Since the invention of the first data warehouse in the late 1980s, it has become an essential tool for ensuring data integration within a company. We explore what a data warehouse is, what types of data warehouse exist and how it differs from other databases.
The first enterprise data warehouse was created in the late 1980s by IBM researchers Paul Murphy and Barry Devlin. Since then, data warehousing has become an essential business process for the smooth running of any organization.
It is not that Murphy and Delvin invented databases, but they did lead the way in the development of an enterprise data warehouse specifically designed to meet companies’ information and BI needs. They achieved their goal by creating an architecture based on a data flow that went from the corporate operating systems to decision support environments.
In this sense, one could say that the concept ‘data warehouse’ is the union between data repositories and data-driven decisions.
In this blog we have previously discussed the importance of data integration for informed business decision making. Without a proper data integration architecture, it is impossible to transform data into information and information into insights.
This is why the vast majority of companies rely on data warehousing as the organization’s central database. In fact, according to a recent study by Global Market Insights, the data warehousing market is expected to exceed US$30 billion by 2025.
What is a data warehouse?
In essence, a data warehouse is a structured database that integrates all actionable business data, extracting it from different corporate data sources and unifying it in a single environment. However, its characteristics, approach and structure make it different from a traditional database.
As already mentioned, data warehouses have been linked to data-driven decisions since their origins. A data warehouse is specially designed to meet companies’ business intelligence and data analytics needs. That is why it is also often called Enterprise Data Warehouse (EDW).
More specifically, we can define a data warehouse as a type of data storage and data integration architecture that facilitates the organization, transformation, understanding and management of data, as well as its later exploitation to make better business decisions. In fact, the creation and development of this type of architecture, as well as the operations involved, are known as data warehousing, which refers to the process of collecting, integrating and organizing data in a data warehouse.
Unlike other databases, the main objective of a data warehouse is to promote and streamline the transformation of ‘raw data’ into business insights, as well as to facilitate its access by business users.
How does a data warehouse work?
A data warehouse is usually the central data repository of an organization. Thus, once the data has been extracted from its original sources and integrated into the data warehouse, it is processed, transformed and organized into views and dimension or fact tables. The most common methodology is the ETL process —Extract, Transform and Load— or, more recently, ELT —Extract, Load, and Transform—.
Once the data has been transformed and organized, users can access it through SQL, business intelligence tools such as Power BI, customer management platforms such as a CRM, etc.
What is the difference between a data warehouse and other types of databases?
The main difference between a data warehouse and other types of databases is its architecture, which allows the order and understanding of the data, preparing them for their exploitation.
The structure of a data warehouse allows data sets to be organized by themes. Thus, once the data has been integrated into the data warehouse, administrators can organize the data according to their preferences, structuring the information according to the business needs of the organization.
Another fundamental feature of a data warehouse is that it has the capacity to respond to complex queries.
On the other hand, unlike operational databases (ODS), a data warehouse often acts as an organization’s central data warehouse where all useful company data is collected, including data that will not be used immediately. While other databases store specific data sets for a particular operation or for a specific business unit, a data warehouse acts as the organization’s single source of truth.
Differences between a data warehouse and data mart
It is important to not confuse a data warehouse with a data mart. A data mart is a small part of a data warehouse or a subset within the data warehouse that stores data linked to a specific business unit or operation.
Differenes between a data warehouse and a data lake
Finally, we must also differentiate a data warehouse from a data lake. The most basic differences between the two are the format of the data they store and their approach.
A data warehouse is a relational database. That is, it only stores structured data, while a data lake integrates any type of data, whether structured, semi-structured or unstructured.
In terms of approach, the data stored in a data warehouse has been previously modeled and structured, so that, once in the data warehouse, it is ready to be used. This process is known as schema-on-write. In contrast, raw data is usually loaded into a data lake and, when it is to be used, it is shaped and prepared (schema-on-read).
What is the architecture of a data warehouse?
As we have already mentioned, the great distinctive feature of a data warehouse is its architecture, which is structured in different layers that interact with each other and, in turn, with the data.
The classic architecture of a data warehouse is structured in 3 layers:
1. Bronze: In the Bronze layer —also called Staging layer, among other names—, the data is extracted from the original data sources —usually by SQL scripts—.
2. Silver: In the Silver layer —also called Core—, data from the different sources is integrated into the data warehouse. Once stored, the data is transformed, modeled —usually in star or snowflake schemas— and transferred to an online analytical processing (OLAP) server. Once the transfer is complete, the data is transformed and loaded into the data warehouse, where it will be available for further analysis and use in decision making.
These two initial layers are usually carried out through an ETL process: extraction, transformation and loading of the data.
3. Gold: In the Gold layer, data is prepared for user consumption. It is organized in such a way that it is ready to be used and exported in business intelligence, reporting and data visualization platforms such as Power BI or other front-end interfaces.
What are the advantages of a data warehouse over other databases?
Today, any type of data-related business activity is based on data integration. If an organization’s data is not integrated, its exploitation will be extremely complicated and probably not very productive.
As we have already explained, a data warehouse is the basic tool of any data integration process and the central data warehouse of an organization. In this sense, the data warehouse has become a key part of the business intelligence and analytical systems of any company.
Currently, having a data warehouse means having a single source of truth that is consolidated and validated. Beyond the fundamental role of the data warehouse in generating business intelligence, the structure of a data warehouse facilitates the work of data experts in ensuring the accuracy, integrity and quality of data, avoiding duplication and inconsistency of corporate information.
Other major benefits of a data warehouse over other databases are:
- Data historification: A data warehouse is a data repository with the ability to store large amounts of data in an organized manner. This allows companies to store long-term historical data for retrospective and evolutionary analysis.
- Data processing and transformation: Data stored in a data warehouse goes through cleansing and transformation processes to ensure accuracy, consistency and standardization in terms of structure and format. This improves data quality and facilitates data analysis.
- Improved overall system performance: Through efficient data collection and analysis, data warehouses reduce query times and improve overall system performance.
- Efficient data loading: Data warehouses offer efficient data loading without facing the high costs associated with other implementation methods or infrastructure.
- Data security: One of the advantages of a data warehouse is that it facilitates the implementation of security measures to ensure data privacy and confidentiality: access controls, data encryption, etc.
In brief, a data warehouse offers a number of advantages over other databases that promote the preparation of data for further analysis and transformation into value. These advantages, in turn, contribute to more informed and efficient decision making.
Cloud Data Warehouse or on-premise data warehouse?
More and more companies are choosing to store their data in cloud data warehouses for a number of reasons. Among the most significant reasons are greater speed, more scalability and a much lower initial investment, as well as significant savings in maintenance costs.
Cloud data warehouses —which can be public or private— does not only provide greater agility, but also allow the adoption of new data flows or types of analysis that reinvent the classic concept of data warehousing.
On the other hand, cloud data warehouses can increase the speed of queries and transformations by taking advantage of massively parallel processing (MPP).ç
Moreover, like any technology, data warehousing is also evolving and most cloud data warehouse vendors already consider scalability as a basic requirement.
At this time, having a data warehouse is essential to implement an efficient data integration process that allows businesses to transform their data into better business decisions without associated risks.
In order to transform data into value, data must go through a series of processes that, without a data warehouse, can become very complicated.
In short, transactional databases are not capable of performing the same functions as a data warehouse when it comes to generating business intelligence.