Modern Data Warehousing Services Built for Growth

Your business runs on data, but legacy systems, brittle pipelines, and siloed sources can make even simple questions hard to answer. SPR’s modern data warehousing services help you build a single, reliable foundation for analytics, reporting, and AI, so your teams can make decisions with confidence.

We work with organizations that are ready to move beyond spreadsheets and scattered databases to a scalable enterprise data warehouse, leveraging cloud platforms and modern architectures to support growth, innovation, and AI initiatives.

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The Problems We Solve for Data and Analytics Teams

Data and analytics teams are under pressure to deliver insights faster, support AI, and keep costs in check, often while managing aging infrastructure and fragmented data. SPR’s data warehouse consultants help you address challenges such as:

Conflicting metrics and “multiple versions of the truth” across reports
Unreliable dashboards that frustrate stakeholders
Legacy warehouses that can’t scale or deliver real-time needs
Complex ETL jobs that are fragile, hard to maintain, and expensive to run
Security, governance, and compliance gaps as more data moves to the cloud
Difficulty connecting warehouse and pipeline investments to AI and advanced analytics

We bring order to this complexity by consolidating data, modernizing platforms, and shaping a warehouse and pipeline strategy that serves both today’s reporting and tomorrow’s AI roadmap.

Core Data Warehousing Services

Our data warehousing services span the full lifecycle, from strategy and design through implementation, optimization, and ongoing support.

Strategy, Assessment & Roadmapping

We start by understanding your current landscape, business goals, and analytics needs. Then we define a pragmatic roadmap for your enterprise data warehouse, including priorities, timelines, and measurable outcomes.

Architecture & Platform Selection

Whether you’re building from scratch or modernizing an existing environment, we help you select and design the right combination of enterprise data warehouse software and cloud services. Our architects are experienced across platforms such as AWS, Azure, and modern warehouse solutions like Azure Data Factory, Snowflake, Databricks, and Amazon Redshift.

Data Modeling, ETL/ELT & Pipeline Development

We help you with complex transformations when your data doesn’t align across systems, design data models that reflect how your business actually works, then build resilient ETL/ELT processes and pipelines to move, transform, and load data efficiently. Our data engineering services emphasize scalability, observability, and cost control so your pipelines are as reliable as your warehouse.

Data Migration & Modernization

From on-premises warehouses to cloud-native platforms, we plan and execute migrations that reduce risk and downtime. SPR ensures your data is moved, reconciled, and validated, while aligning new capabilities with broader cloud migration strategies.

Testing, Data Quality & Governance

We embed quality from the start by validating data, implementing reconciliation processes, and establishing governance patterns so users can trust what they see. Our work complements broader cloud governance and security practices across your environment.

Performance Optimization & Cost Management

We continuously tune queries, storage, and compute to keep your data warehouse running fast and cost-effectively. This includes workload management, partitioning, indexing or clustering strategies, and recommendations for how teams should use the platform day to day.

Enablement, Support & Managed Services

Once your data warehouse is in place, we train your teams, document key processes, and provide ongoing support or managed services as needed, so the platform grows and evolves alongside your business.

How We Build High-Performance Data Warehouses

No two organizations are the same, but successful data warehousing projects tend to follow a consistent pattern. SPR’s process is structured enough to be reliable and flexible enough to meet you where you are.

Discovery & Assessment: We inventory current systems, reports, and data sources; identify pain points; and align on the business outcomes you want from your warehouse.
Architecture & Modeling Strategy: We design your future-state architecture, selecting the right enterprise data warehouse software and cloud services that fit your needs and technology ecosystem, then define data models and integration patterns to support analytics, AI, and operational reporting.
Development & Implementation: Our teams build out data pipelines, schemas, and orchestration, following best practices in data engineering, testing, and DevOps. Where appropriate, we align with your existing cloud enablement and migration roadmaps.
QA, Optimization & Validation: We validate data quality, reconcile critical metrics, and tune performance. This includes stress-testing workloads, addressing security and governance concerns, and ensuring the warehouse fits cleanly into your broader cloud and security posture.
Knowledge Transfer & Enablement: We document the environment, train your data and analytics teams, and establish ongoing practices for monitoring, cost management, and enhancements.
Ongoing Support & Evolution: As your business and data needs grow, we help you expand the warehouse, integrate new sources, and support advanced use cases like AI, machine learning, and agentic workflows.

Why Choose SPR?

Choosing a data warehouse consulting partner is about more than technology, it’s about finding a team that can bridge strategy, architecture, and execution.

End-to-End Cloud & Data Expertise
SPR is a technology modernization firm focused on cloud, data, and AI. We bring experience across data engineering, cloud enablement, and custom software development, so your warehouse is tightly integrated with the rest of your digital ecosystem.

Deep Experience with Modern Warehouses
We design secure, highly scalable data warehouses that take advantage of modern platforms like Snowflake, Databricks, Azure and AWS data services, helping you integrate with ETL tools and streaming platforms without locking you into a single vendor.

Built for AI and Advanced Analytics
Our artificial intelligence and machine learning practices ensure your data warehouse is ready to support predictive models, generative AI, and AI agents, not just historical reporting. We think in terms of the entire data lifecycle, from ingestion to AI-driven decisioning.

Security, Governance & Compliance First
We align warehousing efforts with cloud governance and security practices, ensuring robust controls, clear ownership, and compliance with industry regulations.

