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26 April 2024
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Published by
BluePi
Data-Driven Business Transformation
How to Choose the Right Data Warehouse Architecture for Your Business Needs
A basic idea of data warehouse architecture concepts
Today, technology plays a large part in business activity, and companies are overwhelmed with the flow of data from different directions. Data activities, like data management and analysis, become the core basis and factor of success.
Data management requires organizations to deploy an effective and dynamic data warehouse architecture to make good use of the data.
Unfortunately, as there are currently an abundance of ends from which one can select, determining the right architecture among them could be an uncomfortable task. In this in-depth guide, we will look at the various types of data warehouse architecture and offer you tips on choosing the one that works for you, depending on your business needs.
Earthquake Trend of Present: Data Warehouse Architecture
Through the years of its use, the traditional architecture of a data warehouse, referred to as the Enterprise Data Warehouse (EDW) or the Centralized Data Warehouse, has become one of the most common methods for storing large amounts of data shared among multiple different departments.
In this model, data from different sources is pulled into a single source repository using extraction, transformation, and load (ETL) techniques, which are usually run on a relational database (RDBMS).
The data warehouse situated at the center becomes the central source of all the information being collected, so there is nothing to worry about about the issue of inconsistency of reports and analysis taking place between departments.
Pros
- It maximizes the utility of pooling data from diverse data sources.
- Automates the data quality processes and even does the complex data transformation.
- Allows the organization of data for extremely quick retrieval and report generation.
Cons
- An expensive venture that includes initial outlays, recurring charges, and frequent upgrades.
- Its ability to handle exponential data volumes as they grow can be strained. However, improvements in distributed data storage technologies are being developed to help mitigate this challenge.
- The likely information bottlenecks that the concurrent queries would trigger and the real-time data handling are considerable problems.
One form of data mart architecture uses data from multiple source systems that can be accessed and combined to enable easy and focused analysis.
On one hand, the Data Mart methodology is considered a great alternative to the traditional data warehousing approach.
In this case, the warehouse is divided into small cells; each cell is not a single place; there is more than one where the cube is divided into individual cells that correspond to business functions or departments.
Data marts, while essentially being part of the largest data warehouse, are the subset of the warehouse that contains data related to only a specific niche or subject area.
Pros:
- Focuses on specific business requirements and user needs
- Offers better performance and faster query response times
- Allows for easier implementation and management
Cons:
- Can lead to data silos and inconsistencies across the organization
- Requires additional effort to ensure data integrity and consistency
- May result in redundant data storage and processing
Hub and Spoke Architecture
Combined in the Hub-and-Spoke scalable architecture, there is an application of both traditional data warehouse and data mart modes of operation.
In this pattern, the centralized data hub functions as a main repository for data integration coming from different sources of data.
Thus, between the hub, where the data is gathered, and the multiple data marts, which can be seen as spokes that are designed to serve different business or analytical needs,.
Pros:
- It brings together all the information in a single database, but this does not preclude the development of data warehouses for each department.
- Can grow to match the volume of the data and include extra data sources.
- Well established data governance and the ability to share the data across the organization.
Cons:
- It demands a well-made plan and a high level of cooperation between the central government and the rural areas that provide services.
- It is complicated to build and run at any given time.
- It can result in probable performance footfall for the cluster at the data hub.
Cloud-Aware Data Warehouse Design Infrastructure
The more prevalent cloud architecture is, the more cloud-based data warehouses organizations are adopting. This means that the infrastructure itself is run and owned by, for example, Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform.
This architecture comprises scalability, elasticity, and consumption-oriented models of pricing, which is considered to be its advantage for enterprises regardless of size.
Pros:
- Highly scalable and elastic features that allow for adjusting the size and the detail level, depending on incoming data volumes.
- Low upfront cost of infrastructure as well as reduced spending for maintenance in the long run.
- Adds adaptive analytics and machine learning functionality. Write down from critical thinking perspective what you agree or disagree with the given statements
Cons:
- Data safety and other laws may emerge due to the cloud company provider.
- Diamond planned the data migration and integration strategy necessary.
- It may send these costs over the top for important and performance-oriented jobs.
Picking the Most Viable Data Warehouse Architectures
Choosing the perfect data warehouse architecture is a very important decision. It should be based fundamentally on the particular needs of your organization (data volumes, some analytical functions, future growth plans, etc.).
Consider the following factors when evaluating the available options: Consider the following factors when evaluating the available options:
Data sources and volume:
produce a list of the number and types of data sources that exist, as well as how the volumes of data are expected to increase over time.
Analytical requirements:
Enumerate the variations in analytic specialties across different business units or departments like reporting, dashboarding, and advanced analytics.
Performance and concurrency:
Construct a framework that will stand behind the needs of the system and the data warehouse that will be accessed by several users simultaneously.
Scalability and flexibility:
Analyze the necessity of scalability to be able to support data growth in the future and the possibility of changing the business model to meet changing demands.
Cost and resource considerations:
Evaluate the installation and operation costs, along with the capabilities of the workforce in the setup and maintenance of the technologies employed.
Balancing all these factors and the different pros and cons of each architecture is a decision- making process that will eventually lead to a data strategy and vision the organization needs.
Conclusion:
Selecting an intelligent software architecture for your data warehouse is the first step toward providing your management team with intelligent data for effective decision-making. Data is often collected through centralized collection methods, distributed data marts, hub-and-spoke-style data collection, or cloud-based data storage options.
The important thing, though, is to select the option that matches your business goals, data sizes, need for analytics, and other unique specifications.
Through thoughtful assessment of the prospects as well as the aspects, e.g., scalability, performance, and cost, you will be able to adjust your data warehouse architecture so that it will support your organization’s growth and achievement in the era of such huge data floods.
About the Author
Published by
BluePi
Data-Driven Business Transformation
Published by
Divya Dass
A data-driven solutions architect, leverages his expertise in data science, data lake management, data warehousing, and cloud CDPs to lead impactful data projects across diverse domains. A skilled communicator and collaborator, Divya translates data insights into actionable business strategies, continuously evolving and optimizing data-driven operations within the company.
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