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Data Warehouse Design

A Full Guide

Since 2005, ScienceSoft’s data warehouse consultants help companies design scalable and high-performing data warehouses.

Data Warehousing Services
Data Warehouse Design Guide - ScienceSoft
Data Warehouse Design Guide - ScienceSoft

Data Warehouse Design: the Essence

A data warehouse provides for the integration, structuring and storing of business data for analytical querying and reporting. We, at ScienceSoft, consider data warehouse design the first step in implementing a data warehouse solution, as at this stage we focus on creating the architecture of a data warehouse system.

  • Project time: From 2 months.
  • Data warehouse planning steps:  Requirements engineering, discovery, data warehouse conceptualization, project planning, data warehouse technologies selection, system analysis and data governance design, data modeling and ETL design.
  • Cost: Starts from $40,000.
  • Team: A project manager, a business analyst, a data warehouse system analyst, a solution architect, a data engineer.

Data Warehouse Solution Architecture

A typical data warehouse architecture includes:

Data source layer

– internal and external data sources (ERP, CRM, sensor devices, social media, public databases, etc.) providing data fed into the data warehouse.

Staging area

– a temporary repository where records from data source systems undergo consolidation and processing before loading into the storage area. The staging area may be absent when data transformation goes in the target database (data warehouse/data marts).

Data storage layer

– hosting a data warehouse database permanent data storage that keeps slightly and highly structured data, and data marts – data warehouse subsets providing information for reporting and analysis for a company’s specific business line, department, or team.

Analytics and BI

– the data in the data warehouse database and data marts can be queried via OLAP tools, data mining tools, reporting and visualization tools.

Sample DWH solution architecture

Data Warehouse Design Plan

A data warehouse design process and its duration depend on:

  • Source system complexity and quality.
  • Data analytics complexity.
  • Data security complexity, etc.

Based on ScienceSoft's ample experience in designing and implementing data warehousing solutions, we list core steps needed to design a data warehouse solution.

Note: The timeframes below are highly approximate, as, for example, the architecture design project for an enterprise-level data warehouse may last up to 3-6 months and even more because of the project scale and specificity.


Data warehouse requirements engineering




Data warehouse conceptualization


Data warehouse design project planning


Data warehouse technologies selection


Data warehouse system analysis and data governance design


Data warehouse data modeling and ETL/ELT design

Note: The next steps would be data warehouse development and launch, which are not addressed within the framework of this guide. In case you are interested in the end-to-end data warehouse implementation process, explore our structured overview of the data warehouse implementation process.

Consider Professional Data Warehouse Design and Implementation Services

With 18 years in data warehousing services, ScienceSoft helps you design and implement a cost-effective data warehouse solution meeting your tactical and strategic business needs.

Data warehouse design

  • DWH requirements engineering.
  • DWH design project planning.
  • DWH solution conceptualization and architecture design.
  • DWH software selection.
  • DWH system analysis and data governance design.
  • Design of data models and ETL/ELT process.
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Data warehouse implementation

  • DWH requirements engineering.
  • DWH solution conceptualization and platform selection.
  • DWH architecture design.
  • DWH solution development.
  • DWH quality assurance and launch.
  • DWH support and evolution.
Request DWH implementation

What Makes ScienceSoft a Trustworthy Partner

  • Data warehousing services since 2005.
  • Data analytics expertise since 1989.
  • Designing and implementing business intelligence solutions since 2005.
  • A dedicated team of DWH solution architects, data engineers, DevOps specialists, database administrators, QA specialists.
  • Expertise in delivering complex and large-scale solutions for 30+ industries.
  • ScienceSoft USA Corporation is listed among The Americas’ Fastest-Growing Companies 2022 by Financial Times.

ScienceSoft as a Trusted Data Warehousing Tech Partner:

When we first contacted ScienceSoft, we needed expert advice on the creation of the centralized analytical solution to achieve company-wide transparent analytics and reporting. 

The system created by ScienceSoft automates data integration from different sources, invoice generation, and provides visibility into the invoicing process. We have already engaged ScienceSoft in supporting the solution and would definitely consider ScienceSoft as an IT vendor in the future.

Heather Owen Nigl, Chief Financial Officer, Alta Resources

Our Hallmark Data Warehouse and BI Projects

Development of a Cloud DWH and BI Solution for the Producer of Phytotherapy Products

  • Transparent company-wide reporting and analysis.
  • 20+ report templates to cater to different departmental needs.
  • A set of user guides with the detailed information on the solution’s components and functions.

Migration of a Data Warehousing Solution to Facilitate Big Data Analysis

  • Five-module analytics system for processing more than 1,000 different types of raw data and analyzing around 30,000 attributes.
  • Up to 100 times faster analytical query processing.

Development of a DWH and Analytics Solution for 500+ Nursing Homes

  • Improved analytical process and standardized reporting.
  • Simplified analytical system support due to the consolidation achieved at all levels (database, SSAS, and reporting).