Collaborative, Embedded Approach
We work side by side with your teams, sharing knowledge and best practices so you’re self-sufficient. When you work with SPR, you get more than data warehouse consultants. You get a partner committed to your long-term success.

Contact Us to Discuss Your Data Warehouse

Industries We Serve

Our modern data warehousing services support organizations across a wide range of industries, including:

Financial Services & Insurance
Risk, compliance, and customer analytics
Healthcare & Life Sciences
Clinical, operational, and patient insights
Manufacturing & Industrial
Supply chain, operations, and quality monitoring
Retail & E-Commerce
Customer behavior, personalization, and inventory optimization
Technology & SaaS
Product analytics and usage telemetry
Logistics & Transportation
Network performance, routing, and demand forecasting

When you’re ready, we can take your data warehouse and reporting insights further with predictive analytics solutions embedded into your business processes.

Our Tech Stack

SPR works across major cloud platforms and data technologies, combining the right tools for your needs instead of forcing a one-size-fits-all stack. We’re proud to maintain deep relationships with leading cloud providers and data platforms (such as Amazon Web Services and Microsoft Azure) while leveraging modern data warehouse solutions like Snowflake, Databricks, Azure Data Factory and Amazon Redshift. We select and combine these technologies to fit your organization’s strategy, governance requirements, and existing investments.

Our experience includes:

Cloud data platforms on AWS, Azure, and other major clouds
Modern enterprise data warehouse software such as Snowflake, Databricks, Amazon Redshift, and Azure SQL Data Warehouse
Data engineering and integration tools (e.g., ETL/ELT platforms, orchestration tools, and streaming services like Amazon Kinesis)
Analytics and BI tools for reporting and visualization
AI and machine learning platforms that consume curated warehouse data providing you predictive analytics solutions.

The Difference Proper Data Warehousing Makes

A well-designed warehouse transforms how your teams work with data.

Before

Conflicting metrics and competing dashboards
Manual spreadsheet work and ad-hoc extracts
Slow query performance and frequent timeouts
Limited visibility into data lineage and quality
Security gaps and unclear ownership
Difficulty feeding AI and machine learning with trustworthy data

After

A single, trusted enterprise data warehouse that serves as your “source of truth”
Fast, reliable dashboards and self-service analytics
Automated, observable pipelines with clear SLAs
Strong governance, security, and compliance built into the platform
Predictable cloud spend and tunable performance
High-quality, well-modeled data ready for AI, machine learning, and advanced analytics

Centralized Data Warehouse for a Cloud-Native Platform

A leading affiliate marketing company needed more resilient and reliable reporting for its publisher development team. They were managing critical affiliate data in spreadsheets, making it difficult to scale, audit, or trust reporting as the program grew. SPR helped design and implement a cloud-native application backed by a centralized enterprise data warehouse. The solution:

Consolidated affiliate data into a single warehouse, eliminating spreadsheet silos
Enabled teams to manage publisher information in a structured system of record
Delivered reporting directly from the main data warehouse, improving reliability and performance

With a modern warehouse and pipeline in place, the organization gained a durable foundation for future analytics and AI initiatives across their broader data platform.

View Case Study

Frequently Asked Questions

How does data warehousing support my organization’s AI capabilities?

A modern data warehouse provides the clean, consistent, and well-governed data that AI and machine learning models depend on. Instead of pulling from dozens of inconsistent sources, your AI initiatives draw from curated, documented datasets with clear definitions and lineage.

When paired with SPR’s artificial intelligence and machine learning services, your warehouse becomes the backbone for initiatives like predictive analytics, generative AI, and AI agents, helping you move from experimentation to production-ready AI more quickly and reliably.

How long does a typical data warehouse implementation take?

Timelines vary based on scope, complexity, and the state of your current environment. Many organizations start seeing value within weeks through an initial proof of concept focused on a specific domain or set of reports. Larger, enterprise-wide implementations may unfold over several months to a year in phased releases.

SPR works with you to define a realistic roadmap (from early wins to full-scale modernization) so stakeholders see progress without overloading internal teams.

What’s the difference between a data warehouse and a data lake?

A data warehouse is a structured, curated environment designed to support reporting, analytics, and decision-making. It emphasizes consistent definitions, quality, and performance for business users.

A data lake typically stores raw, large-volume data in its original format, often for exploration, data science, or long-term retention. Without careful design, data lakes can turn into “data dumps” that are hard to use. In practice, many organizations use both, landing raw data in a lake, then transforming it into warehouse-ready structures for trusted analytics.

How do you maintain quality data?

Data quality starts with design. We define clear data models, establish standards for naming and definitions, and ensure sources are integrated consistently. From there, we implement validation rules, data lineage tracking, reconciliation checks, and monitoring to detect issues early.

We also align with your broader cloud governance and security practices, so ownership, approvals, and processes are clear. The result is a data warehouse where teams trust the numbers and can trace them back to their origins.

What’s required of our internal teams during a project?

Your internal teams are critical to a successful data warehouse implementation. We typically ask for:

Business stakeholders to define key metrics, use cases, and success criteria
Data and IT teams to help us understand existing systems, data sources, and constraints
Security and compliance stakeholders to ensure compliance and governance requirements are met

SPR handles the heavy lifting, such as architecture, engineering, testing, and documentation, all while collaborating closely with your teams to make sure the warehouse aligns with how your business actually runs.