Snowflake, BigQuery, or Redshift -- which should we use?

Snowflake is the most flexible choice -- it separates storage from compute, scales independently, and works well across cloud providers. BigQuery is the right choice if your organisation is already in Google Cloud and you want to avoid data movement costs. Redshift performs well for large-volume SQL workloads if you're already on AWS and your access patterns are predictable. Databricks makes sense when your warehouse workloads and ML workloads share the same infrastructure. We assess your existing cloud infrastructure, data volume, team SQL proficiency, and cost constraints before recommending a platform.

What is dbt and do we need it?

dbt (data build tool) is a transformation framework that lets you write data transformations as SQL SELECT statements, version-control them in git, test them automatically, and generate documentation from the model definitions. For most data warehouse projects it is the right tool for the transformation layer because it makes transformations reproducible, testable, and auditable. The alternative -- ad-hoc transformation scripts or stored procedures -- creates a transformation layer that is hard to test, hard to change, and impossible to document systematically. We use dbt as the default transformation tool and will tell you if your project is simple enough not to need it.

How long does data warehouse development take?

A warehouse covering 3 to 5 source systems with core entity models and BI tool integration typically takes 8 to 12 weeks. A more complete build with semantic layer, historical data migration, and a full dbt transformation suite typically takes 12 to 20 weeks. The variables are source system count and complexity, data quality issues in those systems, number of business entities to model, and whether historical migration is in scope.

How do we keep the warehouse from becoming outdated as our business changes?

A warehouse designed for change uses version-controlled dbt models as the transformation layer. When a business definition changes -- for example, how 'active customer' is defined -- you update one model, run the tests, and the change propagates consistently to every downstream report. The semantic layer ensures that metric changes are applied once rather than across every dashboard. We build warehouses with a clear separation between raw data, cleaned staging models, and business-logic marts so that changes in one layer don't cascade unpredictably into others.

Snowflake, BigQuery, or Redshift -- which should we use?

Snowflake is the most flexible choice -- it separates storage from compute, scales independently, and works well across cloud providers. BigQuery is the right choice if your organisation is already in Google Cloud and you want to avoid data movement costs. Redshift performs well for large-volume SQL workloads if you're already on AWS and your access patterns are predictable. Databricks makes sense when your warehouse workloads and ML workloads share the same infrastructure. We assess your existing cloud infrastructure, data volume, team SQL proficiency, and cost constraints before recommending a platform.

What is dbt and do we need it?

dbt (data build tool) is a transformation framework that lets you write data transformations as SQL SELECT statements, version-control them in git, test them automatically, and generate documentation from the model definitions. For most data warehouse projects it is the right tool for the transformation layer because it makes transformations reproducible, testable, and auditable. The alternative -- ad-hoc transformation scripts or stored procedures -- creates a transformation layer that is hard to test, hard to change, and impossible to document systematically. We use dbt as the default transformation tool and will tell you if your project is simple enough not to need it.

How long does data warehouse development take?

A warehouse covering 3 to 5 source systems with core entity models and BI tool integration typically takes 8 to 12 weeks. A more complete build with semantic layer, historical data migration, and a full dbt transformation suite typically takes 12 to 20 weeks. The variables are source system count and complexity, data quality issues in those systems, number of business entities to model, and whether historical migration is in scope.

How do we keep the warehouse from becoming outdated as our business changes?

A warehouse designed for change uses version-controlled dbt models as the transformation layer. When a business definition changes -- for example, how 'active customer' is defined -- you update one model, run the tests, and the change propagates consistently to every downstream report. The semantic layer ensures that metric changes are applied once rather than across every dashboard. We build warehouses with a clear separation between raw data, cleaned staging models, and business-logic marts so that changes in one layer don't cascade unpredictably into others.

When your leadership team asks for the same metric, does every department produce a different number because each team pulls from a different source?
How much of your data team's time goes into explaining why two reports don't agree rather than answering new questions?

Four people produce four different revenue numbers because four systems each have a partial, inconsistent view of the same data.

A data warehouse is the single agreed source of truth that every report, dashboard, and ML model draws from. It brings together data from ERP, CRM, product databases, and SaaS tools, applies consistent business logic, and makes the combined dataset queryable by analysts and BI tools without touching production systems.

We design and build data warehouses on Snowflake, BigQuery, Redshift, or Databricks. Schema design, data modelling with dbt, pipeline integration, and the semantic layer that makes data accessible to people who are not data engineers. Scoped and priced as one engagement.

Warehouse on Snowflake, BigQuery, or Redshift -- chosen for your workload and cost profile
dbt-based transformation layer with version-controlled, tested SQL models
Semantic layer that defines business metrics consistently across every report
Historical data migration from existing data stores, spreadsheets, and legacy databases

RaftLabs designs and builds data warehouses on Snowflake, BigQuery, Redshift, and Databricks -- schema design, dbt transformation models, semantic layer, and pipeline integration. Batch and real-time data ingestion from ERP, CRM, product databases, and SaaS tools. Most warehouse projects deliver in 8 to 14 weeks at a fixed cost.

Every report, dashboard, and business decision eventually depends on a consistent, queryable data layer. Without a warehouse, analysts query production databases directly (risking performance issues and inconsistent results), pull manual exports from each system, or build one-off scripts that become unmaintainable. The result is a different revenue number from every team, a data definition that lives in someone's head rather than the codebase, and reporting that can't scale as the business grows.

