For data that has to arrive on schedule, correct, every time
Data Engineering
A pipeline that fails silently costs more than one that never existed, because the business keeps trusting numbers that stopped updating days ago. This practice builds ingestion, transformation and orchestration as one accountable system, whether pipelines are starting from nothing or already quietly rotting.
How a pipeline earns trust
Ingestion, transformation, orchestration and the tests that catch failure before a report does.
90 secSample frame · video pending01Before any build starts
Pipeline investment is a decision, not a default.
Building a full orchestration platform for three small feeds is a waste, and running everything from a single cron job past thirty feeds is a risk. Four questions settle which side of that line a team sits on.
How many sources actually feed decisions today?
A handful of stable sources rarely need a dedicated orchestration layer. Growth plans that will add sources quickly change that calculation.
Can late or missing data go unnoticed safely?
Some pipelines feed a dashboard nobody checks daily. Others feed billing or compliance, where a missed run has real consequences.
Is the pain scheduling, or is it correctness?
Jobs that run on time but produce wrong numbers need testing, not a scheduler. The two failures need very different fixes.
Who currently owns a failing pipeline?
If the answer is nobody, ownership is the first fix required, before any orchestration tooling gets purchased or built.
| Manual scripts | Orchestrated pipelines | |
|---|---|---|
| Failure visibility | Discovered downstream, often late | Alerted at the failing stage |
| Dependency handling | Held in one engineer's memory | Encoded and inspectable by anyone |
| Time to add a source | Grows with every existing script | Stays roughly constant |
| Data quality | Assumed until something looks wrong | Tested before delivery |
| Right for | A handful of stable, low-risk feeds | Growing or business-critical pipelines |
02Agreed before any code
The ledger that keeps the build honest
Every pipeline engagement fixes the split before work starts: what you bring, what we build, what comes back, and what never leaves your control regardless of scope.
- Your side
You bring
- The source systems that hold the raw data
- The freshness and volume the workload actually needs
- Whatever scripts or exports are stitched together today
- Our side
We build
- Ingestion connectors built and scheduled against each source
- Transformation logic turning raw extracts into modelled tables
- Monitoring and alerting wired around every pipeline run
- Your side
You receive
- A pipeline producing trusted tables on a fixed schedule
- Quality tests that catch bad data before it ships
- Documentation your engineers can extend without asking us
Never leaves you
- Which sources matter first and in what order
- Naming and ownership of the resulting data model
- Sign-off on every schedule and refresh window chosen
Where the line sitsPipeline construction is ours to run. Which sources matter and how the resulting tables are named stays a decision only your team makes.
03From raw source to trusted table
Four stages, each closing on an artefact
Pipelines fail quietly when nobody wrote down what each stage was supposed to guarantee. This system closes every stage with something a stakeholder can inspect directly.
Survey the sources
Every feed is catalogued against volume, freshness need and business criticality, so the build order reflects real impact rather than convenience.
A source map with priorities attachedBuild the pipeline core
Connectors extract from each source and transformation logic reshapes raw extracts into tables modelled around how the business actually uses them.
Ingestion and transformation that run on scheduleWire in quality and monitoring
Data quality checks and failure alerts are attached to every stage, because a pipeline that fails silently is worse than one that never ran.
Tests and alerts covering every runHand over documented ownership
Runbooks, lineage and the orchestration configuration move to your team, so adding the next source does not require calling us first.
A system your engineers can extend
04What actually drives the number
Four drivers, not a flat rate card
Pipeline work is priced against the source assessment, not a template, because these four factors change the effort by multiples.
Number and shape of source systems
A handful of well-behaved APIs cost far less to ingest than a dozen inconsistent exports with changing schemas.
Freshness the business actually needs
Daily batch pipelines are simple. Near real-time streams require infrastructure most teams have not budgeted for yet.
Depth of transformation required
Light reshaping costs little. Heavy modelling against inconsistent historical data is where most pipeline estimates go wrong.
Existing technical debt to work around
Pipelines inherited from a prior team often need untangling before new work can safely begin on top of them.
05Evidence and proof
Pipelines that kept running after we left the room.
Pipeline proof is uptime and caught failures, not a feature list. Every record here waits for written client approval before it goes into production.
Explore case studiesNine spreadsheets became one scheduled pipeline overnight.
Aster North ran product analytics off manual exports stitched together weekly. A scheduled ingestion and transformation layer replaced the exports without losing a single metric definition.
- Manual export hours
- Removed entirely
- Pipeline failures caught before delivery
- All of them
Factory sensor feeds stopped silently dropping data.
Nexa Foundry's plant sensors fed a pipeline that failed without alerting anyone for days at a time. Monitoring and retry logic turned silent failure into a page.
- Silent pipeline failures
- Down to zero
- Time to detect a broken feed
- Minutes, not days
Our pipelines used to fail and nobody found out until finance did. Now the alert reaches us before the number does.
Sample · pending client approval
06Read this first
When a pipeline build is the wrong spend
Selling orchestration to a team with three stable feeds would cost more than it earns us in trust. The exits are marked before the pitch. When the build is justified, it runs on Airflow or a managed equivalent for orchestration, dbt for transformation, and cloud object storage underneath.
- Data integration
You need to connect live systems, not build pipelines
Real-time sync between applications is a different problem with a different toolset entirely.
- Data warehouse
The storage and modelling question is still open
Deciding between a warehouse and a lake comes before pipeline work, not after it.
Your engineers should own this build directly
If your team understands pipelines and just needs capacity, extending it internally is the faster route.
| Shape | Best when | You direct | We carry |
|---|---|---|---|
| Scoped build | The source list is settled | Priorities and sequencing | Pipeline delivery |
| Delivery pod | Sources will keep arriving over time | Roadmap decisions each cycle | Engineering, testing, releases |
| Team extension | Your leads already run delivery well | Architecture and standards | Named engineers and continuity |
07Straight answers
Asked before pipeline work is scoped.
01How is data engineering different from data integration?
Data engineering builds pipelines that move and transform data for analysis and reporting, usually on a schedule. Data integration connects live systems so they stay in sync in near real time, often through APIs or event streams. Many projects need both, but they are separate pieces of work with separate designs.
02Do you work with our existing pipeline tools?
Usually yes. Airflow, Dagster, dbt and cloud-native orchestration services are all common starting points, and the assessment decides whether to extend what exists or replace it. A working tool rarely gets discarded just because it is unfamiliar to us.
03What happens when a pipeline breaks after handover?
The alerting built during delivery notifies your team directly, with a runbook describing likely causes and fixes. Ongoing support is available where wanted, but the design goal is a pipeline your engineers can debug without waiting on us.
04Can you take over pipelines someone else built?
Yes, and the first step is an assessment rather than a rebuild pitch. Many inherited pipelines need targeted reliability fixes, not a rewrite, and the assessment says honestly which situation applies before any work is scoped.
05How do you decide between batch and streaming?
By the actual freshness the business needs, not by what sounds more advanced. Daily or hourly batch pipelines are simpler, cheaper and sufficient for most reporting. Streaming is worth its added complexity only when a decision genuinely cannot wait.
06What does a data pipeline engagement cost?
Scoped builds are priced from the source assessment: how many feeds, how much transformation, and how strict the freshness requirement is. Ongoing operation runs as a monthly commitment sized to how many pipelines your team maintains.
07Will our team be able to maintain the pipelines afterwards?
That is the handover goal from the first week. Documentation, lineage and the orchestration configuration move to your engineers, and adding a new source afterwards should never require calling us back in.