We replace fragile spaghetti ETL with lineage-tracked, tested, observable data workflows. 1B+ events/day. Sub-second query latency. Pipelines your BI team actually trusts.
Data pipelines accrete complexity silently. By the time you notice the problem, it's a multi-year untangling project — and every business decision is made on data nobody fully trusts.
Bash scripts scheduled in cron. SQL files with no version control. When something breaks, no one knows where data came from or what it affects downstream.
Pipelines fail overnight with no alerting. Dashboards show stale data from yesterday. The BI team reports it Monday morning. Business meetings ran on bad numbers all week.
BI analysts spend nearly half their time not building dashboards — but validating whether the numbers are right at all. This is an infrastructure failure disguised as a staffing problem.
When a metric looks wrong in a board deck, the data team is called into question — even when the root cause is a broken upstream ETL job three hops away.
Not a slide deck. Not a reference architecture. A working platform delivered and documented so your team can own it independently.
Real-time event ingestion into a transactional lakehouse. Kafka → Glue streaming → S3 Iceberg tables → Redshift with sub-second freshness for BI and ML alike.
dbt models with full test coverage, data contracts enforced at ingestion, OpenLineage metadata for every transformation, and Airflow orchestrating the whole thing.
Query performance tuning, partition and clustering strategies, materialized view design, and cost rightsizing. We've cut Redshift/Snowflake bills by 40–60% on day one.
Data catalog with automated lineage, column-level access controls, PII discovery, and data mesh architecture for domain ownership at scale.
Every architecture is tailored to your data volumes, latency requirements, and team skills — but here's a representative streaming lakehouse pattern:
Battle-tested tools across streaming, batch, storage, query, and observability layers. We choose for your team's long-term maintainability, not for novelty.
From audit to production platform to fully governed data mesh — structured to deliver business value at every phase, not just at the end.
Map all data sources, ETL jobs, and downstream consumers. Score pipeline health, SLA coverage, and lineage gaps. Deliver prioritised remediation plan.
Migrate highest-value pipelines to dbt + Airflow or streaming lakehouse. Add data contracts, testing, and observability. BI team velocity increases within weeks.
Cost rightsizing, query performance tuning, data catalog buildout, PII controls, and domain ownership handoff for long-term independent operation.
A major retailer was running nightly batch jobs to feed their merchandising and inventory dashboards. By the time buyers saw demand signals, the window to act had already closed. Decisions lagged reality by 12+ hours.
We migrated their entire pipeline to a Kafka → Glue → S3 Iceberg → Redshift streaming lakehouse processing over 1 billion events per day. Latency dropped from 12 hours to 47 seconds. Infrastructure costs dropped by $6,300/month through Redshift rightsizing and Iceberg compaction.
Read more case studiesFixed-scope or range pricing on every engagement. No hourly billing. You always know what you're getting before work begins.
Start with a Pipeline Audit — 5 business days, fixed price, clear roadmap. No obligation to continue.
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