Foundations thatstay fast aseverything grows.
We design and build the data infrastructure your analytics and AI actually run on. Governed, observable, and still quick when the volume multiplies.
- 30+
- sources unified on a single mesh
- 1B+
- data elements queried in place
- Sub-second
- query latency at that scale
Most data programmes fail slowly. The warehouse ships, the dashboards look right, and then the copies multiply, the lineage breaks, and nobody trusts the number in the board deck. We build for the second year, not the launch: governed at the source, observable end to end, and cheap enough to keep running.
The work itself.
- 01
Pipeline engineering
ETL and ELT with automated testing, contract checks and self-healing retries, so a bad upstream day does not become a bad quarter.
- 02
Real-time streaming
Event-driven architectures on Kafka and Spark Streaming where latency is a product requirement rather than a nice-to-have.
- 03
Warehouse modernisation
Migration and optimisation on Snowflake, BigQuery and Databricks, including the cost work most migrations skip.
- 04
Quality and observability
Anomaly detection, freshness monitoring and column-level lineage, so you find out before your stakeholders do.
- 05
Governance and access
Catalogues, row and column controls, and audit trails that satisfy compliance without stalling the analysts.
- 06
DataOps
CI and CD for pipelines, infrastructure as code, and environments a team can actually develop against.
Four phases, and whatyou hold at the end of each.
- 01
Audit
Two weeks inside your stack. We map sources, trace lineage, price the current spend and find where trust breaks down.
You getFindings, a costed target architecture, a sequenced plan
- 02
Foundation
Ingestion, storage and the governed semantic layer land first, with one real business question answered end to end to prove the path.
You getWorking platform, first governed dataset in production
- 03
Migration
Domain by domain, with parity testing against the legacy system at every step, so nothing is cut over on faith.
You getVerified parity, legacy decommission plan
- 04
Handover
Runbooks, monitoring, and paired delivery with your engineers until they are the ones on call and comfortable with it.
You getOwned by your team, documented, monitored
What we build on.
We are not tied to any of these. The choice follows what you already run and what your team can maintain once we are gone.
- Snowflake
- Databricks
- Dremio
- Apache Iceberg
- dbt
- Apache Kafka
- Apache Spark
- Apache Airflow
- Fivetran / Airbyte
- AWS / Azure / GCP
Shipped, and still running.
- GranicusZero-Copy Data Mesh with DremioSemantic Layer + GenAI on AWS — querying 30+ sources and 1B+ data elements in place with zero copies.Read case study
- Appriss RetailSnowTime — Retail POS Data Platform on SnowflakeAgentic dbt Automation — ingesting encrypted retail POS files from 90+ global retailers into the governed ARDM star schema.Read case study
What people ask first.
Ready to buildwhat comes next?
Tell us about the system, product or intelligence layer you need running in production.
- LinkedInNuaav
- Phone+1 703 462 3363
- Address42346 Ponderosa Drive,
Chantilly, VA 20152