Development of a DWH and Analytics Solution for a Multibusiness Corporation

  • Ingesting and storing structured and unstructured data from 15 data sources.
  • About 100 ETL processes.
  • An analytical server with 5 OLAP cubes and about 60 dimensions overall.
  • 90+ reports.

Development of a DWH and Analytics Solution for Advanced Sales Analysis

  • Solution for a multinational FMCG corporation with more than 200 markets, 1 bn consumers, and 60,000 employees.
  • Three-module BI solution for data processing and unification.

Development of a DWH and Analytics Solution for a Regulatory Authority

  • Centralized BI platform with an analytics sandbox to support experimental/development analytics activities.
  • Support for 200+ concurrent business users handling over 500 reports simultaneously.

Development of a Data Warehouse and Analytics Solution for Luxury Vehicle Dealers

  • Analytical system for the automotive software provider with a network of 55,000 clients in 80 countries.
  • ETL-based DWH solution with a staging area, DWH database and data marts.
  • Over 40 customizable reports and dashboards.

Typical Roles in ScienceSoft's Data Warehouse Design Projects

Project manager

End-to-end data warehouse design project management:

  • Defines data warehouse design project scope, goals and deliverables.
  • Develops the data warehouse design project plan and communication approach.
  • Communicates data warehouse design project purpose and expectations to stakeholders.
  • Estimates and coordinates the efforts of data warehouse design project team members.
  • Ensures timelines and quality of the data warehouse design project deliverables within the set budget frames.

Business analyst

  • Analyzes the needs of key stakeholders and end users and translates the needs into the data warehouse requirements affecting design (e.g., the data warehouse solution should support operational analytics).
  • Describes the scope of the data warehouse system, its modules, and integrations with other software.

Solution architect

  • Designs a data warehouse architecture based on business and technology requirements.
  • Ensures the architectural requirements (availability, scalability, performance, reliability, etc.) are implemented in the data warehouse design.
  • Suggests a technology stack.

Data warehouse system analyst

  • Examines data sources and data analytics software (if any) to be integrated into the data warehouse solution.
  • Draws up a system requirements specification for creating data models, designing ETL/ELT processes, etc.
  • Defines data integrity and data cleansing rules, etc.

Data engineer

  • Designs a data model and its structures and draws up the data flows.
  • Designs ETL/ELT processes.

Sourcing Models

All in-house

Pros: The company has full control over the data warehouse design project.

Caution: Risk of project delays/failure due to the shortage of resources.

Outsourcing of technical resources

The company owns the data warehouse design project management while relying on outsourced resources to perform data warehouse platform selection, data warehouse solution architecture design and data modeling, etc.

Pros: No risk of the technical resources overprovisioning after the project completion.

Caution: The model requires constant cooperation of all team members. High requirements for in-house PM and BA competencies.

Complete outsourcing (in-house project sponsor, everything else is outsourced)

The company communicates its data warehouse-related needs to a vendor, who takes on detailed data warehouse requirements engineering, business planning, systems analysis, data warehouse design, etc.

Pros: No data warehouse project delays or failures due to resource unavailability.

Caution: Increased vendor dependency.

Get Your DWH Well-Designed!

ScienceSoft’s data warehouse team is ready to design a cost-effective and high-performing data warehouse solution within the set time and budget frames, applying data warehouse design best practices.

Benefits of Data Warehouse Design with ScienceSoft

Data warehouse services from A to Z

With our 18 years of experience in business intelligence, we are eager to deliver any DWH-related service to you, be it design, implementation, support, data management, security, etc.

Traditional BI + Big Data

Our team is equally competent to design and implement data warehouses for both traditional and big data analytics solutions.


Multi-industry experience

Having expertise in 30 industries, we know how to design our data warehouse solutions to meet our customers' individual needs.

Get all the information you need to choose an optimal data warehouse technology for your project in our free guide.

Choose Optimal Techs to Design a Reliable DWH

We are ready to assist you with selecting the right data warehouse technology stack to design a scalable and effective data warehouse solution to address your short-and long-term data storage and processing needs and reduce data warehouse implementation and maintenance costs.

Data Warehouse Design Cost

Among the major data warehouse design cost drivers are:

  • Number of data sources (ERP, CRM, SCM, etc.), data disparity across different sources (e.g., the difference in the data structure, format), data source complexity.
  • Data volume to be processed and stored.
  • Source data quality (low-quality data requires sophisticated data cleansing procedures).
  • Required data security level.
  • Data warehouse velocity, scalability, and fault tolerance requirements.

About ScienceSoft

ScienceSoft is a global IT consulting and software development company headquartered in McKinney, TX, US. Since 2005, we’ve been providing d serata warehousing services, including data warehouse consulting, to help our customers build robust analytics with scalable and effective data warehouse solutions designed in accordance with their particular business needs. Being ISO 27001-certified, we guarantee cooperation with us does not pose any risks to our customers' data security.