Data warehouse architecture is a complex information system that contains commutative and historical data from different sources. In other words, it is the structural representation of the concrete functional arrangement on which the enterprise data warehouse is constructed.
Enterprise data warehouses and their architecture vary depending on the elements of the organization’s situation. The three most common architectures include the basic, staging area, and the combination of the staging area and data marts.
In this guide, we’ll help enterprises understand the complete architecture, types, and best practices related to cloud data warehouses in detail.
Characteristics Of Data Warehouse
Data virtualization and warehouse are often used interchangeably; however, they are different from each other. The process of data warehousing involves the extraction and electronic storage of data for ad-hoc reporting and queries. On the contrary, data virtualization means accessing, managing, and retrieving critical business data. Another aspect of data virtualization is that it does not collect or duplicate the data in a physical repository.
Before we explain the three main types of data warehouse architecture, here are the key data warehousing characteristics.
A data warehouse is subject-oriented as its purpose is to render information regarding the theme rather than the company’s ongoing business operations.
The subjects, in this case, can be anything from sales and marketing to distribution. One of the main purposes of a data warehouse is to focus on data modeling and analysis to make informed decisions.
All the similar data from the different databases are integrated into a standard unit of measure. The data stored in the warehouse is collected from disparate sources like relational databases, flat files, mainframes, etc.
The data warehouse has an extensive time horizon than operational systems. This is because all the data stored in the warehouse is recognized within a particular period. Another unique aspect of a data warehouse is that once the information is inserted, it can’t be changed or updated.
The non-volatile nature of data warehouses means that previous data is not erased, whereas only new information is inserted into it. The historical data is analyzed to help you understand what and when the changes happened.
Types Of Data Warehouse Architecture
The global data warehouse market is expected to cross $51.18 billion by 2028, implying companies prefer storing their data in a single source of truth. But before you choose any data warehouse, it’s vital to understand its architecture. It generally consists of three tiers:
Single Tier Architecture
A single-tier data warehouse architecture aims to minimize the amount of information stored. Its objective is to remove data redundancy. However, it is not frequently used.
Two-layer architecture aims to separate physically available sources and data warehouses. However, this is not expandable and has connectivity problems due to network limitations.
One of the widely used modern data warehouse architectures is a three-layer structure.
- Top Tier: Top tier comprises the client-side front-end of architecture. This tier uses all the transformed and logically applied information for different business processes.
- Middle Tier: The OLAP (Online Analytical Processing) server is implemented using either MOLAP (Multidimensional Online Analytical Processing) or ROLAP (Relational Online Analytical Processing) model. The layer acts as the mediator between the database and the end user.
- Bottom Tier: In this layer, the data is cleansed, transformed, and loaded using the back-end tools.
A modern data warehouse can store both structured and unstructured volumes of data. If you want to integrate a data storing solution or migrate data from a traditional database to the cloud, contact the experts of Inferenz today.
Data Warehouse Architecture Best Practices
Below are some best practices you’ll need to follow to design the data warehouse architecture.
- Follow the top-down and bottom-up approaches to design a data warehouse.
- Ensure that the data is processed accurately and quickly when consolidated into a single version of the truth.
- Thoroughly develop the complete data acquisition and cleansing process for the data warehouse.
- Design metadata architecture that eases metadata sharing between different data warehouse components.
- Consider the 3NF data model to ensure that the data model is integrated and not only consolidated.
Manage Your Data Better With Inferenz Experts
Different types of data warehouses store, centralize, and query large volumes of data from multiple sources. In the data warehouse structure, the metadata plays a crucial role. It determines the source, values, features, and use of data. Only a well-designed data warehouse is the foundation of a successful BI or analytics program. It will help you improve data quality, speed up data retrieval and analysis, and enhance overall decision-making.
Many companies are increasingly choosing modern data warehouses and cloud data warehouses. If you want to integrate the modern data warehouse solution into your business, contact the Inferenz data experts today. The professionals will help you improve decision-making and bottom-line performance by understanding the data warehouse architecture.
What is OLAP in data warehousing?
In simple terms, OLAP is software that performs multidimensional analysis at high speeds from a data mart, warehouse, or other centralized data storage.
What are the 4 components of a data warehouse?
The components of a data warehouse system are a central database, metadata, access tools, and ETL (extract, transform, load) tools.
What is the main purpose of a data warehouse?
The data warehouse resembles a central data repository that can be analyzed to make more informed business decisions. Data flows from relational databases, transactional systems, and other sources to the data warehouse to help analysts use the right information for internal business operations.