Data Warehouse Architecture Explained: How It Drives the Success of Data Management and Processing?  

  • Last updated on: July 10, 2024
Leveraging the full potential of data is paramount in today’s digital landscape to derive valuable insights and make informed decisions. At the core of this objective lies the need to implement a robust data management system to streamline the collection, storage, and utilization of data. This is where a data warehouse comes into the picture. 
Several components come together to make a data warehouse work according to business requirements. To make these components work efficiently and derive the expected value out of the data warehouse, enterprises must have a well-designed data warehouse architecture in place.
In this blog post, we will go over what a data warehouse architecture is, the layers that make up the architecture, the types of data warehouse architecture, and the best practices for a good data warehouse architecture. 

Table of Contents

What is Data Warehouse Architecture?

A data warehouse architecture defines the design of the data warehouse that helps streamline the collection, storage, and utilization of data gathered from disparate sources for analytical purposes. A well-developed data warehouse architecture decides the efficiency of collecting raw data and transforming it to make it valuable for business processes. 

Layers of a Data Warehouse Architecture

While there can be various layers in a data warehouse architecture, there are a few standard ones that are responsible for the efficient functioning of the data warehouse software. These include: 
  • Source Layer
The source layer is where the different types of data from various sources such as CRM, ERP, or even spreadsheets are collected and subsequently sent to other layers.  
  • Staging Layer
From the source layer, the data moves to the staging layer where it is temporarily stored, organized, and goes through several quality checks. This is popularly called the Extract, Transform, and Load (ETL) process and is done to ensure the data processing is efficient.  
  • Warehouse Layer
The warehouse layer or the core layer is where the processed data from the staging layer is stored and optimized for analysis. This layer leverages a standardized data structure to move the data into different tables.  
  • Presentation Layer
The presentation layer (also known as the consumption layer) is where analysts and other users can access the data. It allows users to leverage queries and tools to extract insights, generate reports, and make informed decisions. This layer consists of data marts and data interfaces that present data related to a specific subject area to a particular group of users in the required format. 

Types of Data Warehouse Architecture

Data Warehouse Architectures come in three forms. Each architecture has its pros and cons, and the decision to choose the right architecture depends on the type or size of the business. Here are the three data warehouse architectures: 

1. Single-tier Data Warehouse Architecture

A single-tier data warehouse architecture comes with the source layer, warehouse layer, and analysis layer. Typically, it is used by small businesses looking to store a limited amount of data and eliminate duplicate data. It may not help businesses that deal with massive volumes of data and that have advanced or real-time data processing requirements. Additionally, in a single-tier architecture, analytical processing (e.g. Generating a report) and transactional processing (e.g. recording purchases made by a customer) happen together in the same system, which causes performance issues.  

Single tier data warehouse architecture
2. Two-tier Data Warehouse Architecture 
The two-tier architecture has a staging layer in addition to the source layer. The staging layer temporarily stores data gathered from various sources for cleaning and transforming it into a predefined format before ingesting it into the warehouse layer and subsequently into the analysis layer. In a two-tier architecture, a separation is established between analytical processing and transactional processing. While this data warehouse architecture provides more value than a single-tier architecture, it comes with scalability issues, making it unsuitable for large businesses. 
3. Three-tier Data Warehouse Architecture 

The three-tier data warehouse architecture is the solution to the problems imposed by the single-tier and two-tier architectures and the widely used architecture for a data warehouse system. The data flows across three tiers: 

  • Bottom Tier (Data Warehouse Layer) is where the data is stored, cleansed, and transformed to ensure consistency and quality. 
  • Middle Tier (Reconciled Layer) organizes the data to make it available for analysis. The OLAP (Online Analytical Processing) server does this, and the data is presented in a simple and standardized format, making it easy for users to understand and interact with. 
  • Top Tier (Front-end Client Layer) enables users to extract insights from the data in the warehouse through reporting and data visualization tools. 

Best Practices for Data Warehouse Architecture

To derive the expected value from a data warehouse, it is important to follow some industry best practices for data warehouse architecture. Here are some of these best practices: 
Best Practices for Data Warehouse Architecture
  • Adopt a Single Design Approach 
Organizations must maintain consistency when it comes to designing a data warehouse. They should either choose a top-down approach (defining the overall structure on a high level and breaking it down into more detailed levels) or a bottom-up approach (beginning with the detailed levels and gradually building up) to maintain clarity throughout the design process. 
  • Automate Data Cleansing 
By automating the cumbersome repetitive tasks such as data validation, standardization, and deduplication, businesses can accelerate the loading process while maintaining data quality. They can use ETL tools to cleanse the data of any errors, inconsistencies, or duplicates. This also reduces the manual effort.  
  • Ensure Proper Data Integration 
The data collected from multiple sources should be combined in such a way that it maintains data integrity and consistency in the data warehouse. Businesses can follow the Third Normal Form (3NF) normalization principles to organize data logically and efficiently, reducing data redundancy and improving data quality. 
  • Automate Maintenance Processes and Leverage Cloud 

Employing machine learning to automate the maintenance processes can significantly transform the data warehouse management tasks such as resource allocation, system monitoring, and others. Additionally, organizations should consider leveraging cloud data warehouses to reap the benefits of scalability, cost savings, and accessibility, thereby transforming data management capabilities.

  • Optimize Data Warehouse Models 

Organizations should prioritize designing the data warehouse models in a way that makes it easy to retrieve information efficiently. For example, Dimensional Data Modeling organizes data into clear categories for easy understanding, while de-normalized modeling combines related data into fewer tables to reduce redundancy. Sometimes, a combination of these approaches can also be used to meet specific business requirements. 

Join Hands with a Strategic Partner to Transform Your Data Warehouse Journey

Establishing a robust data warehouse architecture is crucial for organizations looking to take full advantage of their data. By choosing the right type of architecture based on specific business needs and embracing industry best practices, organizations can streamline the data collection, storage, processing, and utilization processes, enabling informed decision-making and gaining a competitive advantage.  
KANINI, a trusted digital transformation partner, helps enterprises efficiently establish a well-defined data warehouse architecture, maximize the value of their data, and become future-ready. Reach out to us to learn more. 
Author

Soundar Vetrivel
Soundar is a results-driven professional with 16+ years of diverse experience in Data Analytics and Project Management. Currently spearheading data warehouse projects at KANINI, Soundar is known for his forward-thinking approach, delivering value to our clients. His expertise extends to managing enterprise architecture processes, data management programs, and creating innovative business solutions powered by advanced analytics.
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