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Enterprise Data Warehouse

Architecture, Features, Integrations, Costs

Since 2005, ScienceSoft provides businesses from 30+ industries with scalable EDW solutions for efficient data consolidation and enterprise-wide analytics.

Enterprise Data Warehouse (EDW)
Enterprise Data Warehouse (EDW)

What Is an Enterprise Data Warehouse: Core Concepts

An enterprise data warehouse (EDW) is a data management solution that centralizes company-wide data in a highly structured format ready for analytics querying and reporting.

  • Possible integrations: a data lake, ML and BI software.
  • Implementation timeline: 3-12 months.
  • Implementation costs: $70,000 – $1,000,000, depending on solution complexity. We can provide you with a custom estimate based on your needs. Use our free calculator below.
  • ROI: up to 400% five-year ROI with the investment usually breaking even within 9 months.

To enable answering both enterprise-level and department-specific questions, EDW ingests data from all corporate business-critical software and external data sources, including:

  • Enterprise resource planning (ERP) system.
  • Customer relationship management (CRM) system.
  • Accounting software.
  • Talent management system.
  • Business process management (BPM) system.
  • Intranet.
  • The company’s website.
  • IoT device management system.
  • Publicly available datasets for machine learning, etc.

EDW as Part of Revenue-Driving Enterprise Intelligence Framework

Enterprise intelligence is the ability of a business to synthesize information, deliver insights at scale, learn from them collectively, and develop a strong data culture. IDC reports that businesses with excellent enterprise intelligence index achieve a 10% increase in revenue growth and customer acquisition rates and enjoy shorter time to market for new offerings.

Below, our experts share a high-level EDW architecture and show the EDW’s core place within the enterprise intelligence framework.

Enterprise Intelligence Framework - ScienceSoft

  • Information synthesis: an EDW pulls data from multiple sources, cleans, aggregates or otherwise transforms it, and loads analytics-ready data to the data storage.
  • Insights delivery at scale: an EDW feeds BI and ML/AI analytics tools with accurate, up-to-date data. EDW’s highly structured nature allows for both scheduled reporting and ad hoc data exploration, which ensures flexibility in insights delivery.
  • Collective learning: being a single point of truth, an EDW guarantees that all users make decisions based on common information and remain on the same page regarding current processes and past experiences.
  • Strong data culture: An EDW is essential for eliminating manual work and providing value-driving insights. It helps build an environment where employees value data, trust it, and prioritize data-driven decision making.

Enterprise Data Warehouse Types

There are three deployment environment types for data warehousing solutions:

  • On-premises – a company purchases all required hardware and software to build and deploy an EDW and maintains it further on.
  • Cloud-hosted – a company deploys an EDW in the cloud, eliminating the need to purchase and maintain hardware and software.
  • Hybrid – a company augments an on-premises enterprise data warehouse with a cloud-hosted repository.



  • Full control over the enterprise data warehouse. In case of a failure, an in-house IT team has direct access to the DWH’s problem area for hardware and software tuning. Moreover, data security remains strictly under the in-house IT team’s control.
  • Full compliance with the required data standards. Data security compliance is easier to achieve with on-premises enterprise DWHs.
  • Availability. Business users from a facility where the EDW is located can effectively access all the data stored in the data warehouse without dependence on the internet connection.

See the points of caution


  • Full responsibility. Together with the control of the on-premises enterprise data warehouse, a company is fully responsible for its implementation and maintenance.
  • Complexity of agile scaling. To comply with the increased storage or compute requirements, you need to purchase new hardware, which may result in the need to tune or replace current software.




  • Scalability. The inherent agility of cloud data warehouses allows upscaling and downscaling with no impact on enterprise data warehouse performance.
  • Reduced costs. There are no hardware-related costs (hardware acquisition, deployment, maintenance, administration, etc.). And if you opt for Enterprise Data Warehouse as a Service, all software acquisition and maintenance costs are eliminated too.

See the points of caution


  • Data compliance.  Although most cloud providers have security features that make it difficult for attackers to penetrate the environment, some organizations (e.g., in healthcare, finance, insurance, public sector, and legal services) choose to store their sensitive data on-premises to meet industry regulations.
  • The risk of budget overruns. Unexpectedly increased query volumes, which require additional compute/storage resources, lead to overspending if no controlling or limiting the cloud resources is set up.




  • Cloud flexibility. Meeting storage and compute requirements with near-unlimited cloud resources.
  • Data compliance. Ensuring sensitive data is stored within the environment which fully meets data compliance standards.

See the points of caution


  • DWH costs. The company has to cover the maintenance costs and operating expenses of the on-premises DWH system while still paying the subscription fee for cloud DWH services.


Implement an Enterprise Data Warehouse with Experts

ScienceSoft's data analysts, solution architects, developers, and compliance consultants are ready to develop an enterprise data warehouse that will smoothly fit your infrastructure and help you reach your unique business goals.

