The data warehouse architecture has been the backbone of enterprise data ecosystems for some decades now.
As more companies have to process more data than ever before to personalize the customer experience, cloud computing and predictive analysis have been more developed.
Information technologies are also more employed, and data warehouses have become more in use.
Today, there are better ways of storing, analyzing, and indexing data, but the significance of data warehousing in data storage can not be overemphasized.
The Benefits of Data Warehouse
A data warehouse is a storage facility where historical and commutative data from different sources are kept.
The warehouse streamlines enterprise reporting and business intelligence processes.
For example, instead of manual processing of transactions, a data warehouse would take off the task and acts as a relational database, capable of performing queries and analysis.
A data warehouse basically stores historical transaction data, but it can also keep data from other sources as I mentioned recently.
It distinguishes analytical capacity from transactional capacity and enables businesses to synergize data extracted from different sources.
This will help businesses to maintain historical records, evaluate data from time to time, for a better understanding and improvement of business processes.
In addition to a relational database, a data warehouse design can include numerical analysis, extract, transform, and load (ETL), reporting, and data mining.
It can also include other applications that help to collate and transform data into valuable information.
This will be easily delivered to the business analysts and the management of the business.
Features of Data Warehouse Design
The following are the main features of data warehouse design:
- It is Theme-oriented
A data warehouse design uses a specific theme. The warehouse stores information about the theme and not your transaction details.
Data Warehousing doesn’t focus on your business transactions, but rather than the Business Intelligence (BI).
It helps to display and analyzes your business data for better decision making.
The data warehouse also provides a simple and concise interpretation of the subject matter by removing the information which may not be important in a particular decision making.
- There should be Harmony
A data warehouse design unifies and integrates all analog data from different databases in a collectively acceptable manner using data modeling.
It processes data from different sources such as flat files, relational and non-relational databases, mainframes, cloud-based storage, etc.
More so, a data warehouse should have a common nomenclature, layout, and coding to give room for effective data analysis.
- Data preservation
Unlike some other OS, data warehouse stores data over a longer period of time and can not be altered.
Each block of data collected is identified with a certain period of time, thus, provides insights from the past.
The data that is entered in the warehouse can not be restructured or changed.
- Non-volatility of Data
Another important feature of the data warehouse is its non-volatility. What this means is that previous data don’t change each time more data is stored in the system.
Also, the data is read-only and can be temporarily updated to provide the user with a complete and updated picture.