Synapse, Snowflake, and BigQuery implementations - with star schema design, ETL/ELT pipelines, and reporting layers built for analysts, not just engineers.
Modern cloud data warehouses - Synapse, Snowflake, BigQuery - eliminate the infrastructure burden of traditional on-premises systems. But the difference between a fast, trustworthy warehouse and an expensive mess of inconsistent reports comes down to how the data is modelled, how the pipelines are built, and how the reporting layer is structured.
At DynamicUnit, we design warehouses using proven dimensional modelling techniques - star schemas for reporting performance, proper conformed dimensions for consistency across subject areas, and ELT pipelines built in dbt for testable, documented transformations. The result is a warehouse your analysts can trust and your Power BI dashboards can query without hitting walls.
Whether you need a greenfield warehouse or are migrating an existing on-premises system to the cloud, our team handles architecture design, pipeline development, and BI layer delivery end to end. For organisations that also need to centralise raw and unstructured data, our enterprise data lake service complements the warehouse with a schema-on-read layer for exploration and ML workloads.
Source data quality is critical to warehouse success. We frequently pair warehouse builds with data cleansing and data migration services to ensure what lands in the warehouse is accurate and complete from day one.
Unified sales, inventory, and customer data across channels - with near-real-time feeds from e-commerce platforms and POS systems for demand forecasting and margin analysis.
Production, quality, and supply chain data modelled for OEE reporting, yield analysis, and procurement cost tracking - with ERP data at the core.
Transaction, risk, and regulatory data consolidated for compliance reporting, fraud detection, and portfolio analysis - with full audit trails and column-level security.
Patient, clinical, and operational data centralised with HIPAA-grade access controls - enabling outcomes research, capacity planning, and population health analytics.
From platform selection and schema design to pipelines, testing, and BI layer delivery - here's what we build.
Evaluate and configure the right platform - Azure Synapse Analytics, Snowflake, or BigQuery - based on your cloud footprint, query patterns, and cost profile.
Design fact and dimension tables using star schema principles - ensuring analyst queries are fast, conformed dimensions are consistent, and the model is extensible.
Build batch and incremental pipelines using Azure Data Factory, Fivetran, dbt, or custom frameworks - with SCD handling, error logging, and reconciliation.
Write SQL transformations as version-controlled dbt models with built-in tests, documentation, and lineage - making your transformation logic auditable and trustworthy.
Migrate historical data from legacy systems, on-premises databases, or ERP exports into the warehouse with backfill logic and data quality validation.
Build semantic models in Power BI (Tabular), Looker (LookML), or Tableau - defining business metrics consistently so every dashboard agrees on the same numbers.
Implement role-based access, column-level security, data classification tags, and audit logging to meet compliance requirements and control data exposure.
Tune query performance with distribution keys, clustering, materialised views, and result caching - reducing BI query times from minutes to seconds.
The most common data warehouse problem isn't technical - it's that different reports show different numbers for the same metric. Here's how we prevent that from happening.
We design the dimensional model before writing a single pipeline - ensuring the schema supports your reporting requirements rather than just replicating source data.
Every transformation is tested for nulls, uniqueness, referential integrity, and custom business rules - so you know when data quality breaks before your analysts do.
dbt's auto-generated documentation means every model, column, and metric has a definition your team can reference - ending the "what does this field mean?" conversations.
We define business metrics in the semantic layer once - ensuring Finance, Sales, and Operations dashboards all calculate revenue, headcount, and margin the same way.
We design for query efficiency - right distribution keys in Synapse, clustering in Snowflake and BigQuery, and materialised aggregations - keeping compute costs justified.
We hand over a warehouse your team can actually use - with trained analysts, documented data dictionaries, and a support plan for the first months of production use.
We audit your source systems, reporting requirements, and query patterns. You get a warehouse architecture document covering platform choice, dimensional model, and pipeline design.
We design the star schema, build the dbt transformation layer, and develop ETL/ELT pipelines - with data quality checks at every stage.
We migrate historical data, validate record counts and balances, and connect the warehouse to Power BI or your preferred BI tool with a semantic model.
Pipelines go live with monitoring and alerting. We hand over documentation, train your analysts, and transition to managed support for ongoing pipeline and query optimisation.
Tell us your source systems, current reporting pain points, and your platform preferences - we'll design the right warehouse architecture for your use case.
