Data Warehousing involves the construction, and integration of data from different sources and consequently querying and other analytics of data. Data Warehousing has become an important aspect for all businesses and upcoming startups to be able to deal with their data efficiently while also ensuring it remains safe and free from infiltrations. Data Warehouse Design is essential in understanding the underlying components and the working of a Data Warehouse. The query processing integration with Data Warehouses is widely dependent on its architecture and design functionality.
In this article, you will explore the different functioning components of the Data Warehouse Architecture, Data Warehouse Design, the types of Data Warehouses, and the typical design characteristics in order to understand the complete functioning of Data Warehouses.
Table of Contents
What is Data Warehouse Architecture?
The Data Warehouse Architecture can be understood as a three-tier structure with inter-connected functionalities. Each layer or tier serves a different role and integrates different tools/operations with the Data Warehouse system. The primary components of the Data Warehouse Architecture can be understood as follows:
1) Client Layer: Query and ETL Tools
The top tier in the Data Warehouse Architecture is the front-end or client layer of the system. This includes ETL and query processing tools and different platform integrations for data mining, reporting, and analytics. It primarily involves interfaces that connect to the Data Warehouse to extract data.
2) OLAP Servers: Multidimensional Operations
The middle tier consists of OLAP servers that facilitate multi-dimensional operations. These operate under OLAP functionalities based on the concepts of a Multidimensional Database. This is the application layer of the system that acts as an intermediary between the client and the Database.
3) Data Warehouse Servers: Relational Database System
The bottom tier of the Data Warehouse is that which consists of Databases and the metadata repository of the Data Warehouse. The metadata is used for data building, maintenance, and management within the Data Warehouse. It plays a very significant role in defining the values, source, usage, and other specifics of data in the warehouse. This is also the layer where all Data Marts and other specific use classifications operate for the monitoring and administration of data. Data is sorted, transformed, and loaded with the help of this back-end interface that primarily functions like a Relational Database system.
The below-illustrated chart represents how these three tiers function in tandem with each other with data being sourced from different operational Databases as well as external sources:
Image Source: https://panoply.io/data-warehouse-guide/data-warehouse-architecture-traditional-vs-cloud/
More information regarding Data Warehouse Architecture can be found here.
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What are the Types of Data Warehouse Designs?
Data warehouses can be of different types depending on how their functions are implemented. The three functional types of Data Warehouses are observed as follows:
1) Data Mart
Image Source: https://crbtech.in/need-data-mart
A Data Mart is actually a subset of a Data Warehouse that is dedicated to a specific business. Data Marts can be recognized as individual Data Warehouses each aligned for a specific function within the organization. These deliver separate singular functions that can either be pertaining to a specific line in the business or perhaps a specific department that operates independently. A Data Mart helps eliminate excess data sets to focus on critical points and accumulate insights faster and more efficiently than you would within the entire Data Warehouse.
More information about Data Marts can be found here.
2) Enterprise Data Warehouse
Image Source: https://key2consulting.com/concepts-of-an-enterprise-data-warehouse-edw-what-is-an-edw/
Enterprises Data Warehouses are essentially collections of Databases that allow businesses to store large chunks of data within specific parameters. It allows businesses to store data in ways that would make sense, and consequently, use the classified data for analytics and insights generation within the organization. Enterprise Data Warehouses enable brands to draw data from even unrelated sources and refine them to improve the quality of data and convert them into forms useful for the business.
More information about Enterprise Data Warehouse can be found here.
3) Operational Data Store
Image Source: https://www.linkedin.com/pulse/ods-operational-data-store-still-thing-can-lakes-replace-amit-soni
An Operational Data Store is a Central Database that presents information about all relevant and latest datasets from different transactional systems. It takes data from multiple production systems, loosely integrates it, and incorporates time variance and non-volatility factors. Thus, businesses can easily combine data from different sources into a singular destination for the purpose of reporting and analysis while still retaining the data in the original format.
More information regarding Operational Data Store can be found here.
What are the Data Warehouse Design Characteristics?
The design characteristics of a Data Warehouse are owing to the functional metrics more than the mechanism. Your Data Warehouse must be designed to fit the use that your business expects to operate with. This can be in terms of the functions carried out, sources it incorporates or excludes, or perhaps cater to different possibilities of errors and redundancies.
Some Data Warehouse Design characteristics can be understood as follows:
1) Non-Volatile Data Warehouse Design
Non-volatility is important for any Data Warehouse which refers to the preservation of all versions of old data even when new additions are made to the Data Warehouse’s Database. Thus, data remains read-only and can be retained in a secure environment without the possibility of any concurrent losses. All datasets remain for future reference and are not removed when new data is added to the Database. This can be an important design characteristic for a Data Warehouse for the robust handling of your business data.
2) Unification Centric Data Warehouse Design
Data unification refers to the combination of data from different sources that are integrated for a collective insight/accumulation process. This ensures that all insights from the cloud, mainframe, relational and Non-Relational Databases are considered before any conclusions can be drawn in data analytics. The element of unification in Data Warehouse Design ensures the most accurate and reliable insights for the effective analysis of your business data.
3) Function Centric Data Warehouse Design
A specific function or theme is often set for in the implementation aspects of different Data Warehouses. For instance, certain Data Warehouses are focused on the purpose of sales, while others would handle transactional material, etc. A function or theme-centric Data Warehouse ensures that Business Intelligence elements are incorporated in the analysis and decision-making process of your business data. This process considers only the factors that would be instrumental for the specific decision and leaves out other information to keep the process precise and efficient.
4) Time-Variant Data Warehouse Design
A time-variant Data Warehouse or Design susceptible to time variance is actually an important factor that ensures some valuable analytical gains which would otherwise not be possible. The data that is accumulated in the Data Warehouse over the period of time remains identified with that time and can be later used to generate time-specific insights. This also ensures that a thorough corroboration can be made in case of any alterations or unauthorized modification since the data remains time identifiable.
Conclusion
Thus, Data Warehousing and Design functionalities can play a major role in how your Data Warehouse setup can function to accommodate the requirements of your business. While what types of Data Warehouse you choose will definitely depend on your use case, the design characteristics can be specific to the features that are more in use or can make the system more robust and efficient for analytics.
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