Features of Data Warehouse Design and Types

Features of Data Warehouse Design and Types

A Data Warehouse (DWH) is a specialized system designed for the centralized storage, processing, and analysis of large volumes of structured information. Unlike operational databases, which are intended for daily company operations, data warehouses are used to accumulate historical information and prepare it for analysis.

The main feature of a DWH is that the data is organized according to a predefined architecture and processed using ETL (Extract, Transform, Load) processes. This ensures the integrity, consistency, and usability of the information for business decision-making.

Data warehouses provide:

• The ability to connect analytical tools
• High data processing speed
• Access to years of historical data

These features make data warehouses a key element in managing business operations and making strategic decisions.

Models and Types of Data Warehouses

Modern DWHs are divided into several types and models depending on their purpose and organization method.

Features of Cloud Data Storage

Cloud data storage is becoming increasingly popular due to its flexibility and cost-effectiveness. The main advantages of such solutions include:

• Scalability: Companies can increase or decrease resources based on their needs.
• Global Accessibility: With an internet connection, data is always available.
• Integration with Other Cloud Services: This simplifies the creation of complex data processing systems.

1. By Architecture:

• Single-tier: Minimalistic systems for storing small amounts of data.
• Multi-tier: More complex systems that include a data processing layer and data marts optimized for different types of queries.

2. By Location:

• On-premises Storage: Located in the company’s own data centers, providing full control over data.
• Cloud Storage: Operates over the internet, offering flexibility, scalability, and cost reductions for infrastructure.

3. By Function:

• Corporate DWH: Covers data from all company departments.
• Data Marts: More specialized systems focused on one aspect of the business.

Disadvantages:

Cloud storage has potential risks such as data leakage and reliance on internet connection quality. However, most cloud providers offer high security and data backup solutions.

How is Data Warehouse Design Performed?

Data warehouse design is a multi-step process that includes:

• • Analysis of Business Requirements: Determining the goals, tasks, and types of data that need to be stored.
  • Architecture Selection: Developing the structure (single-tier, multi-tier, etc.) based on data volumes and analytics needs.
  • Defining ETL Processes: Deciding how data will be extracted from sources, transformed, and loaded into the data warehouse.
  • Integration with Analytical Tools: Connecting BI systems, configuring data marts, and setting up reporting.
  • Testing and Optimization: Ensuring that all processes work reliably and meet business requirements.

This process requires deep knowledge of database design, data analysis, and business processes.

Differences Between DWH, Databases, Data Lakes, and Data Marts

• • DWH vs. Database: A database is designed for operational data storage, while a DWH is used for analytics.
  • DWH vs. Data Lake: A Data Lake stores both structured and unstructured data, while DWH works only with structured data.
  • DWH vs. Data Mart: A Data Mart is a specialized part of a DWH, optimized for specific tasks or departments.

When You Need Professional Help

Creating and maintaining a Data Warehouse (DWH) is a complex process that requires deep knowledge in IT, analytics, and data architecture design. Not every company has the internal resources and expertise to implement such projects. Therefore, in certain cases, it’s better to turn to professionals for assistance.

• • Increased Data Volume and System Overload. When a company faces exponential data growth, traditional database management systems often struggle to handle the load. This leads to decreased performance, slower query processing, and an inability to quickly access necessary analytical data. We help optimize existing infrastructure or develop a new system tailored to current volumes and growth rates.
  • Need for High Data Security. If a company processes sensitive data, such as financial information, customer personal data, or strategic business metrics, ensuring their protection is crucial. DBServ professionals can implement modern encryption, backup, and access control mechanisms that comply with legal requirements and industry standards.
  • Regular Analytics for Decision-Making. Businesses need timely access to analytical reports to assess the current situation, identify trends, and make informed strategic decisions. Our team can set up data collection, processing, and visualization processes, integrate BI tools, and create user-friendly data marts that allow quick access to necessary information.
  • Challenges with Integrating Different Data Sources. Companies often work with data from various systems — CRM, ERP, marketing platforms, manufacturing, and logistics systems. Each source has its own formats, standards, and characteristics, making it difficult to combine and analyze the data. DBServ professionals design and configure ETL processes that automate the extraction, transformation, and loading of data into a unified storage system.
  • Internal Resource Limitations. Even if a company understands the importance and necessity of a data warehouse, it may lack the internal resources to implement the project. This could be a shortage of time, specialists, or technical infrastructure. In such cases, our professional team can take on the entire process — from requirement analysis to system deployment and ongoing support.

By turning to DBServ specialists, you will not only minimize the risks associated with complex projects but also receive an efficient solution that will become a reliable tool for the growth of your business.

When Does a Company Need a Data Warehouse?

A Data Warehouse (DWH) should be implemented when:

1. 1. Handling Large Volumes of Data from Various Sources. If an organization collects data from different systems — CRM, ERP, marketing platforms, production databases — and the data volume continues to grow, a data warehouse helps consolidate it into a unified system for easy storage and processing.
   2. Automation of Analytics is Required. For companies that need to generate regular reports for performance evaluation, sales analysis, or planning, a DWH automates these processes, reducing the time spent on preparation and minimizing errors.
   3. Historical Data Preservation is Necessary. A data warehouse allows long-term storage of information, which is crucial for trend analysis, forecasting, and making strategic decisions.
   4. Integration of Different Systems is Needed. When data from various systems needs to be combined for subsequent analysis, a DWH provides the ideal solution by offering convenient mechanisms for data processing and visualization.

A DWH becomes a critical tool for companies striving to improve data quality and analytics while continuing their growth.

DBServ specializes in the design, implementation, and support of all types of data warehouses — from corporate to cloud-based solutions. We can help you choose the right architecture, set up data processing and integration workflows, and ensure the reliable operation of the system.

By reaching out to us, you’ll receive a professional solution tailored to meet your business’s needs.