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Data Warehouse Development Malta

Data warehouse development services in Malta. Design, build, and optimise modern analytical data warehouses on Snowflake, BigQuery, Redshift.

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/ the solution /

Data Warehouse Development built around your business.

Every solution we deliver is built on three pillars: your data, your context, and continuous improvement. Each capability is traceable and measurable.

  • Dimensional Modelling & Schema Design

    Design optimised data warehouse schemas using star, snowflake, and Data Vault patterns tai…

  • Cloud Data Warehouse Implementation

    Deploy and configure modern cloud data warehouses on Snowflake, BigQuery, Redshift, or Azu…

  • dbt Transformation Layer

    Build modular, tested, and documented transformation pipelines using dbt that convert raw …

  • Historical Data & Slowly Changing Dimensions

    Implement slowly changing dimension strategies that preserve historical data accurately fo…

/ how it works /

Live in weeks, not months.

We document analytical requirements, reporting needs, and compliance obligations. We profile source systems to understand data volumes, quality, relationships, and change patterns that inform warehouse design decisions.

We design the warehouse schema with dimensional models optimised for your query patterns. Fact tables, dimension tables, and aggregation layers are designed to serve dashboards, ad-hoc queries, and downstream ML workloads efficiently.

We select and configure the optimal cloud warehouse platform based on your workload characteristics, existing cloud presence, budget, and team skills. Infrastructure-as-code ensures reproducible and auditable platform configuration.

We build dbt models that transform raw data into analytical tables with comprehensive testing, documentation, and quality checks. Modular design enables incremental development and easy modification as business requirements evolve.

We connect your data warehouse to BI tools including Power BI, Tableau, and Looker Studio with optimised semantic layers and curated datasets that enable self-service analytics for business users.

We optimise query performance through clustering, partitioning, materialised views, and caching strategies. Cost optimisation ensures you use warehouse compute efficiently without over-provisioning resources.

/ what you get /

Everything you need. Nothing you don't.

Dimensional Modelling &
Schema Design
Cloud Data
Warehouse Implementation
dbt Transformation
Layer
Historical Data &
Slowly Changing Dimensions
/ built for your situation /

Sounds familiar?

Head of Data, retail group
"Our sales data lives in three different systems — Shopify, our ERP, and a warehouse management tool — and we can't get a single view of inventory performance"

We build a unified data pipeline that ingests from all three sources, applies consistent business logic, and loads into a data warehouse your BI team can query in real time.

How Neural AI helps

We build a unified data pipeline that ingests from all three sources, applies consistent business logic, and loads into a data warehouse your BI team can query in real time.

/ proof /

Real deployments. Real results.

Business Intelligence

Compre Group Dashboard

Power BI dashboard providing comprehensive visibility into payables, costs, and financial operations for Compre Group's insurance business.

Unified analytical warehouse serving 50+ management reports

Read case study →
Business Intelligence

Film Sector Power BI

Custom Power BI backend for the Malta Film Commission tracking diversity, equity, and inclusion across crews, productions, trainees, and departments.

Centralised data warehouse powering industry-wide analytics

Read case study → Business Intelligence

Social Benefits Dashboard

Two interactive public-facing dashboards using Google Looker Studio for real-time monitoring of social benefits and expenditure data across Malta.

Warehouse processing 500K+ records for policy decision support

Read case study →
/ questions /

Data Warehouse Development FAQ

What is the difference between a data warehouse and a data lake?
A data warehouse stores structured, curated, and modelled data optimised for analytical queries. A data lake stores raw data in its native format for flexible processing. Modern lakehouse architectures combine both, storing data in lake format with warehouse-like query performance. We help you choose the right approach based on your analytical needs and data variety.
Should we choose Snowflake, Redshift, or BigQuery?
Snowflake offers the best multi-cloud flexibility and separation of compute from storage. Redshift integrates deeply with the AWS ecosystem. BigQuery provides serverless simplicity with excellent cost-per-query economics. Azure Synapse suits Microsoft-centric organisations. Your existing cloud platform, team skills, and workload characteristics guide the recommendation.
How long does it take to build a data warehouse?
A foundational warehouse with core dimensional models and key source integrations typically takes 8-12 weeks. Enterprise warehouses with many subject areas, complex transformations, and compliance requirements may take 3-6 months. We deliver iteratively, providing value from early stages while building toward the complete vision.
What is dbt and why should we use it?
dbt is a transformation framework that enables data teams to write modular, tested, and documented SQL transformations version-controlled in git. It brings software engineering practices to data transformation, dramatically improving reliability, collaboration, and maintainability compared to stored procedures or ad-hoc scripts.
Can you migrate our existing data warehouse?
Yes, we handle migrations between warehouse platforms including on-premises to cloud, Redshift to Snowflake, and legacy systems to modern platforms. Migration includes schema conversion, query translation, pipeline updates, and parallel validation to ensure data accuracy and minimal disruption.
How do you handle data warehouse costs?
Cloud warehouse costs are primarily driven by compute usage and storage volume. We optimise costs through warehouse scheduling, auto-suspend, query optimisation, materialised views that reduce compute, and storage tiering for historical data. Most clients achieve 30-50% cost reduction through our optimisation practices.
What about data warehouse security and access control?
We implement role-based access control, row-level security, column masking, and encryption at rest and in transit. Data classification labels govern access policies, and audit logging tracks all data access for compliance. These controls satisfy GDPR, MGA, and MFSA regulatory requirements.
Can the data warehouse support machine learning workloads?
Yes, modern cloud warehouses provide ML capabilities. Snowflake has Snowpark, BigQuery has BQML, and Redshift has Redshift ML. For more advanced ML workloads, the warehouse serves as the feature store source, feeding prepared data into dedicated ML platforms like Databricks or SageMaker.
/ get started /

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