Data Warehouse Development Services
Data warehouse development for the number every team needs to agree on.
A warehouse earns its place when several systems must support the same reporting and analytical model. We design the grain, history, transformations, metric definitions, access, and operating cost before connecting dashboards to another collection of unexplained tables.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Do finance, sales, and operations calculate the same measure from different sources?
Are analysts querying production systems or rebuilding joins for every report?
Plain answer
Data warehouse development creates a governed analytical store that combines several source systems without querying production applications directly. RaftLabs selects the platform, models business entities and history, builds tested dbt transformations, defines shared metrics, and connects BI tools. A focused warehouse starts at $30,000 and usually takes eight to twelve weeks.
Revenue had four answers because it had four starting points.
Finance used invoices. Sales used closed deals. Operations used fulfilled orders. The executive dashboard joined extracts from all three and silently applied a fourth definition.
A warehouse cannot settle that argument by storing more data. It needs one modeled definition and a visible owner.
Relevant platform proof
- gas stations connected
- 40+
- Beta rollout recorded in the case study
- transactions processed in one day
- 20K+
- Real-world testing result
- full platform delivery
- 16 weeks
- Including six weeks of discovery
The gas-station platform case study documents centralized operational data and reporting. It is adjacent proof, not a published warehouse-only build, and it does not establish a platform recommendation for another company.
Build a warehouse when several sources need one analytical history.
A warehouse adds another system to own. It should remove repeated joins, production-query risk, and metric disagreement that simpler integration cannot.
Several source systems feed recurring analysis or reporting.
Historical changes and shared entity definitions matter.
The team can own platform costs, access, and model changes after handoff.
One source and one report can be served safely without a new store.
The immediate problem is a missing connector into an existing warehouse.
No owner can decide metric definitions or approve access boundaries.
Warehouse development vs ETL pipeline development
| Data warehouse | ETL or ELT pipeline | |
|---|---|---|
| Primary job | Model shared analytical history | Move and transform data between systems |
| Core decisions | Grain, keys, history, metrics, access, cost | Extraction, cadence, retries, schema drift, destination |
| Useful when | Several consumers need consistent entities | A known destination needs dependable source data |
| Often combined | Receives data through pipelines | Can feed a warehouse or an operational system |
Scope
What the first warehouse must make explicit
- 01
Platform and cost model
Choose Snowflake, BigQuery, Redshift, Databricks, or an existing platform using real workload and operating constraints. - 02
Dimensional and historical model
Define facts, dimensions, table grain, keys, and how changes remain queryable over time. - 03
Tested transformation layer
Build version-controlled dbt models with dependencies, documentation, structural tests, and deployment review. - 04
Semantic definitions and access
Give important measures one calculation and owner while enforcing role and field-level boundaries. - 05
Reconciliation and handoff
Compare migrated totals and relationships, connect BI consumers, and document the operating paths another team must own.
How it works
From source systems to governed analytical model
- Phase 101
Define the analytical jobs
List source systems, users, decisions, refresh needs, history, security boundaries, and expected query patterns.
- Phase 202
Choose the platform and model
Select the warehouse and define table grain, keys, history, raw layers, marts, metric ownership, and cost guardrails.
- Phase 303
Load and reconcile a useful slice
Connect priority sources, build tested transformations, and compare critical totals and relationships with their origins.
- Phase 404
Release governed access
Connect BI or analytical consumers, test permissions and performance, document the model, and hand over operating alerts.
Risk
Warehouse decisions that become expensive later
- The fact table has no declared grain
- State what one row represents before measures and joins multiply values silently.
- Raw data is overwritten
- Retain source fidelity and load metadata so transformations can be rerun after a rule changes.
- History is treated as a snapshot
- Decide which entity changes need effective dates before past reporting loses its original context.
- Compute cost has no owner
- Set workload limits, monitoring, and review paths before unrestricted queries become an invisible operating bill.
Scope and price
A focused data warehouse starts at $30,000.
Start with three to five sources, core entities, tested transformations, shared metrics, access controls, and one analytical consumer.
Cloud usage, managed connectors, and BI licences remain visible third-party costs. We do not hide them inside an implementation estimate.
Starting investment
Starts at $30,000
A focused release usually takes eight to twelve weeks. Historical migration, data cleanup, real-time feeds, complex access, or several domains can extend the plan.
Platform choice before procurement
Client-owned model
Work with us
Bring the reports that cannot agree.
Share the source systems, disputed measures, history, and BI consumers. We will tell you whether the missing layer is a warehouse, a pipeline, or metric governance.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
Data warehouse development creates a separate analytical store for data from operational systems. It includes platform setup, source ingestion, historical modeling, tested transformations, shared metric definitions, access controls, orchestration, and cost monitoring so analysts and dashboards can query governed data without loading production applications.
The choice depends on your cloud, workload, concurrency, data formats, governance needs, team skills, and cost model. Snowflake fits many cross-cloud analytical teams; BigQuery suits GCP-native operations; Redshift aligns with established AWS estates; Databricks fits mixed engineering and machine-learning workloads. We document the tradeoff before procurement.
A pipeline is enough when data must move into an existing, fit-for-purpose destination. A warehouse is justified when several sources need shared historical models, governed metrics, analytical performance, and controlled access. Many projects need both, but the two decisions should not be disguised as one vague data-platform scope.
A focused warehouse across three to five sources starts at $30,000 and usually takes eight to twelve weeks. Historical migration, source condition, slowly changing dimensions, access complexity, near-real-time ingestion, and many business domains can extend the plan. Platform usage charges remain separate and visible.
The gas-station case documents a platform that consolidated activity from more than 40 locations and processed over 20,000 transactions in one day during testing. It demonstrates centralized data integration and reporting, not a published Snowflake, BigQuery, Redshift, or Databricks warehouse implementation.