Data warehouse architecture is the technique to define the entire architecture of the data communication presentation and processing, which is present for the potential clients who are computing in the business organization.
Every data warehouse is different. However, all of them are characterized with the aid of different standard vital components.
Production applications like inventory control, payable product purchasing, payroll accounts are meant to execute the OLTP or online transaction processing.
These kinds of applications are responsible for the collection of detailed data for daily operations. The data warehouse applications are meant to support different user ad-hoc data needs.
Production databases are updated constantly with the use of hand or through OLTP applications.
The data warehouse is updated periodically from different operational systems, primarily during the off-houses. Since the OLTP data is collected into the production databases, it is filtered and extracted regularly after being loaded within the dedicated warehouse, accessible to different users.
Since the warehouse gets populated, it should be restructuring the tables. The new keys, fields are added, and the data are cleansed of different redundancies and errors for reflecting the different user requirements to combine, sort, and summarize the data. The data warehouse and the architecture differ, which depend on the situation of the business enterprise.
The three common architecture of the data warehouse, needed for Data Warehouse consulting Services are:
The Basic
- The one with the Staging Area.
- The one with Staging Area and Data Marts.
Table of Contents
Basic Architecture of the Data Warehouse
Operational System
It contributes to being a technique, which is used in data warehousing. It refers to the system, which is used on a wide scale for processing different daily transactions of the business enterprise.
Flat Files
It contributes to being the system of different files in which the transactional data gets stored. Here, each file of the system includes a unique name.
Meta Data
It contributes to being the set of the data, which defines and offers information about the other kinds of data. Here, the metadata is used for a plethora of objectives.
The MetaData is known to provide a summary of the prerequisite information about the data. It helps in finding and working with the specific instances of data, thereby making it more accessible. It is used for directing the query for the proper data source.
Highly and lightly summarized data
It is another area of the specific data warehouse that saves the highly summarized and predefined lightly data that the warehouse manager produces.
The ultimate objective of the summarized information is to boost the query performance. Here, the summarized record gets updated constantly as new information gets uploaded into the data warehouse.
End-user access tools
The ultimate objective of the data warehouse is to offer information to different business managers to make strategic decisions for the business. Here, the customers interact with the warehouse with the aid of different end client access tools.
Some of the primary instances of the end-use access tools include application development tools, reporting, and query tools, online analytical processing tools, executive information system tools, and data mining tools.
With staging Area
It is a prerequisite to clean and process the operational details before being loaded into the data warehouse.
It is possible to achieve this programmatically. Here, the data warehouse makes the right use of the staging area.
It simplifies the data consolidation and cleansing for the operational method derived from different source systems, primarily for the business data warehouses in which the business organization’s relevant data gets consolidated.
The Data Warehouse’s staging area refers to a temporary location in which the record from different source systems will be copied.
With Data Marts and Staging Area
If you want to customize the warehouse architecture for several groups present in the organization, you can do with the addition of the data marts.
It is recognized as the segment of different data warehouses that offer the prerequisite information for the reporting and analysis of the department, unit, section, and operation within the business enterprise.
Property of Data Warehouse Architecture
The below-mentioned properties are essential for the data warehouse system:
Separation
The transactional and analytical processing needs to be kept apart.
Scalability
Here the software and hardware architectures should be simple. It helps in upgrading the data volume, which should be processed and managed. It is essential to upgrade the total number of user needs, which enhance progressively.
Extensibility
The architecture should be capable of executing different technologies and operations without the need to redesign the whole system.
Security
It is essential to track the accesses due to the strategic data stored within the data warehouses.
Administrability
It will help if you keep in mind that the data warehouse management should not be too complicated.
Ask for professional advice from Modula US, since they provide a wide range of advanced warehouse management and analytics solutions.
Kinds of Data Warehouse architecture
Primarily, there are three different kinds of data warehouse architecture, which are referred to as single-tier architecture, two-tier architecture, and three-tier architecture.
Single-tier architecture
The single-tier architecture’s ultimate objective is to reduce the amount of data stored for reaching the specific objective.
It is useful in removing all types of data redundancies. In this specific technique, the data warehouses are known to be virtual. It indicates that the data warehouse gets implemented in the form of the multidimensional operational data view, which is generated by the certain middleware.
The vulnerability of such a kind of architecture is in its failure to accomplish the separation between the transaction and analytical processing.
Two-tier architecture
This two-tier data warehouse architecture helps in highlighting the separation between the data warehouses and the physically available resources. It comprises four data flow phases: the Data staging, source layer, analysis, and the Data Warehouse layer.
Three-tier architecture
The three-tier data warehouse architecture comprises the reconciled layer, source layer, and the data warehouse layer. Such type of architecture is beneficial for enterprise-wide systems.
Summary
Data warehouse is a critical part of the information system that comprises the commutative and historical data from various resources. Such sources can be Cloud Data Warehouse, regular Data Warehouse, or the Virtual Data Warehouse.
