A data warehouse model refers to an architectural plan that is applied to define the structure and organization of a data warehouse more explicitly to enhance the ease of storage, retrievals and analysis of data. These models are critically important for sustaining high data volume rates in the warehouse and complex querying and reporting features. Let’s explore different types of data warehouse models in detail.
What is a Data Warehouse?
It is a centralized system of an organization where data is stored, arranged and brought from one or many source systems for analysis, development reporting and decision-making. This database provides an optimum platform for querying and signing and business intelligence or BI operations. In its full sense, a data warehouse is a system that collects data from different operating systems and external data sources and transforms it into more suitable structures.
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Types of Data Warehouse Models
- Enterprise Data Warehouse (EDW): An enterprise data warehouse is a centralised data depot that captures corporate data from distributed sources. It maintains an integrated perspective of organization data that can be used for analysis and making strategic decisions. Since EDWs come in handy with large-scale data storage and more advanced, complicated querying requirements. It gathers data from different applications like ERP and CRM and from outside, providing BI regarding corporate performance.
- Operational Data Store (ODS): Pure ODS is the data of an organization’s operations stored in the architecture like a data warehouse. The other difference between EDW and ODS is that EDW is a system of history while ODS is a current and real-time system. It is often carried out for operation intent, hence the name operational data store. This is true, especially when organizations deal with information such as client orders and information on stocks.
- Data Mart: A data mart could also be described as just a subset of a data warehouse for a given department, organizational unit, or group of users. It is structured to include only a subset of the data pertinent to that particular group or function.
- Cloud Data Warehouses: When a data warehouse hosts the data on the cloud computing such as Amazon Web Services( AWS). It brings a barrage of advantages for an organization. Data warehousing in the cloud results in higher scalability and reduced IT expenses on internal resources. Cloud-based companies optimize their databases using cloud-based data warehouses & scale up or down their systems as required.
- Big Data Warehouses: They offer the infrastructure for complex queries and advanced analytics on big data. These warehouses integrate high scalability and performance with data integration and processing tools. These platforms, like Hadoop, Amazon Redshift, and Snowflake, enable an organization to analyze data from various sources, which drives insights into the finance, healthcare, and e-commerce industries. Therefore, their ability to efficiently manage large-scale data makes them a critical requirement for big data applications.
Also Read: Key Components of Data Warehouse: An In-depth Guide
Importance of Data Models in Data Warehouse
- Importance of Data Quality: The data modelling ensures that the structure of the data warehouse promotes consistency, accuracy, and reliability. It defines clear relationships and constraints. This reduces errors and ensures the data is clean and trustworthy, which is critical for generating meaningful business insights.
- Efficient Data Retrieval: Organizing data into logical and optimized structures makes it easier and faster to retrieve information. This is crucial for businesses that rely on timely decision-making, as it minimizes query times and enhances the user experience.
- Scalability: The data model guarantees that the data warehouse can manage growing amounts of data without compromising performance. Scalability is essential in today’s data-driven employment, where the volume of generated data is increasing rapidly.
- Reduced Redundancy: Data Modelling eliminates unnecessary data duplication, reducing storage costs and simplifying data management processes. It helps maintain a leaner and more manageable data architecture.
Also Read: Exploring Advantages and Disadvantages of Data Warehouse
Comparison of Data Warehouse Models
| Type |
Best For |
Scalability |
Cost |
Complexity |
| EDW |
Large Enterprises |
High |
High |
High |
| ODS |
Operational reporting |
Medium |
Medium |
Medium |
| Data Mart |
Department specific needs |
Low |
Low |
Low |
| Cloud DW |
Flexible, scalable needs |
Very High |
Pay-as-you-go |
Medium |
| Big Data DW |
Large, varied datasets |
Very High |
High |
High |
| Virtual DW |
Distributed data sources |
Medium |
Low |
Medium |
| Hybrid DW |
Balancing security and scalability |
High |
Medium |
High |
| Real-Time DW |
Immediate insights |
High |
High |
High |
How Does Data Warehouse Work?
