Data & Platform

    Data Pipelines and ELT

    The worst pipeline failure is the quiet one: replication stops, dashboards keep rendering yesterday's numbers, and somebody notices a week later in a board meeting.

    Start with the audit

    No sales layer, no juniors. You meet the engineer before anything begins.

    // What is included

    What you get

    • Replication from source systems with alerting on lag and failure
    • Transformations version controlled and reviewed like application code
    • Personal data handled deliberately at ingestion rather than cleaned up later
    • Backfills that can be run again safely without duplicating data

    // How it runs

    The sequence

    1. 01

      Instrument

      Freshness and volume checks so silence is not mistaken for success.

    2. 02

      Codify

      Move transformations into review and version control.

    3. 03

      Recover

      Make backfill and replay a routine operation.

    // Stack

    • Fivetran
    • Snowflake
    • ClickHouse
    • AWS
    • PostgreSQL
    • Terraform

    // Related work

    Where this has been done before

    Client names under NDA. The numbers are not.

    Analytics warehouses under control, PII isolated (NDA)

    • Single owner for schema and migrations across both warehouses
    • Managed replication with personal data isolated from the analytics path
    • Idle warehouses downsized automatically

    Warehouse compute brought under control

    All case studies

    // Questions

    Before you ask

    Talk to the engineer who would do the work

    A 20 minute call. You describe your setup, you get an honest read on whether this helps, and the top risks worth looking at first.

    See pricing