Data Warehousing and Data Engineering

Data architecture is the foundation for scale and speed

If your current data systems are struggling and are hard (and expensive) to maintain, reports take hours to run, you may be looking to upgrade your data infrastructure.

Qurious Solutions designs and implements scalable, fast, cost-effective cloud data warehouses that grow with your business and enable analytics at any scale.

Why implement cloud data warehousing

Cloud data warehouses have fundamentally changed what’s possible with enterprise data and set high-performance expectations:

Modern platforms reduce query latency by up to 90%; reports now run in seconds, not hours
Unlimited concurrent users and queries; scale storage and compute independently
Pay-as-you-go pricing eliminates expensive hardware; typical cloud warehouses cost 60-70% less than on-premises alternatives
Reduced maintenance without patching, tuning, or infrastructure management; you focus on analytics instead of IT operations

Data engineering can have a profound business impact:

Real-time queries enable immediate insights for faster decision-making
20-30% cost reduction through automation and cloud economics
Competitive agility – get faster to market with analytics-driven products and services
Data warehouses handle 10x, 100x, or 1000x data growth without infrastructure upheaval

Why choose Qurious Solutions for Data Warehouse Migration

No job is too big or too small.

We have delivered over 200 projects for multiple sector clients – mining companies, financial services, retail and logistics, healthcare, government and utilities – from ASX-listed giants to small businesses, and understand the regulatory environment and compliance requirements specific to Australian companies.

Technology-agnostic. Results-focused.

We’re not pushing you toward one platform. Our recommendation depends on your requirements—whether that’s Snowflake, BigQuery, Redshift, Azure, or Databricks.

Qurious Solutions delivers projects faster and more cost-effectively by focusing on best-in-class solutions of today, not replicating what was used yesterday. We focus on real business needs, leverage modern platforms’ capabilities, and avoid unnecessary complexity — so you get solutions that deploy quickly and drive real adoption.

Our Data Warehousing & Data Engineering Services

1. Cloud Data Warehouse Design & Selection

Choosing the right platform is critical. We assess your requirements and recommend the platform that delivers the best value for your situation. Our evaluation includes:

Workload analysis: Understanding query patterns, data volume, and concurrent user needs
Technology fit: Evaluate Snowflake, BigQuery, Redshift, Azure Synapse, Microsoft Fabric, Databricks based on your requirements
Total cost of ownership: Calculate licensing, infrastructure, and operational costs for each option
Ecosystem integration: Ensure compatibility with your BI tools, data integration platforms, and analytics services
Roadmap alignment: Select a platform that supports future analytics and AI initiatives

Platform Overview:

Platform	Best For	Key Advantage
Snowflake	Organisations needing multi-cloud flexibility	Separates storage & compute; easy scaling; zero maintenance
BigQuery	Google Cloud ecosystem; real-time analytics	Fully serverless; integrates with Google Workspace; strong ML support
Redshift	AWS ecosystem; large-scale analytics	High performance; deep AWS integration; mature platform
Microsoft Fabric	Microsoft ecosystem; integrated BI/ML	Power BI integration; unified analytics & BI; SQL/code flexibility
Databricks	Machine learning & advanced analytics	Unified data engineering & ML; handles structured & unstructured; strong collaboration

2. Data Warehouse Architecture & Implementation

We design scalable, performant architectures, then implement them with minimal business disruption:

Schema design optimised for analytical queries (star schema, dimensional modelling)
Data modelling: dimensional tables, fact tables, conformed dimensions for consistency
Partition data for faster queries and cost optimisation
Indexing & optimisation, performance tuning for common query patterns
Compute resource allocation: balance performance vs. cost
Backup, replication, and failover procedures

Infrastructure provisioning, cloud environment setup with security and compliance controls
Data migration from legacy systems; transform and load into cloud warehouse
Comprehensive testing before cutover; parallel run verification
Performance optimisation through query tuning and configuration refinement
Architecture documentation and operational procedures

