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.
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. Its implementation costs can range from $70,000 for the most basic solution to $400,000 to cover the needs of a midsize enterprise. The costs of an EDWH for a large enterprise comprising multiple data sources and advanced analytics capabilities will be higher. Enterprise data warehouses integrate with a data lake, ML and BI software.
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 Data Warehouse Architecture
An enterprise data warehouse is a core element of a BI solution, which is structured in accordance with the particular data management and analytics needs a company pursues. Below, ScienceSoft's experts provide components of EDW architecture that are common for our projects:
- A data source layer - an enterprise data warehouse pulls data from apps’ databases, enterprise systems (e.g., CRM, ERP, document management software, HRM), and external sources (e.g., social media, government reports, chosen stock market trackers).
- A staging area - an intermediate storage area of temporary nature for data processing under the extract, transform, and load (ETL) process. ETL consolidates data from multiple sources and transforms it into a modeled format suitable for storing in the enterprise DWH. Cloud-based EDWs, due to their scalability, use ELT (extract, load, transform), which means that the transformation step is performed after data loading into the EDWH.
- Data storage layer - centralized storage where data is made accessible for analytics (querying, reporting) and sharing.
- Analytics and BI - data in the enterprise data warehouse can be queried via OLAP tools, data mining tools, reporting and visualization tools.
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.
Enterprise Data Warehouse Key Features
For each project, ScienceSoft creates a tailored feature set that perfectly suits our customers’ business needs. 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).
- 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).
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
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.
EDW Implementation Success Stories by ScienceSoft
End-to-End BI Solution Development for the Producer of Phytotherapy Products
- Design and implementation of a cloud EDW based on Azure SQL Server to integrate data from multiple enterprise apps (e.g., ERP, CRM, SCM, manufacturing execution system).
- Near real-time analytics with Power BI; 20+ different report templates for sales, production, product quality management, and other departments.
Result: Efficient company-wide reporting that brings transparency to the entire product life cycle and helps the company client to improve its internal processes and operations.
Retail Analytics Solution for a Multinational FMCG Corporation
- A three-module BI system that enables advanced sales analytics on 100 SKUs being sold via marketing channel of 10 large retail chains and 10,500 stores.
- Analytics engine for querying sales data from an OLAP cube and calculating specific KPIs (e.g., sales growth in a particular store over a time span).
Result: Boosted sales analytics helping the company identify its sales trends, find out the most promising SKUs, estimate growth potential, and optimize sales and marketing activities.
BI Solution for 200 Healthcare Centers
- Development of Microsoft SQL Server enterprise data warehouse that uses a Transact-SQL script for loading data from 200 databases.
- JReport tool to build multiple reports on medication classification, missed medications, hospital readmissions, employee performance reporting, community marketing activity summary.
Result: Prompt analytics and reporting allowing healthcare centers to streamline their management processes.
Financial Reporting with BI for an International Real Estate Developer
- ETL pipelines and a data warehouse aggregating data from around 40 sources.
- Analytical cube with 15 dimensions and 30 measures enabling to analyze enterprise’s income, net operating expenses, net profit, operating cash flow, and other indicators.
- Integrated financial and analytical reports and dashboards based on Power BI.
Result: Comprehensive financial analysis that helped the real estate developer improve its decision-making. better understand its business.
Development of an EDW and an Analytics Solution for a Multibusiness Corporation
- Over 90 reports for different business directions and user roles.
Result: A 360-degree customer view, optimized stock management, and easy assessment of the employees’ performance.
BI Platform for a Regulatory Authority
- BI solution for financial market analysis that can support 200+ concurrent business users over 500 reports at once.
- Elaborate user access control, permission matrix based on row and column level for advanced security of sensitive data.
Result: Easy monitoring and management of enterprise data for the company to conduct in-depth financial analysis, automate data flows, build custom analytics models.
Development Costs and Timelines
$70,000 – $200,000*
For companies with 200 – 500 employees.
$200,000 – $400,000*
For companies with 500 – 1,000 employees.
$400,000 – $1,000,000*
For companies with more than 1,000 employees.
*Monthly software license fee and other regular fees are NOT included.
The major factors that influence the cost of the enterprise data warehouse implementation project are:
- The number and diversity of data sources, the complexity of data flows.
- The volume of the data to be ingested and stored by the solution.
- An EDW’s performance, availability, scalability requirements.
- The target data quality thresholds (consistency, accuracy, completeness, etc.) to be achieved within the data transformation processes.
- The data sensitivity and data security requirements.
Based on ScienceSoft’s experience in EDW software implementation, the approximate timeframes for the 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.
Enterprise Data Warehouse Benefits
With all business users having access to a single point of truth, EDW eliminates data silos, enables easy and secure data sharing, and guarantees that all users make decisions based on common information.
Saved time of IT staff and data analysts
Thanks to self-service BI capabilities and data management automation, an EDW minimizes the involvement of IT teams in BI tasks and significantly reduces the manual work of analytics teams.
A 360-degree view
The role of an enterprise warehouse in data mining is essential. An EDW consolidates and stores massive data from various sources, allowing companies to get a comprehensive view of their business at the current point, from the perspective of the past years, and at an angle of intelligent predictions and what-if scenarios.
A solid foundation for implementing AI/ML analytics
Providing vast amounts of multi-source, high-quality data, an EDW makes it possible to train precise AI/ML models that enable accurate predictions, smart recommendations, and business process automation.
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.
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.
Heather Owen Nigl, Chief Financial Officer, Alta Resources
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
Dynamic data masking, built-in authentication, authorization, data encryption, etc.
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
End-to-end encryption, granular access controls, network isolation, etc.
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 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.
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.