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Data Pipelines

Petabytes, moved quietly.

From the first Kafka topic to the last dashboard: pipelines that move terabytes a second, stay correct under backfill, and cost less every quarter.

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Stack

KafkaAirflowBigQuerySnowflakeIcebergdbtSparkDuckDBSupersetPostgres

Deliverables

What the engagement produces.

A scope reads like this list, with dates and a number beside it.

  • 01Streaming and batch ingest (Kafka, Pub/Sub, Airflow, Dataflow)
  • 02Warehouse and lakehouse design (BigQuery, Snowflake, Iceberg, DuckDB)
  • 03Schema governance, lineage, and data contracts
  • 04Cost engineering: partitioning, tiering, and retention that pays for itself
  • 05Observability, freshness SLAs, and backfill tooling

Case studies

Where it has run.

  • Public safety

    First Responder File

    A records platform first responders actually use in the field.

    Full-stack web application for first responders: case files, media, and reporting with strict access control, built and operated end to end.

    web app, API, infra
    End-to-end
    web app, API, infra
    access and audit trails
    Role-based
    access and audit trails
    GCP platform with CI/CD
    Multi-env
    GCP platform with CI/CD

    TypeScript · React · Python · Postgres · GCP · Kubernetes

  • 74 PB
    data under management

    Warehouses, lakes, and streaming stores we designed and still operate.

  • 1 TB/s+
    peak pipeline throughput

    Peak ingest across our streaming and batch systems.

  • 2.5 B
    messages processed in 90 days

    Agentic moderation for ModWell, one game network at a time.

Get started

Tell us what your data is doing.

Thirty minutes. You describe the problem, and we tell you honestly whether we are the right people for it.

Book a 30-minute call

No discovery deck, no sales engineer.