From raw toreliable.
Dashboards lie when pipelines drift. We build the ingestion, modelling, and quality gates that turn raw exports into data your team can bet a quarter on.
From the run ledger of a typical automated deployment: one pipeline, one quarter.
One graph, fromsource to decision.
A pipeline is a dependency graph, not a script. Every table knows what feeds it, every run knows what changed, and a failure anywhere stops exactly the branch it should.
Ingestion. Scheduled and streaming loads land raw with schema validated at the door, and the originals stay immutable so any run can be replayed.
Hover any node to see what that layer is responsible for. The pulse runs in dependency order because the pipeline does: nothing downstream rebuilds until its inputs pass.
Bad rows stop here,not in your board deck.
Every run validates its inputs before they move. Watch one malformed row hit the gate: the test names it, the quarantine holds it, and the clean rows carry on.
A wrong dashboard looks exactly like a right one.
That is the quiet failure mode of analytics: nothing crashes, the chart still renders, and the number is wrong. So tests run where the data enters, not where it is read. Schema at the door, nulls and uniqueness on every key, freshness on every source, and a dataset version pinned for every run so any result can be reproduced months later.
Built overnight.Trusted by nine.
Freshness is a contract, not a hope. The pipeline does its work while nobody is watching, and what your team opens at nine has already passed its tests. Here is a typical night on the loop.
Exports and event streams arrive. Schema is checked at the door.
Nulls, uniqueness, ranges, freshness. A failure halts the run, not your morning.
Staging to marts, incrementally. Only what changed is recomputed.
Serving picks up the new marts. Alerting confirms freshness.
Fresh, tested, and the same number in every meeting.
A failed test halts the run and pages the pipeline, not the analyst. Yesterday’s tested numbers keep serving until tonight’s pass.
Every tool earnsits place.
One line each: what it does in your pipeline and nothing more. We pick by team familiarity and pipeline complexity, not vendor preference.
Where most teams should start: materialised views before platforms, simple and cheap until it is not.
Scheduled DAGs on cron and sensor triggers, the workhorse for traditional pipelines.
Asset-based orchestration: every table knows what it depends on and when it is stale.
Staging to marts inside the warehouse, with tests and docs living in the same repo as the models.
Heavy transforms at cluster scale, for when a single warehouse node stops being enough.
Real-time feature serving once several models need the same features. Not before.
Data drift and model health scored continuously against the training distribution.
Dashboards and alerting shipped before cutover, not after the first incident.
Sometimes the right answer is less. One model and one team do not need a feature store; premature abstraction kills velocity. We will tell you when the boring option is the right one.
Pipelines fail.We plan for it.
Sources drift, APIs break, vendors change formats without telling anyone. The design question is not whether that happens. It is what your team sees when it does.
What happens when an upstream API changes its schema overnight?
The contract rejects the breaking change at the door. That source halts and alerts; everything downstream keeps serving the last tested build instead of quietly going wrong. You wake up to one failed source, not a quarter of corrupted history.
And when bad data has already reached the warehouse?
Backfills, done properly: partitioned by day, idempotent, row counts verified against the source. We replay the affected window and the marts rebuild from corrected inputs. Nobody hand-edits a production table.
What if the data drifts slowly instead of breaking loudly?
Drift is scored on every cycle against the training distribution. Crossing the threshold opens an incident and schedules a retrain, so the correction starts before anyone has to notice a stale model in a meeting.
The honest promise is not that nothing breaks. It is that breakage is loud, contained, and cheap to repair.
Pipelines inproduction.
Collecting, transforming, and serving real data today.
Common questions about data pipelines and MLOps.
What teams ask before they retire manual ETL.
Airflow for traditional scheduled DAGs, Dagster for modern asset-based pipelines, Prefect for Python-native workflows, Temporal for long-running stateful workflows. We pick based on team familiarity and pipeline complexity, not vendor preference.
For most teams: start with Postgres + materialized views (simple, cheap, sufficient). Scale to Feast or Tecton when you need real-time feature serving across multiple models. Skip feature stores entirely if you have only one model and one team. Premature abstraction kills velocity.
We deploy LangSmith + Arize + OpenTelemetry for LLM observability (prompts, latency, token usage, hallucination rates). For traditional ML: Evidently AI or Arize for data drift, WhyLabs for ML health. Every BearPlex pipeline ships with dashboards and alerting before cutover.
Yes. Common migrations: Informatica/IBM DataStage → Airflow/Dagster, SSIS → dbt + Airflow, home-grown cron scripts → proper orchestration. We do parallel-run cutover (old and new systems run side-by-side until trust is established) to de-risk migrations in regulated environments.
GitHub Actions or GitLab CI for the pipeline itself. MLflow or Weights & Biases for experiment tracking. Model registries (MLflow, SageMaker Model Registry) for versioning. Deployment via BentoML, Seldon Core, or native SageMaker/Vertex AI endpoints. Everything Git-versioned, everything reproducible.
Trust yournumbers.
If your team double-checks the dashboard before quoting it, the pipeline has already failed. Bring us the numbers you hedge on. We will build the system that makes them safe to bet on.