrees on the numbers.
Business data spread across disconnected systems with no single source of truth. Every team runs its own version of revenue, and the meeting stops being about the decision and starts being about whose figure is right.
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Every new report means another one-off pipeline.
Manual data movement and spreadsheet stitching drain engineering time that should go to the product. The pipeline works, nobody documents it, and eighteen months later nobody will touch it.
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The data team is a bottleneck, not an accelerant.
Answers that should take minutes take days. Architecture sized for last year's volumes buckles under this year's, and every new source system makes the next one harder to add.
Our approach
Built in layers, delivered end to end.
We start by mapping your existing sources and maturity level: what systems hold the data, who owns it, and where today's reporting breaks down. From there we design the target architecture, choosing a warehouse and orchestration stack sized to your actual data volumes rather than to a template.
Implementation is incremental. Each stage lands something usable rather than saving all the value for a launch date, and the layers are built to evolve independently, so adding a source or changing a downstream consumer does not mean touching everything else.
The transformation layer is where most of the day-to-day work happens. For how we structure it, see dbt Model Development.
- 01 Ingestion batch, streaming, and CDC connectors
- 02 Storage warehouse or lakehouse, sized to volume
- 03 Transformation dbt and Python, layered and tested
- 04 Consumption BI, reverse ETL, operational tools
What we deliver
Service capabilities
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Warehouse architecture
Redshift and Snowflake architectures sized to your workload, with PostgreSQL where a warehouse is more machinery than the problem needs. Migration off a legacy warehouse runs in parallel with the old system until validation checks pass.
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Ingestion pipelines
Batch, streaming and change-data-capture pipelines moving data from ERP, CRM, application and third-party API sources. Every pipeline ships with monitoring and automated recovery, not a cron job someone forgot about.
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Transformation layer
dbt projects laid out as staging, intermediate and mart layers, with tests and generated documentation. Business logic lives in one place and gets reused, rather than being reimplemented per dashboard.
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Governance and lineage
Column-level lineage from dbt, automated quality checks, and access control per environment, so every dataset has an owner, a quality expectation, and a trail you can follow backwards when a number looks wrong.
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Orchestration and observability
AWS Step Functions orchestration with alerting built in from the first pipeline rather than added after the first incident. Retries, backoff and dead-letter handling are part of the design, not a patch.
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Cost control
Right-sizing warehouse compute, tuning queries that scan full tables for questions a well-modelled mart answers instantly, and storage tiering. Cost monitoring goes in on day one, because usage-based billing is easy to lose track of until the invoice arrives.
How we work
A staged delivery process
First working pipelines and dashboards typically ship in six to eight weeks. Full platform foundations run three to six months depending on how many source systems are involved and how tangled the business logic is.
- 01 Typically 1 to 2 weeks
Discovery
Assess current systems, data volumes, team capabilities and what the reporting actually needs to answer.
- 02 Typically 2 weeks
Architecture design
Stack selection, layer design and a migration strategy, written down and agreed before anything gets built.
- 03 Typically 2 to 3 weeks
Proof of concept
Validate the assumptions that would be expensive to get wrong, on one real source, before full implementation starts.
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Implementation and migration
Build the platform foundations incrementally and move existing workloads across with parallel running and validation checkpoints at each step.
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Handover and tuning
Tune performance, cut costs where the query patterns allow it, and hand over with documentation generated from the project itself.
Technology
The stack we build on
- Cloud
- AWS
- Warehouses
- Amazon Redshift
- Snowflake
- PostgreSQL
- Transformation
- dbt
- Python
- SQL
- Orchestration
- AWS Step Functions
- AWS Lambda
- AWS ECS
- Storage
- Amazon S3
- Infrastructure
- Terraform
- Docker
- GitHub Actions
The stack we work in every day. If yours differs, raise it on the first call and you will get a straight answer on fit.
Options
How the options actually compare
| Criterion | Build it in-house | Off-the-shelf platform | Work with us |
|---|---|---|---|
| Time to value | Four to six months, learning the tools on production data as you go. | Fast to switch on, but the built-in model rarely matches your business day one. | First pipelines live in six to eight weeks, against a written scope. |
| Ongoing cost | Variable, and usually hidden inside headcount rather than a line item. | Recurring licence that scales with usage, whether or not the usage is useful. | Fixed-scope engagement, priced before work starts. |
| Risk | Highest. No prior playbook, and the mistakes land on live data. | Bounded by what the platform's model supports. Anything outside it needs a workaround. | Lower. Architecture agreed first, then built incrementally with validation at each stage. |
| What you keep | Everything, including the parts nobody documented. | Access for as long as you keep paying. The modelling lives in their system. | The whole thing. dbt project, infrastructure as code, and docs generated from the project. |
Frequently asked questions
Common questions
A data warehouse is one storage layer inside a data platform. The platform also covers ingestion, transformation, governance, and the BI, reverse ETL and operational tools that consume the warehouse. It is the full path data takes from source to decision, not just where it lands.
Plan on three to six months for a full platform foundation, with the first working pipelines and dashboards delivered in six to eight weeks. The number of source systems drives the timeline more than data volume does.
We work with what you have wherever it is sound. Architecture decisions follow from your data and your team's skills, not from a preference for rip-and-replace. Replacing something is a recommendation that has to be argued for, with the migration cost stated.
The fixed price dbt pipeline audit is EUR 2,500 for five business days. Platform work is scoped and quoted individually, and every contract takes a 30 percent deposit before work starts, with the balance invoiced against agreed milestones. Get in touch with the shape of your problem and you will get a number for it.
The approach is parallel running with automated validation: the legacy system and the new platform run side by side, and cutover only happens once the checks agree. That is slower than a hard switch and it is the reason a migration does not become an incident.
Both are in production experience here, and the honest answer is that the difference matters less than the modelling on top of it. Redshift fits where the rest of the stack is already AWS and the workload is predictable. Snowflake separates storage and compute more cleanly, which helps with spiky query patterns. One caveat worth stating: the Snowflake work here is the schema layer, including hybrid tables. Senior-level warehouse operations, meaning credit management, clustering strategy and RBAC hierarchy design, is not something we claim.
Architecture review
Get a second opinion on your current architecture.
Thirty minutes, no charge, no pitch. Bring an architecture diagram or just a description of where the reporting breaks down, and you will get a straight read on what is worth fixing first and what can wait.
Book a reviewRelated Services
Building a platform, or rebuilding one that outgrew its design? Tell us where the reporting breaks down and we'll take it from there.