A data warehouse solves this by creating a single layer where all source systems land, business logic is applied consistently, and analysts and BI tools can query without understanding the operational structure of each upstream system. Building that layer is a design and engineering project. We handle the architecture decisions, the physical build, the transformation models, and the handoff to the team that maintains it.

What we build

Warehouse platform selection and setup

Platform assessment based on your existing cloud infrastructure, data volume, team SQL proficiency, and cost constraints. Snowflake for separation of storage and compute and cross-cloud flexibility. BigQuery for Google Cloud organisations that want to avoid data egress costs. Redshift for AWS shops with large, predictable query volumes. Databricks for organisations where ML workloads and analytical workloads share the same infrastructure. Initial setup, access control, role-based permissions, and cost guardrails configured before any data lands.

Data modelling and schema design

Dimensional modelling using star schema or snowflake schema patterns depending on query patterns and analyst tooling. Entity definitions for customers, orders, products, revenue, and usage -- named and structured to match how the business actually talks about them. Staging and mart layer separation so raw source data is preserved while business logic lives in a separate, testable layer. Schema documentation written in plain language so analysts and business users can understand what each table and field represents without reading SQL.

dbt transformation layer

dbt models for every business-defined transformation -- modular SQL checked into version control so every change is tracked and reversible. Incremental materialisation strategies that process only new or changed records rather than recomputing entire tables, reducing warehouse compute cost on large datasets. Built-in dbt tests for null values, uniqueness, referential integrity, and custom business logic assertions run automatically on every pipeline execution. Generated documentation from model definitions gives analysts a queryable reference for every table and column.

Semantic layer and metric definitions

Centralised metric definitions for revenue, churn rate, customer acquisition cost, lifetime value, and any other business metric that appears in more than one report. Every BI tool, ad-hoc query, and API consumer draws from the same definitions so the same question always produces the same answer regardless of who asks it or which tool they use. Business logic versioned alongside the data model so metric changes are tracked and applied consistently. Reduces the volume of "why do these numbers differ" conversations to near zero.

Historical data migration

Migration of historical records from legacy databases, spreadsheets, CSV archives, and deprecated data stores into the new warehouse. Data cleaning and standardisation applied during migration so historical records conform to the same schema and business logic as current records. Reconciliation reports confirming that migrated totals -- revenue, order counts, customer counts -- match the source systems within an agreed tolerance. Historical data is a business asset; migration preserves it in a queryable form rather than archiving it somewhere inaccessible.

BI tool integration and handoff

Connection setup for Looker, Tableau, Metabase, Power BI, or the BI tool your team already uses. Semantic layer exposure to the BI tool so analysts build dashboards from business-defined metrics rather than raw warehouse tables. Query pattern guidance for analysts so reports run efficiently at warehouse scale without triggering expensive full-table scans. Documentation of the data model, mart layer, and metric definitions delivered to the team that maintains the warehouse after the engagement ends.

Have a data warehouse project?

Tell us your source systems, reporting use cases, and where analysts waste time today because data isn't consistent. We'll scope the warehouse and give you a fixed cost.

Talk about your data warehouse project

Data Engineering Services -- full data engineering capability overview
ETL Pipeline Development -- pipelines that move data from source systems into your warehouse
Real-Time Data Pipelines -- streaming ingestion for data that needs to be current
Data Quality Management -- validation and monitoring for data entering the warehouse

Predictive Analytics -- ML models built on your warehouse data
Business Intelligence -- dashboards and reporting on top of your warehouse
Cloud Migration -- move existing databases and infrastructure to cloud

Frequently asked questions

: Snowflake is the most flexible choice -- it separates storage from compute, scales independently, and works well across cloud providers. BigQuery is the right choice if your organisation is already in Google Cloud and you want to avoid data movement costs. Redshift performs well for large-volume SQL workloads if you're already on AWS and your access patterns are predictable. Databricks makes sense when your warehouse workloads and ML workloads share the same infrastructure. We assess your existing cloud infrastructure, data volume, team SQL proficiency, and cost constraints before recommending a platform.
: dbt (data build tool) is a transformation framework that lets you write data transformations as SQL SELECT statements, version-control them in git, test them automatically, and generate documentation from the model definitions. For most data warehouse projects it is the right tool for the transformation layer because it makes transformations reproducible, testable, and auditable. The alternative -- ad-hoc transformation scripts or stored procedures -- creates a transformation layer that is hard to test, hard to change, and impossible to document systematically. We use dbt as the default transformation tool and will tell you if your project is simple enough not to need it.
: A warehouse covering 3 to 5 source systems with core entity models and BI tool integration typically takes 8 to 12 weeks. A more complete build with semantic layer, historical data migration, and a full dbt transformation suite typically takes 12 to 20 weeks. The variables are source system count and complexity, data quality issues in those systems, number of business entities to model, and whether historical migration is in scope.
: A warehouse designed for change uses version-controlled dbt models as the transformation layer. When a business definition changes -- for example, how 'active customer' is defined -- you update one model, run the tests, and the change propagates consistently to every downstream report. The semantic layer ensures that metric changes are applied once rather than across every dashboard. We build warehouses with a clear separation between raw data, cleaned staging models, and business-logic marts so that changes in one layer don't cascade unpredictably into others.