Data Warehouse Design: Top 6 Factors for Selection

Data Warehouse Design: Top 6 Factors for Selection | Data Science and Analytics | Emeritus

Data, one of the most critical assets of any organization, needs to be strategically stored and analyzed. To do so, an increasing number of companies are creating data warehouses. Simply put, these warehouses are centralized data repositories that include structured and semi-structured data collected from multiple departments within the organization. A data warehouse comprises critical data that helps organizations perform data analysis and make strategic decisions. Therefore, a data warehouse should be highly organized to execute queries and analyze data. Hence, the setup or architecture of a data warehouse or data warehouse design plays a crucial role for organizations.

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What are the Key Factors to Consider When Selecting a Data Warehouse Design?

Data is undoubtedly essential for organizational growth in the technological era. However, it needs to be structured and interpreted in real-time to generate valuable insights. This requires selecting the appropriate data warehouse design. The following are some factors for data warehouse design considerations:

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1. Business Goals or Requirements 

The first factor to consider in data warehouse architecture is to assess the business requirements and analytical needs of the business.

2. Architecture and Platform

Data warehouse architecture tops the list of factors to consider in data warehouse design. For example, if a business analyzes real-time data, it needs four or five layers in a data warehouse architecture. However, if a business needs to store data for a longer period, a single-layer architecture, which comprises only the source late, can work.

3. Data Sources

The next factor in choosing data warehouse design is the complexity of data sources. The data warehouse design should be able to easily source, segregate, and maintain data from different sources.

4. Data Integration

This is one of the most important factors to consider in data warehouse design. It involves checking the quality and consistency of data that will be stored in the warehouse, as well as data transformation requirements.

5. Scalability

While planning a data warehouse design, an organization needs to ensure that the design is scalable to accommodate high volumes of data and adapt to changing user demands.

6. Data Model and Schema

Data warehouse design considerations also require finding the most suitable data model based on the complexity of business data.

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What are the Different Types of Data Warehouse Designs Available?

The most critical part of data warehouse design is choosing data warehouse model which compiles relevant data into an efficient structure. The following are the most commonly used data warehouse models:

1. Star Schema

This is a relational database model in which data is organized into a central fact table. It includes numerical information related to events such as stock balances, sales orders, etc. The fact table is connected to several dimension tables that include descriptive data.

2. Snowflake Schema

This is also a multidimensional model similar to the star schema. In the snowflake schema, dimensional tables are further broken down into logical subdimensions. Simply put, the snowflake schema comprises normalized data.

3. Data Vault

Another popular data warehouse design modeling technique for enterprises, the data vault includes three types of entities. These are hubs representing core business concepts, links acting as the bridge between hubs and satellites, and satellites comprising information related to various hubs.

4. Constellation Schema

Constellation or Galaxy schema comprises several fact tables that have some common dimensional tables. It is so called because it is arranged into the form of several star schemas connected together.

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How Can I Determine the Scalability and Performance of a Data Warehouse Design?

One of the critical factors in assessing the efficiency of data warehouse design and data warehouse implementation is evaluating the scalability and performance of data design. Here is how data warehouse implementation can be carried out:

  • Set Key Performance Indicators (KPIs) such as query response time, ability to handle data overload, and user support
  • To test scalability, increase the data load, and monitor the model’s performance following the increased workload
  • Run concurrent queries to assess how the model will perform if multiple users access the platform simultaneously

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What are the Best Practices for Implementing a Data Warehouse Design?

The following are some of the best practices for data warehouse design that businesses use to leverage data analytics and optimize processes:

1. Cloud Strategy 

Designing a data warehouse on a multi-cloud or hybrid cloud platform enables scalability and easy access to data. It also facilitates real-time data analytics.

2. ETL Process

Efficient Extract, Transform, and Load (ETL) processes are essential for data accuracy and reliability. This practice involves identifying various data sources, defining data transformation rules, and preparing data loading sequences.

3. Real-Time Data Integration

Incorporating real-time data collection and analytics tools ensures consistency as changes are immediately reflected in the data warehouse.

4. Data Governance and Cataloging 

Using effective data governance and cataloging policies ensures data consistency across the organization. Moreover, it improves data accuracy.

4. Metadata Management 

Metadata provides information about the data stored in the warehouse. Effective metadata management involves using a standard model to define data lineage and usage. It helps users understand data context.

5. Indexing 

Effective indexing is one of the best practices for data warehouse design. It involves labeling to map the volume of columns to their physical location. It enhances query performance.

How Does the Choice of Data Warehouse Design Impact Data Analytics Capabilities?

A well-designed data warehouse selection serves three main purposes—business intelligence, data reporting and analytics, and adhering to regulatory requirements. Here is how data warehouse design impacts data analytics capabilities:

1. Query Performance

An efficiently designed data warehouse architecture can boost query performance by enabling techniques such as indexing, partitioning, query tuning, and query optimization.

2. Quality of Insights Generated

Data warehouse design influences the quality and relevance of data insights generated from the data warehouse. A well-functioning data warehouse design provides reliable and secure data, facilitating accurate data analysis. Ultimately, it plays a critical role in helping organizations make informed, data-driven decisions.

3. Data Retrieval

The design also impacts data retrieval. It ultimately influences how users can perform ad hoc data queries and implement various analytical tools to generate data dashboards and compile reports.

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Sneha is a content marketing professional with over four years of experience in helping brands achieve their marketing goals. She crafts research-based, engaging content, making sure to showcase a bit of her creative side in every piece she writes. Sneha spends most of her time writing, reading, or drinking coffee. You will often find her practicing headstands or inversions to clear her mind.
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