Enterprise Data Warehouse Key Features

With experience in 30+ domains, ScienceSoft delivers DWH solutions tailored to specific industry needs and unique business goals of our customers. From our experience, the following features of an enterprise data warehouse make up a secure and efficient solution.

Data integration and management

  • Creating enterprise data warehouse models.
  • Data integration with ETL/ELT.
  • Full and incremental data extraction/load.
  • Structured, semi-structured, unstructured data ingestion.
  • Big data ingestion.
  • Streaming data ingestion.
  • Data loading and querying using SQL.
  • Subject-oriented data repository.
  • Time-variant (data from the historical point of view) data repository.
  • Nonvolatile (read-only) data repository.
  • Granular data storage.
  • Metadata storage.
  • Storage in multiple environments (cloud, on-premises, hybrid).

Database performance

  • Scalability.
  • Automated DWH maintenance tasks – backups, replication, patching, etc.
  • Advanced data searching (materialized view support, data indexes, result-caching, etc.).

Security and compliance

  • Data encryption.
  • Securing data access with user authentication and authorization.
  • Granular access control (row- and column-level).
  • Compliance with national, regional, and industry-specific regulations (for example, GDPR, HIPAA, PCI DSS).

ScienceSoft’s Head of Data Analytics with 12+ years of experience

To build a future-proof EDW with all the necessary features, it’s crucial to create data models that not only reflect current business operations but can be easily modified in response to your business growth. Keeping data models’ flexibility in mind, ScienceSoft’s solution architects always work together with seasoned industry experts to envisage potential business changes and efficiently organize enterprise data access, collection, storage, analysis, security, etc.

Key EDW Integrations

Recommended Enterprise Data Warehouse integrations

Ensures cost-efficient storage of raw data in its initial format. We often implement tiered raw data storage for further cost optimization, e.g., keeping frequently accessed data in a high-performing, costlier tier and storing less ‘popular’ data in a lower-cost, lower-performance tier.

Self-service analytics software

Your team members can generate the reports they need within minutes instead of waiting days for IT or data analysts to create them. Having access to accurate and up-to-date data at the required angle, they can make informed decisions.

Machine learning software

Your data scientists get access to vast amounts of reliable data to build highly accurate models for forecasts, process automation, smart recommendations, and more.

ScienceSoft’s Head of Data Analytics with 12+ years of experience

I believe one of the reasons ScienceSoft has many happy customers is our focus on trying to see a bigger picture rather than solving discrete tasks. We don’t consider an EDW as a stand-alone component. Instead, we plan and implement integrations that will help our clients improve the work of their teams and connect the dots among disparate business processes, different technologies, and cross-department initiatives. Such an approach takes effort, including writing custom code and building APIs. However, the result is always worthwhile, as it has a decades-long positive impact on business growth and profitability.

Highlights of ScienceSoft's EDW Portfolio

Development Costs and Timelines

The cost of enterprise data warehouse development may vary from $70,000 to $1,000,000+*, depending on software complexity. 

$70,000 – $200,000

For a simple solution with up to 5 data sources, basic data management, and rule-based analytics of structured data.

$200,000 – $400,000

For a medium complexity solution that encompasses up to 15 data sources, features advanced data management and real-time analytics. It enables both rule-based and ML powered insights driven from all types of data.

$400,000 – $1,000,000

For an advanced, maximally automated solution that supports real-time analytics (incl. big data) and ML/AI-powered scenario modeling across all the integrated data sources.

*Monthly software license fee and other regular fees are NOT included.

Based on ScienceSoft’s experience in EDW software implementation, the approximate timeframes for an EDW implementation project are from 3 to 12 months, and the cost of an enterprise data warehouse implementation project may vary as follows:

Ballpark timelines for each stage of EDW implementation

A typical ScienceSoft's project on EDW software implementation covers the following stages and timelines:

  • EDW goals elicitation: 3-20 days.
  • EDW solution conceptualization and tech stack selection: 2-15 days.
  • Business case and project roadmap creation: 2-15 days.
  • System analysis and EDW architecture design: from 15 days.
  • EDW solution development and stabilization: from 2 months.
  • EDW solution launch: from 2 days.
  • After-launch support, maintenance, and evolution: as requested.


Estimate the Cost of Data Warehouse Implementation

Please answer a few questions about your business needs to help our experts estimate your service cost quicker.


*What type of data does your organization primarily deal with?

*What is your data volume?


If you don’t know the data size in TB, describe it as the number of data records: e.g., orders, payments, cases, customer interactions, sensor readings.

*What data volume growth do you expect during the next 12 months?

How many users will use the DWH?


The number of users and their nature help to estimate the read load on the DWH.

*What is the share of users who will use your DWH daily?


Different user groups may have different use frequency. If you know these details, please provide them in the box below.

*Please describe the data sources for your DWH. Check all that apply.

*Should your DWH offer complex analytics?

*How promptly should changes in source data be reflected in the DWH?

*Do you have any preferences for the environment?

*Do you have any tech stack preferences, incl. cloud platforms?

Do you already have a DWH you want to migrate data from?

*Are there any compliance requirements for your DWH? Check all that apply.

