Data Warehousing
Your Analytical Foundation
Modern cloud data warehouse architecture that consolidates your data estate into a single, governed, query-ready platform — eliminating the siloed exports and one-off extracts that slow your analytics teams and distort your numbers.
One Governed Repository — For Every Analytical Workload You Run
Most data quality problems aren't data quality problems. They're data architecture problems. When different teams pull from different sources with different transformation logic, the same metric produces different numbers in different dashboards — and the debate about which number is correct consumes the time that should be spent acting on it.
SWA's Data Warehousing practice designs and builds modern cloud data warehouses that give every team in your organisation access to the same governed, reliable data — with the query performance, scalability, and cost controls that enterprise-scale analytical workloads require. From greenfield cloud DWH builds on Snowflake or BigQuery to legacy on-premises migrations, we handle the full data engineering lifecycle: ingestion, transformation, modelling, governance, and serving.
- Cloud DWH architecture on Snowflake, BigQuery, Redshift, and Databricks
- ELT/ETL pipeline development with dbt, Airflow, and Fivetran
- Data modelling — dimensional, data vault, and lakehouse architectures
- Data governance, cataloguing, and lineage tracking
- Legacy data warehouse migration and modernisation
What We Deliver
DWH Architecture Design
Platform selection, storage layer design, compute configuration, and scalability planning — delivered as a documented architecture blueprint before the first pipeline is built, so implementation decisions are defensible and reversible.
Data Ingestion Pipelines
Batch, micro-batch, and streaming ingestion from databases, SaaS APIs, files, and event streams — with idempotent pipeline design, schema drift handling, and full observability for every source-to-warehouse data flow.
Transformation & Modelling
dbt-native transformation layer development — dimensional models, data vault, and OBT architectures — with automated testing, documentation generation, and lineage visualisation that makes your data model self-explaining.
Data Governance & Catalogue
Data ownership assignment, classification, access policy enforcement, and business glossary — with automated lineage tracking that lets your data team answer "where does this number come from?" in seconds, not days.
Cost Optimisation
Query performance tuning, clustering key design, materialisation strategy, and warehouse scheduling — calibrated to your actual query patterns so compute spend scales with analytical value, not with raw data volume.
Legacy Migration
Structured migration of on-premises Oracle, Teradata, SQL Server, and Netezza environments to cloud — with parallel running periods, data validation frameworks, and user acceptance testing before the legacy system is decommissioned.
Why Choose SWA for Data Warehousing
Architecture Before Implementation
We invest in an architecture design phase before writing the first pipeline — because a poorly designed DWH scales exponentially in the wrong direction. The architecture deliverable is yours to own, regardless of who implements it.
Data Quality as a First Principle
Every pipeline we build has automated data quality tests integrated into the transformation layer — not bolted on after go-live when production data issues are already affecting downstream reporting and decision-making.
Governance Without Bureaucracy
We implement data governance frameworks that make data access faster for authorised users — not frameworks that create a catalogue nobody updates and an approval process nobody follows. Governance should enable access, not block it.
Ready to Build an Analytical Foundation That Scales?
Tell us what data sources you're working with and what analytical workloads you need to support — we'll design an architecture that serves them reliably at the scale you expect in three years.