3. Data Integration & ETL Pipeline Development

Raw data in cloud storage isn’t useful until it’s transformed and loaded into the warehouse. We build reliable ETL processes:

Connect to multiple data sources (ERP, CRM, databases, APIs, cloud applications)
Cleanse, normalise, enrich data; apply business rules; handle edge cases
Efficient data loading optimised for cloud platforms
Automated retry logic, data quality checks, and error notification
Streaming pipelines for near-real-time analytics
Automate pipeline scheduling, dependencies, and failure handling

4. Advanced Data Architecture (Data Lakes, Lakehouses, Iceberg)

Modern data platforms handle both structured (data warehouse) and unstructured data (logs, images, documents). We architect solutions for both:

Data Lake: Raw data repository for any data type; supports exploratory analytics and ML
Lakehouse, including Iceberg: Combines data lake flexibility with warehouse performance; single system for analytics and ML
Data Mesh: A Decentralized architecture where business domains own their data
Real-time data streaming: Kafka, Firehose, Pub/Sub integration for continuous analytics

5. Cloud Data Platform Operations & Optimisation

We maintain and optimise your data warehouse continuously. Our managed services keep your platform running efficiently:

Performance monitoring: Dashboards showing query performance, resource utilisation, cost
Cost optimisation: Identify opportunities to reduce spend through workload management and resource allocation
Capacity planning: Forecast data growth and recommend resource adjustments
Security maintenance: Access control reviews, compliance monitoring, security patching
Disaster recovery testing: Regular testing of backup and failover procedures
User support & training: Ongoing technical support and education for analytics teams

6. Data Engineering Ongoing Support

We provide experienced data engineers to supplement your internal team.

Contract data engineers: we work with your internal team for design, implementation, or ongoing operations
On-site engineers working directly with your teams
Train internal teams on data platform management and optimisation
Periodic architecture reviews ensure platform remains optimised for evolving requirements

Common questions about Data Warehousing and Data Engineering

What's the timeline for implementing a cloud data warehouse?

Typical cloud warehouse implementation takes 1-2 months depending on complexity.

Our experienced team and agile approach make it possible to complete data projects a lot faster than industry average – typically 4-18 weeks. Most of our clients see measurable benefits in as little as four weeks’ time (versus the industry average of 8 weeks).

How much does a cloud data warehouse cost?

Costs vary widely based on data volume and query complexity. Small implementations run $15,000-$50,000 annually; large enterprises pay $100,000-$500,000+. Most cloud warehouses cost significantly less than on-premises alternatives—typically 60-70% cheaper within 12 months.

Do we need to migrate all our data?

No. Many organisations start with critical data (sales, finance, customers), then expand over time. We recommend prioritising high-value use cases to demonstrate ROI early.

Can we keep our existing BI tools?

Yes. Cloud data warehouses connect to all major BI platforms (Power BI, Tableau, Looker, Qlik). No need to change existing tools; the warehouse works alongside them.

What about data security in the cloud?

Cloud providers offer enterprise-grade security often exceeding on-premises standards: encryption at rest/in transit, advanced identity management, audit logging, and compliance certifications. Qurious Solutions ensures your specific security and compliance requirements are met during implementation.

How do we handle the learning curve?

Cloud data platforms require different skills than on-premises warehouses. We provide training for your data engineering and analytics teams. Ongoing support during the first 12 months ensures a smooth transition.

What if we have poor data quality currently?

Data warehouse implementation is an opportunity to fix data quality. We incorporate data cleansing and quality improvement into ETL pipelines. Quality improves over time as new controls take effect.

Considering migration to data warehouse?

Schedule a consultation (30 minutes, no obligation)

Discuss your current data setup, challenges and performance issues
Learn how peer enterprises are leveraging cloud data platforms
Explore whether data warehousing aligns with your technology strategy