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Our team is on it!

ScienceSoft's experts will study your case and get back to you with the details within 24 hours.

Our team is on it!

Enterprise Data Warehouse Benefits

  • Up to 10% revenue increase

    due to clear understanding of previous situations and status quo, opportunities, trends, and risks

  • Up to 30% more productive analytics teams

    due to self-service reporting

  • Up to 60% lower costs of operations

    due to automated data management

Looking for Professional EDW Services?

We develop enterprise data warehouse solutions since 2005. Our team of data analytics professionals with 12–27 years of experience and in-house compliance experts ensures your EDW is future-proof, high-performing, scalable, and secure. Having established project management practices, we drive projects to their goals regardless of time and budget constraints and changing requirements.

EDW consulting

Looking for expert assistance? ScienceSoft’s team will work out a robust EDW implementation strategy and design an efficient solution architecture. We will equip you with a detailed project roadmap and actionable recommendations for each project stage.

Request consultation

End-to-end EDW implementation

ScienceSoft’s engineers will dive into your enterprise data analytics needs, design an optimal feature set and architecture, then build and comprehensively test your EDW. After the solution is deployed and integrated with the required systems, we are ready to stay with you for its long-term support and evolution.

Request implementation

ScienceSoft as a Reliable EDW Implementation 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. After a series of interviews, ScienceSoft’s consultants analyzed our workloads, documentation, and the existing infrastructure and provided us with a clear project roadmap. They stayed in daily contact with us, which allowed us to adjust the scope of works promptly and implement new requirements on the fly. Additionally, the team delivered demos every other week so that we could be sure that the system aligned with our business needs.

Solutions ScienceSoft Recommends

The selected platforms are recognized leaders in enterprise data warehousing solutions (The Forrester Wave, Gartner Magic Quadrant), which are fully compliant with the key criteria for an enterprise-scale DWH: almost instant scalability of compute and storage resources (due to the cloud-based nature), high performance and availability (up to 99.99% uptime), advanced security, etc.

Azure Synapse Analytics


A scalable data warehousing solution with a node-based architecture which employs parallel query processing to achieve fast query response time and high query throughput. Azure Synapse unifies the Azure Data Lake storage and the SQL data warehouse to allow direct querying of raw data and combining relational and non-relational data for deeper analytics insight.


Data storage - $23 per TB/month ($0.04/1 TB/hour).The data storage encompasses the size of your DWH and 7 days of incremental snapshot storage.

Learn Azure Synapse costs for your case with Pricing Calculator

Data security

Dynamic data masking, built-in authentication, authorization, data encryption, etc.

Amazon Redshift


A scalable data warehousing service which achieves great performance due to such features as massively parallel processing, columnar data storage, query optimizer, result caching, etc. With the Redshift Spectrum feature it is possible to query data directly from Amazon to enable data lake analytics.


The price is charged according to the amount of stored data and the number of nodes. The on-demand pricing option starts from $0.25/hour (hourly rate based on the type and number of nodes in the cluster).

Learn Amazon Redshift costs for your case with Pricing Calculator

Data security

End-to-end encryption, granular access controls, network isolation, etc.

Google BigQuery


A scalable data warehousing solution backed up with the Dremel technology designed to instantly run queries on massive structured datasets.


Storage costs: $0.02/GB/mo ($0.01/GB/month for long-term storage).

Streaming inserts: $0.01/200 MB.

For query performance, 2 subscription options are available:

  • Pay-as-you-go ($5/TB, 1st TB/month is free).
  • Flat-rate pricing (from $10,000/ month for a dedicated reservation of 500 processing units).

Data security

Data encryption, Google’s virtual private cloud policy controls, etc.

Common Questions about Enterprise Data Warehouse, Answered

How do you build an enterprise data warehouse?

Building an enterprise data warehouse involves a feasibility study, business needs and requirements elicitation, optimal toolset selection, architecture design, project roadmap creation, solution development, and launch. If you’re interested in a detailed description of DWH implementation steps, you can read our dedicated guide.

Why should an organization implement enterprise data warehousing?

Implementing EDW allows organizations to improve their operational efficiency and gain a competitive advantage through dynamic decision-making driven by accurate, up-to-date data.

How do you ensure compliance with the regulatory standards our EDW requires?

We have in-house compliance experts that can guarantee your solution fully adheres to any required global and local regulations, including HIPAA, GDPR, FDA, MDR, PCI DSS, ADHICS, and more.

What is the difference between an enterprise DWH and a DWH?

The difference between an EDW and a data warehouse is in scope. An enterprise data warehouse supports company-wide decision-making and deals with data from the entire organization, including all the internal and external sources, departments, and business units. A DWH can be limited to handling department-specific analytics.

About ScienceSoft

ScienceSoft is a global IT consulting and IT service company headquartered in McKinney, TX, US. Since 2005, we render data warehouse consulting services to support our clients’ agile and data-based decision-making. Being ISO 27001-certified, ScienceSoft guarantees cooperation with us does not pose any risks to our customers' data security.