- Data Extraction (ETL Process): Information gathered from various source systems, such as transactional databases, external files, APIs, etc. It is then pre-processed to fit the record layout and dimensions needed in the data warehouse. This process comprises cleansing the data or information, filtering or processing the data or information, aggregating the data and information and applying business rules to the data or information. Finally, the transformed data is moved to the data warehouse, where it is stored.
- Data Storage: In star schema or snowflake schema, like the structure of a DW, structured information is stored in tables based on quantitative data and dimension tables containing descriptive information. The information related to history is kept in a format that will facilitate query and reporting.
- Data Integration: The remote data collection sources can be compiled into one homogenized, easily manageable format. This ensures that all data can be easily analyzed irrespective of the source because they are all from different systems.
- Data Querying and Analysis: DW are mostly suited for query and reporting operations. Heterogeneous data stored at multiple places and formats can be queried and analyzed using business intelligence (BI) tools or OLAP (Online Analytical Processing) cubes. The users can understand past performances, and new real-time reports and dashboards can also be set up.
- Data Refresh and Maintenance: Data in the warehouse is ever-changing and normally gets updated daily, weekly or monthly, depending on a business’s needs. This means that current data are obtained while historical data are used for trend analysis and period reports.
- Decision-Making: Managers and executives use this data to draw information, develop and prepare organizational reports and anticipate trends for managing change.
Let’s see the best data warehouse tools.
- Amazon Redshift: This is a fully managed, scalable cloud data warehouse service provided by AWS. It has excellent query performance and easily integrates with all the AWS services. It is the best for large-scale analytics.
- Snowflake: This cloud-native data warehouse provides great scalability, high performance, and easy usability. Snowflake is quite flexible and can be used to separate computing and storage, which is scalable in multi-cloud environments.
- Microsoft Azure Synapse Analytics: This unified analytics platform integrates big data and data warehousing capabilities. This offers powerful data integration, transformation, and analytics tools, making it ideal for Azure users.
- Teradata: Teradata offers enterprise-level data warehousing with powerful processing capabilities. It is well suited for large organizations with complex data needs.
- Cloudera Data Warehouse: A hybrid and multi-cloud data warehouse solution focusing more on secure, scalable data storage and processing. It becomes ideal for organizations that need big data management and advanced analytics in diverse environments.
- Informatica: It has robust data integration capabilities. Informatica offers data warehousing tools that ensure seamless data integration, transformation, and analytics, catering to enterprises of all sizes.
Conclusion
Consequently, for the effective functioning of an organization, there are several types of data warehouses: EDW, Data Marts, ODS, Cloud Data Warehouses, and Virtual Data Warehouses, depending on the scale, availability, and demands of reports. All the models have their benefits, including the integration in the centre, reporting in real-time, being cheaper, and developing at a great scale.
Knowledge of these types allows businesses to select the proper data storage and organization strategy and use it to support decision-making and remain competitive. There are varieties of DW models, and it is up to a particular organization, its data nature and the organizational resources available for this process to embrace a certain DW model, as mentioned above. The Certificate Program in DevOps and Cloud Engineering at Hero Vired teaches industry-aligned skills and builds capabilities in areas where innovation and efficiency are necessary. This hands-on program takes you forward in your career while leading the way for future success in data-driven applications. Enrol now!
FAQs
The hybrid data warehouse model contains features of both EDW and data marts, but it resembles the EDW more. This approach enables organizations to benefit from centralizing data for overall analysis and departmentalizing data for instant outcomes.
A data mart model is often more suitable because it focuses on specific departments or business areas, offering a more cost-effective and easier-to-implement solution.
Data in EDW, otherwise known as a central data warehouse, is extracted from source systems such as the Enterprise Resource Planning (ERP) system and the Customer Relationship Management (CRM) system, among others. It is then cleansed, transformed, and loaded into the central repository for analysis. Information is then retrieved from the EDW using business intelligence tools for reporting and analysis.
A data mart may be the basis of an enterprise data warehouse. As time passes, an organization’s data needs expand, and many data marts can be merged to form one full-fledged EDW.
Updated on December 16, 2024
