Service
Data Engineering and Dashboards
Pipelines that bring your systems into one queryable model, and dashboards that answer the three questions your week actually turns on.
The problem
What usually goes wrong
Reporting usually arrives as a request for a dashboard and turns out to be a data problem. The number exists in four systems with four definitions, and the meeting is really an argument about which one is right.
The second failure is volume. A dashboard with forty tiles is read by nobody, because nothing on it is the thing you act on.
Useful reporting starts with agreed definitions and ends with a small number of views tied to decisions.
Scope
What we deliver
Metric definitions
Written, agreed definitions for each measure, including the filters and the time basis. One definition per metric, in one place.
Ingestion pipelines
Scheduled or event driven loads from your operational systems, with incremental extraction and checkpointing.
Modelled warehouse tables
Cleaned, conformed tables built for querying rather than for transactions, with slowly changing history where it matters.
Data quality checks
Freshness, row count, null rate and referential checks that run with every load and raise a visible failure.
Dashboards
A small number of views built around decisions, each with a stated owner and an action that follows from it.
Self service layer
Documented tables and saved queries so your team can answer new questions without a project.
Method
Technical approach
We agree definitions before building anything. What counts as an active customer, when revenue is recognised, whether a cancelled job is still a job. Most reporting disputes are definition disputes, and writing them down resolves them permanently.
Extraction is incremental. Each source has a high water mark, and loads read only what changed since the last successful run. Checkpoints are committed with the data so a failure resumes cleanly.
The model separates raw, cleaned and presentation layers. Raw is kept immutable so a transformation bug can be replayed rather than investigated. Presentation tables are shaped for the questions, so a dashboard query is a simple scan rather than a six way join.
Every load runs quality checks. Freshness against an expected interval, row counts against a tolerance, null rates on required fields, and referential integrity between the tables that join. A failed check blocks publication rather than quietly publishing a wrong number.
Dashboards are built to a decision. Each view names the question it answers and the person who acts on it, and anything that fails that test is not built.
Handover
Deliverables
Everything below is yours at delivery, including the intellectual property. Sawhorse keeps nothing.
- 01Metric dictionary with agreed definitions
- 02Incremental ingestion pipelines with checkpointing
- 03Layered warehouse model with documented tables
- 04Data quality checks wired into every load
- 05Dashboards tied to named decisions and owners
- 06Documented self service tables and saved queries
- 07Alerting for stale or failed loads
- 08Source code, schema documentation and deployment guide
Related
Systems this service builds
- BI Dashboards and ReportingAgreed metric definitions, modelled tables and a small number of views tied to decisions, so a meeting argues about the action rather than the number.
- Inventory and Order ManagementOne stock position and one order lifecycle across every channel, built on a movement ledger rather than a quantity field people adjust.
- Custom CRMCustom CRM software for account relationships, sales pipelines, activity history, permissions, reporting and integrations with your operational systems.
FAQ
Questions people actually ask
Do we need a separate warehouse?
Not always. At moderate volume a read replica with modelled views is enough and far cheaper to run. We size it against your actual row counts and query patterns rather than by default.
How fresh can the numbers be?
From near immediate for event driven sources to a defined schedule for systems that only support export. Each dashboard states its freshness so nobody reads a stale figure as current.
Can we keep using the reporting tool we have?
Yes. The modelled tables are the deliverable, and any tool that speaks SQL can sit on top. That also means you are not locked to one vendor.
What happens when a source system changes a field?
The quality checks catch it, usually as a null rate or referential failure, and the load stops rather than publishing a wrong number. The fix is then a mapping change in one place.
How do you handle historical corrections?
Raw data is immutable and transformations are replayable, so a correction is reprocessed rather than patched. Where the business needs point in time history we model it explicitly.
Who owns the pipelines and the dashboards?
You do, including the code, the definitions and the documentation.
Let’s talk about the system you need.
Tell us where the current process breaks down. We can map the workflow, examine the integration requirements and define a practical scope for the build.
- A mutual NDA is signed before project details are shared.
- Scope is fixed in writing before any build starts.
- You own the source code, the IP and the documentation at delivery.
- Support plans are available and tailored on a discovery or technical call.