Largely in the shadows, data warehouse architecture has been the pillar of corporate data ecosystems over many decades. Now, despite the advancements in the field of Big Data and the massive potential that AI has showcased, data warehouses are even more integral than ever before.
 
Yes, there exist multiple possibilities for storing, analyzing, and indexing data. But one can not disregard the importance of data warehousing and the benefits it offers those involved in the business.
 
The question still exists: What is data architecture and why is it even important? Well, today you will get the answer to those very questions!

Introducing Data Warehouse

Think of it as an entity that streamlines the reporting and BI processes of businesses. It acts as a relational database through which one could not only analyze data but also configure queries based on particular incidents/specifics. While the former could serve through the usage of transactional databases but you would fall short of having any analytics. This is what data warehouses provide.
 

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

Data warehouses also provide users with an Extract, Transform, and Load (ETL) tool which helps streamline data from various sources and provide cumulative analysis that is concise and accurate.
 
It also extracts data that increases the potential for reporting capabilities, data mining abilities, and various other avenues. They are related to the collection, conversion, and conveyance of data to business analysts or any other users.
 
Through the inculcation of a data warehouse template, you would be able to assess business needs, goals, and any/all technical aspects. Which are required to build, plan, and operate the data warehouse. What’s even more interesting is the opportunity it provides users to change/transform data by unique business needs.

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.

Data Mart

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.

Unified data

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

Unlike other systems, data warehouses have the capability to store data from a wider time horizon. For example, it can gather data from various timelines despite the variance that might exist between them.
 
What this means is that when the insights are inline, they can provide supporting data from the past which helps create standardized and better-structured data points.

Secured data

This can be under the banner of non-volatility which means that data warehouses have the ability to save older data. there is no need to remove any preceding data to process new data.
 

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.

Theme-focused

Gathering data is important but what is even more important is the need to structure and align data by specific business goals/targets. Data warehouses provide information about particular departments/themes like sales, marketing, etc. which helps gather specific data which in turn contributes to the better potential for streamlined analysis.
 
By moving away from focusing on business operations/transactions, data warehouses focus on BI (business intelligence) which is primarily the display of data that is pertinent to decision making. By focusing on specifics, the data warehouse offers an outlook that is concise and focused on goals/targets. They are useful for the user rather than offering overwhelming data that holds no value for the organization.

Components Of Data warehouse

An integral component of data warehouses is the metadata that structures a framework for the data being collected. This helps to not only construct data points but also preserve and handle the workings of the data warehouse.
 
This process helps to classify the data into two types:
  • 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:

Load manager:

This is known as the front component. It performs all operations about the extraction and loading of data into the warehouse.

Warehouse manager:

Responsible to perform operations such as the analysis of data for consistency, creating indexes, and transforming/merging source data.

Query manager:

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

These refer to tools about data reporting, query tools, application development tools, EIS tools, and OLAP or data mining tools.

Conclusion:

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.