All practices
Pipelines that turn events into decisions.02 / 06

Data & Analytics

Lakehouse on Iceberg or Delta, streaming that survives replay, and dbt models with tests that actually fail the build.

ic-data · warehouse / marts / revenue412 models · 34 / 34 tests · freshness 4m
SOURCESSTAGING · DBTMARTSEXPOSURESpostgres.orderscdc · debeziumkafka.events12k msg/ss3.exportsvendor · dailystg_ordersincremental9 testsstg_eventsincremental7 testsstg_exportsfull refresh4 testsfct_revenue1.2b rows · iceberg11 testsdim_customerscd type 23 testsRevenue boardlooker · 41 viewersFinance closeowner · controllerstale 31mChurn modelfeature storeBUILD TIME · LAST 12 RUNS47mcompaction backlog on run 12 · 1,204 small files mergedMODELS41294 martsTESTS34 / 340 failingROWS1,204,882fct_revenueFORMATicebergv2 · merge-on-read
Representative lineage · not a live system

Pipelines that turn events into decisions, not into a backlog.

From raw event streams to executive dashboards, we build the data substrate that lets your team move from opinion to evidence. Real-time, batch, semantic layers — wired into the tools your operators already use.

01Lakehouse architecture (Iceberg / Delta)
02Streaming (Kafka, Flink, Materialize)
03Reverse ETL & semantic layer
04Self-serve analytics enablement
05Data quality contracts