Data Warehouse Pricing

How to Plan Your DWH Project Budget

In data warehouse development since 2005, ScienceSoft outlines the main cost factors of DWH implementation to help businesses in 30+ industries confidently plan their data analytics initiatives.

Data Warehouse Implementation Pricing - ScienceSoft
Data Warehouse Implementation Pricing - ScienceSoft

Data Warehouse Cost in Brief

The major factors influencing the cost of data warehouse implementation include the number of data sources, the diversity of data types, the data processing specifics (batch or real-time), the chosen architecture, the tech stack, and the deployment model. The requirements for analytics & reporting complexity, data quality management, security, and regulatory compliance also influence the TCO.

Depending on these variables, the cost of data warehouse implementation can range from $30K to $2M.

On average, data warehouse implementation brings a 400% five-year ROI with a payback period of 9 months and leads to a 30% increase in the productivity of data and analytics teams.

Key Factors Defining Data Warehouse Cost

Data warehouse pricing depends on particular business purposes and the solution’s technological complexity. Below, our experts outline the main factors to consider when planning your DWH implementation budget. To make the cost ranges easy to compare, we outlined three major DWH pricing brackets: from a basic system to a large-scale enterprise data warehouse.

Basic data warehouse

Corporate data warehouse

Enterprise data warehouse

Data sources

Up to 5 internal sources (e.g., ERP, CRM, SCM).

All the required internal sources.

  • Unlimited number and types of data sources, incl., e.g., IoT user apps and devices.
  • Several same-type sources (e.g., ERPs from different enterprise divisions).
Data diversity and complexity

Structured data arriving at scheduled intervals.

All data types (structured, unstructured, and semi-structured) arriving at scheduled intervals.

  • All data types arriving in unpredicted patterns.
  • Support for region-related data specifics (e.g., different currencies and taxation systems).
Data processing specifics

Batch data processing (e.g., once every 24 hours).

Batch and real-time processing.

Batch and real-time processing.

Data quality management


Partially automated.

Maximally automated.

OLAP tools

Online analytical processing (OLAP) tools present data in multiple dimensions, enabling users to consolidate, drill down, slice, and dice it from various perspectives.


Via market-available reporting and visualization tools (e.g., Microsoft Power BI, Tableau).

Via market-available reporting and visualization tools (e.g., Microsoft Power BI, Tableau).

Via market-available reporting and visualization tools + custom data visualization modules.

Analytics complexity

Rule-based analytics.

Rule-based and ML/AI-powered analytics.

Rule-based and ML/AI-powered analytics, including real-time and big data analytics. ML training modules for continuous updating of ML models.





Additional Cost-Defining Factors

Data volume.

The number of BI users (overall, daily, concurrent).

The diversity of user roles (C-level, analysts, data scientists, department-specific BI users, etc.).

DWH deployment format (on-premises, cloud-native, cloud-only, hybrid).

Data security requirements (e.g., end-to-end data encryption, row-level security).

Fault-tolerance, scalability, and availability requirements (e.g., data redundancy, data backup mechanisms & backup frequency, failover mechanisms, DWH monitoring and alerting systems).

Regulatory compliance requirements (e.g., HIPAA, PCI DSS, GDPR).

Data Warehouse Implementation: Cost vs. ROI

When optimizing DWH implementation costs, it may be tempting to give up advanced capabilities like automated data quality management. However, features requiring substantial initial investments may secure lower operating costs and higher productivity of the entire system in the long run. For instance, implementing an efficient cloud DWH with a feasible degree of data management automation can bring up to 400% five-year ROI with a payback period of around 9 months, driven by a 30% increase in the productivity of data management and analytics teams. Thus, the benefit/cost ratio and ROI are essential factors to consider when planning your DWH budget.

How ScienceSoft Optimizes DWH Development Costs Without Compromising Quality

Implementing DWH and BI solutions for optimized data storage and enterprise-wide decision-making since 2005, ScienceSoft knows how to build a robust data warehouse that drives maximum value at an optimal cost.

Expert data sources audit

We exclude redundant data sources (e.g., due to their little relevance for BI purposes or duplicate information), which allows our customers to avoid expenses on unnecessary integrations, capacity, and quality management procedures.

Prompt MVP delivery

With a data warehouse MVP, you can start gaining payback and gathering user feedback long before the release of the full-featured solution.

Vendor neutrality

Having partnerships with AWS, Azure, Oracle, and other global tech leaders, we opt for the technologies that will bring the most value in your case, considering the data volume and analytics requirements and the existing tech stack.

Implement a Data Warehouse With Experts

In data analytics services since 1989, ScienceSoft has implemented specialized solutions for a broad range of demanding industries, including healthcare, manufacturing, BFSI, retail, telecoms, and more. Our analysts, data scientists, and software engineers are ready to share their experience to implement a tailored DWH for your unique needs.

DWH implementation consulting

We understand that DWH implementation is a large undertaking that requires careful planning. ScienceSoft’s experts will conduct a feasibility study, design the DWH architecture, find the optimal tech stack, and estimate the project cost and ROI to help you avoid unnecessary risks.

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DWH development

Finding a reliable vendor for any large-scale IT initiative is half the battle won. Let us prove our expertise in practice: ScienceSoft is ready to develop a cost-effective implementation plan and deliver a high-performing solution that will address your current business needs and will be easy to evolve in the future.

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About ScienceSoft

ScienceSoft is a global IT consulting and software development company headquartered in McKinney, Texas. Since 2005, we’ve been helping our customers implement secure, scalable data warehouses with the optimal cost-benefit ratio. Relying on ISO 9001 and ISO 27001-certified management systems, we guarantee high service quality and full security of our customers’ data.

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