Pipelines that run on schedule — and recover on their own.
Reliable batch and streaming ETL with real scheduling, dependency-aware retries, SLA-based alerting, and backfill tooling — built on Airflow, Dagster, or Prefect, or something simpler when those are overkill. So when a job fails at 2am, it retries, recovers, or tells you exactly what broke before morning.
Right now, cron is your orchestrator and silence is your monitoring.
That works right up until a job fails at 2am and nothing notices — the load step ran on data the extract never finished, the report comes up wrong, and you find out from the person reading it. The fix isn't another cron line; it's an orchestrator that models your pipeline as a graph, runs the steps in order, retries the ones that flake, and raises an alert the moment an SLA slips. We build pipelines that recover on their own and tell you when they can't — so a 2am failure is a notification you read, not a fire you discover.
Your pipeline is held together by cron and hope.
Each symptom has the same root — and the same fix underneath.
- 01
Cron is running your whole data pipeline
An orchestrator that models dependencies, order, and retries — not forty unaware jobs.
- 02
You don't know which pipeline failed last night
One place that names the failed pipeline, task, and error every morning.
- 03
A failure halfway through corrupts everything after it
Dependency-aware runs that hold downstream steps instead of feeding them half-finished data.
- 04
Backfills take a week of manual babysitting
Parameterised, idempotent backfills that replay a date range on demand.
- 05
You hear about breaks from stakeholders
SLA-based alerting that reaches the right channel before a report goes stale.
The machinery behind a pipeline that runs itself.
From schedule to recovery — the unglamorous work that makes data pipelines reliable.
Scheduling & orchestration
Airflow, Dagster, or Prefect — pipelines as dependency graphs, not cron lines.
Dependency-aware retries
Failed steps retry on backoff; downstream waits until the data is actually ready.
SLA-based alerting
Misses a freshness window, slows, or returns too little — and the right channel hears it.
Backfill tooling
Replay a date range or a single source, idempotently, without babysitting the run.
Batch & streaming
Batch by default; near-real-time only where a use case genuinely needs it.
Cost monitoring
What each pipeline and query costs, flagged before the bill surprises anyone.
Every run, ordered and accounted for.
- Extract01
- Transform02
- Load03
- Schedule04
- Retry05
- Observe06
A step only runs when the one before it finishes; a failure retries, then holds everything downstream rather than feeding it bad data; and every run is observed, so you see what ran, what didn't, and why. That's the difference between hoping the right jobs ran and knowing it.
Order, not forty unaware jobs.
Each pipeline becomes a dependency graph an orchestrator runs in the right order — extract before transform, transform before load, with nothing firing on data that isn't ready. You set the schedule once and stop wiring cron lines that have no idea the others exist.
- Pipelines modelled as dependency graphs
- Airflow, Dagster, or Prefect — or simpler tools
- Steps run only when upstream finishes
- One schedule, one source of truth for runs
It retries before it bothers you.
When a flaky API or a briefly busy warehouse trips a step, it retries on a backoff schedule and carries on — and only the failed branch reruns, not the whole job. When a step truly can't recover, everything downstream is held instead of fed half-finished data, and you get one clear alert, not a cascade.
- Backoff retries on transient failures
- Only the failed branch reruns
- Downstream held, never fed bad data
- One clear alert instead of a cascade
Replay a date range without losing a week.
Backfills stop being a week of hand-edited scripts. We build idempotent, parameterised backfill tooling that replays a specific date range or a single source on demand, touches only the affected partitions, runs them in parallel where it's safe, and restarts cleanly if it stops partway.
- Idempotent, parameterised reruns
- Partition-aware — only affected days
- Parallel where it's safe to be
- Restartable if it stops partway
It tells you before the report goes stale.
Freshness, runtime, and cost are watched against the SLAs you agree. A pipeline that misses its window, slows down, or returns suspiciously little raises an alert to the right channel — and the expensive queries get flagged and tuned before the bill surprises anyone.
- SLA-based freshness & runtime alerts
- Cost tracked per pipeline and query
- Incremental, partition-aware processing
- Problems found by us, not stakeholders
Built to run itself — and be maintained.
Map the pipelines
We catalogue what runs today, what depends on what, and which jobs fail silently or get babysat most.
Choose the orchestrator
We pick the lightest tool that covers your dependencies, retries, and observability — Airflow, Dagster, Prefect, or simpler.
Build & migrate
Pipelines move off cron onto dependency graphs with retries and backfill tooling, one painful job at a time.
Add SLAs & monitoring
Freshness, runtime, and cost SLAs with alerting to the right channel, plus the checks that catch silent failure.
Document & hand over
Docs, a walkthrough, and a system your team can rerun, backfill, and extend on their own.
The cron jobs have become the system.
Cron is your orchestrator
Flexible until one job has to wait for another — then a missed step quietly poisons everything downstream.
Failures are silent
A pipeline that breaks quietly is worse than one that breaks loudly; you find out from the wrong person, too late.
Backfills eat a week
When a rerun means hand-editing scripts and babysitting, fixing yesterday's data becomes a project nobody wants.
Proven pipeline tools, used with discipline.
The things data teams ask first.
Let cron stop being your orchestrator.
Tell us which pipelines you babysit and which ones fail in the night. We'll move them onto a real orchestrator — with dependency-aware retries, SLA alerting, and backfills that take minutes — so a 2am failure becomes a notification you read, not a fire you discover.