Introducing Data Warehouse
Through the usage of a data warehouse, companies can include data from both historical transactions and external sources. Its analysis is different i.e. it allows organizations to gather data from numerous sources and evaluate/analyze it to enhance the operations and increase efficiency.
Extract, Transform, and Load (ETL) Tool
Types of Data Warehouses Architecture
Let us now turn our attention towards discovering the 3 main types of Data Warehouses that are available.
Enterprise Data Warehouse
Think of it as a centralized warehouse that provides support services across the board. It boasts a unified approach for the organization and representation of data. Moreover, providing the ability to classify data according to the subject and its particular divisions.
Operational Data Store
Also called ODS, these are nothing but data stores utilized in the absence of OLTP systems that fail to support the organization’s needs. The unique factor is its ability to refresh data in real-time which makes it a great tool for routine activities like the storing of employee records.
A subset of data warehouses. It is specifically available for particular lines of business such as sales or finances. With an independent data market, the user has the opportunity to collect data directly from the source without having to form links or outlining sources.
Characteristics of Data Warehouse Architecture
Let’s now dive into the make-up of data warehouses to better understand its design and functionalities.
Data warehouses are famous for their ability to integrate data from varying databases to provide a collective report that helps model data efficiently. By incorporating data from diverse sources, it helps to provide a cumulative report/study for the business. This all happens while the data warehouse maintains a consistent framework which contributes to effective analysis of data.
Collect data over a period of time
Through this, users can view a complete outlook that outlines past trends and helps businesses to understand the whole process in a more comprehensive manner.
Components Of Data warehouse
- technical metadata
- business metadata
The former provides information that is useful for developers and managers and helps them outline warehouse development/administrative tasks. The latter is a more concise form. The data shared is understandable for everyone and anyone wishing to learn more about the data being stored in the warehouse. Other components include:
This is known as the front component. It performs all operations about the extraction and loading of data into the warehouse.
Responsible to perform operations such as the analysis of data for consistency, creating indexes, and transforming/merging source data.
This is also called the backend component, it performs all operations related to user questions/queries and provides users the opportunity to schedule the execution of queries.
End-user access tools
The data warehouse architecture topic hoped to outline the makeup of data warehouses and provide a concise introduction to the architecture that has set up the foundation for many businesses all across the globe. There is much more to explore in the world of data warehouses.