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Introduction
There are a lot of data management techniques available. One of them is warehouse data management. It is a one of its kind data management technique with the help of which the user can help them support business intelligence type of activities. Like the one known as business analytics. Such data has been comprised of large data which is usually in the form of queries and analyses being done on these and a large amount of historical data has been present here to play with. One thing that should have been considered here is that the data warehouse shouldn’t be confused with the conventional database. In the database, the data has been organized in the form of storage, accessibility, and retrieval, which means the data has been stored in some place that can be accessed and can be processed at any time in the database. Databases are basically time-dependent. While talking about the data warehouse, it is basically a kind of a database that works on the integration of the data transactions from a source system, and by this source, it got the provision to work different analytics on them. The intelligent business techniques may have been applied to them for the use in future. (What Is a Data Warehouse: Overview, Concepts and How It Works, n.d.)
Features of Data Warehouse
There are different parameters and features involved in the data warehouse. The main important features that can be observed in the data warehouse have been shortlisted in this subsection. With the help of these features, the link between the data warehouse with the database has been determined. By reading this section, a brief introduction to the difference between database and data warehouse can be observed. Following are the features of the data warehouse. (Database vs. Data Warehouse: A Comparative Review, n.d.)
While talking about data warehouse, it is a subject-oriented kind of technique. As in a data warehouse, a topic-wise discussion has been obtained rather than through a complete process of finding each and every detail from a single dataset. This can be demonstrated as if a person wants to know about the companies’ sales and profits only. With the help of a data warehouse, he or she can directly access the sales and profit of the whole dataset. In simpler words in a data warehouse, only information can be selected which has been useful for the user. That is the reason it is mostly used in business-related websites and databases.
The data that has been used in a data warehouse is integrated. In simple words, the data that has been stored in the data warehouse has been in a consistent format. The reason behind this consistency is by having the data in the form of a single format, it becomes easily accessible by the user when a single subject-based data is called. In this way, the data analysis becomes easy for the AI agent to process the data more rapidly and efficiently.
The data stored here is a non-volatile kind of data. The data that was once stored in such databases cannot be removed from here rather, they have left the history here in the form of transaction receipt. This will give the idea to the user that what will happen to that particular data. This all becomes possible due to reinforcement learning. This helps in better data analysis when required.
The data presented or stored inside a data warehouse are stored as a function of time. The time can affect the data explicitly or can be implicit. The time variance is an important factor in such databases as with the help of time data analytics can become easy.
Types of Datawarehouse
There are different types of data warehouses depending on the nature of the application, the size of the data, what the user wants, and what kind of analysis you want. For this purpose, different types of data warehouses have been shortlisted. A brief discussion on each type has been given as follows. (Data Warehouse – Overview, History, Types, How It Works, n.d.)
1. EDW or Enterprise Data Warehouse
When a user or a system required some decision-making kind of database EDW can provide such decision-making for them. EDW is basically a key for central databases. With the help of such databases, the user can access cross-organizational information as well using a single database. These kinds of databases are mostly used in organizations, industries, universities, and factories. Where any person can update a task and the team can update about the performances and task completion. Such a database has the ability to connect persons from different parts of the world. Oracle is one of the most common examples of such a data warehouse. (What Is a Data Warehouse? | Key Concepts | Amazon Web Services, n.d.)
2. Operational Data Source
A real-time-based data warehouse, which is updated or refreshed on the basis of time. The use of such databases can be observed where the employee record is an important factor. It can be used in such places where the reporting of the business has not been required by the user. (What Is a Data Warehouse? Definition, Concepts, and Tools | Talend, n.d.)
3. Data Mart
Another type of data warehouse with the help of which one can maintain a database for a particular department. This is mostly used in large businesses. There is always a central repository in such kind of database in which the data has been stored. The data that has been stored in this warehouse has been sent to the operational data source as in data mart we can not process or analyze the data. For analysis, we have required ODS to process our data and obtain fruitful results from the data that has been available in the database. (What Is a Data Warehouse: Overview, Concepts and How It Works, n.d.)
Final Words
Data warehouses are used to pull data from internal applications and organize or clean it as needed. Utilize the data gathered by means of these data warehouses with Algoscale to adjust production methods, conduct customer analyses, or even operations analyses. Algoscale, one of the fastest-growing big data engineering service providers, has in-depth practical knowledge of the top data engineering technologies available today. Let our experts take care of your data warehousing needs!
References
- Data Warehouse – Overview, History, Types, How It Works. (n.d.). Retrieved April 14, 2022, from https://corporatefinanceinstitute.com/resources/knowledge/data-analysis/data-warehouse/
- Database vs. Data Warehouse: A Comparative Review. (n.d.). Retrieved April 14, 2022, from https://www.healthcatalyst.com/insights/database-vs-data-warehouse-a-comparative-review/
- What Is a Data Warehouse: Overview, Concepts and How It Works. (n.d.). Retrieved April 14, 2022, from https://www.simplilearn.com/data-warehouse-article
- What is a Data Warehouse? | Key Concepts | Amazon Web Services. (n.d.). Retrieved April 14, 2022, from https://aws.amazon.com/data-warehouse/
- What is a Data Warehouse? Definition, Concepts, and Tools | Talend. (n.d.). Retrieved April 14, 2022, from https://www.talend.com/resources/what-is-data-warehouse/
