Are you looking for information on data warehouse architecture? Then, it would be best if you went through this post. Data warehousing is a popular approach to collect and combine data from more than one source.
It is an essential process of a business and store. These pieces of data contribute to further assessment and reporting. And all these aspects help to make informed business decisions. Most of the companies maintain a typical transactional database. Besides, it comprises of regular activities.
An organization also maintains various other sources of data. Some are internal operation related whereas some others are third party related.
Professionals collect and combine pieces of data from all such sources. Then, a data warehouse comes to the picture to store all the combined data. And the process used here is ELT (Extract/Load/Transform) or ETL (extraction, transformation, and loading).
The warehouse comes with a data model of a specific design. As a result, it becomes feasible to amalgamate data pieces from all such sources. It has streamlined making business decisions.
What Is Data Warehouse Architecture?
Big data warehouse architecture is complex by nature. Moreover, it has come as a great information system with different kinds of data pieces. However, there are multiple sources of data.
Three approaches are there to build a data warehouse:
Single Tier Approach
Two Tier Approach and
Three Tier Approach
It is time to explain all these three approaches below in brief:
What is the main goal of single-tier data warehouse architecture? It is to alleviate the volume of data storage. Moreover, it also helps to eliminate data redundancy. However, it is a rarely-used architecture.
Two-tier architecture comes with double layers. It divides a data warehouse and physically accessible sources. However, it is not possible to expand the two-layer architecture. Besides, it is not suitable for a long list of end-users. This architecture also features connectivity issues. And the reason lies in its network restrictions.
When it comes to the most used big data warehouse architecture, the three-tier comes up. This approach comprises three layers: bottom, middle, and top.
Let us explain the three layers in brief:
Bottom Tier: The bottom tier consists of the database of the respective data warehouse. It is often an RDBMS (relational database management system). Back-end tools contribute to data cleansing, transformation, and loading of data into the bottom tier.
Middle Tier: This tier of three-tier architecture comes as an OLAP (Online analytical processing) server. The MOLAP or ROLAP model contributes to implementing OLAP. The middle layer works as a middleman between the database and the end-user.
Top-Tier: It comes as a front-end client tier. The top tier contains API (Application programming interface) and the tools. A user can connect the same to extract data from the respective data warehouse. However, the tools can be of many types. It includes reporting tools, Data mining tools, Query tools, Analysis tools, etc.
What Are Data Warehouse Components?
RDBMS servers contribute to the data warehouses. This server works as a central information repository. Certain major components revolve around RDBMS. As a result, the data warehouse has become accessible, achievable, and functional.
Key Elements Of Data Warehouse
Data Warehouse Database
What is the base of the data warehousing structure? The answer is the central database. RDBMS technology executes the data warehouse database.
However, the data warehouse database comes with a constraint. And it is that conventional Relational DataBase Management Systems comes perfected for transactional database processing. There is no concept of data warehousing in this regard.
For example, the ad-hoc query consumes a lot of resources. As a result, it can reduce the speed of performance.
“Metadata” comprises of certain high-level technological approaches. But, the strategy is rather simple. You might know that metadata is simply data about specific data. And it leads to the definition of the data warehouse. It contributes to structuring, handling, and keeping up the data warehouse.
What is one of the key elements of data warehouse architecture? It is meta-data. Metadata mentions various aspects of data. It includes the usage, features, source, and so on regarding the data of a data warehouse.
Metadata also specifies the changing patterns and processing of data. Metadata and the data warehouse share a great connection.
Do you want to extract actionable briefs from the data? Then, it is a must to document the metadata. However, the metadata must hold a connection with many aspects. It includes staging tables, source tables, and so on.
Even it is also possible to trace the lineage of your data. Just you need to complete a proper design of the ETL tool. Some well-known ETL tools can monitor data lineage with the ace.
What is one of the main goals of data warehousing? It is to offer proper information to a business. As a result, it becomes possible to make informed business decisions.
Query tools contribute a lot in the data warehouse architecture. It allows communication between users and the data warehouse system.
Query tools are of four types:
Application Development tools
Query & reporting tools
Data mining tools
Many other tools are also there in data warehouse architecture.
Some Other Aspects
It is essential to keep the transactional database unconnected from the extraction tasks. And the best practice here is to execute the same on a replica table. As a result, you can ensure to keep the primary operational database’s performance intact.
It is also critical to keep track of the condition of the ETL/ELT process. Moreover, the proper configuration of alerts is also essential to make sure of credibility.
Another important aspect in this regard is logging. Unfortunately, many people do not emphasize the same. You should hold a centralized repository. Here, you can keep track of the logs. Moreover, it is also possible to make an analysis.
The central repository will further help to debug in less time. Also, it helps to make a solid ETL process.
Now, you are well familiar with data warehouse architecture and many of its associated factors.