A data warehouse (DW) is a type of digital storage system Its goal is to feed business intelligence (BI), reporting, and analytics, as well as support regulatory requirements so that businesses can turn data into insight and make smart, data-driven decisions. Data warehouses consolidate current and historical data and serve as an organization’s single source of truth. Here we are going to discuss and explore the data warehouse architecture, types of data warehouse architecture, and application of data warehouse architecture.
What is a Data Warehouse?
A data warehouse is a large and centralized repository of integrated data from multiple sources that have been optimized for querying and analysis. This data is then transformed, cleaned, and integrated into a common format that is optimized for analysis.
What is Data Warehouse Architecture?
Data warehouse architecture is the design and structure of a data warehouse, which is a centralized repository of data that is used for reporting and analysis. In simple words, we can say it is a collection of different data and a data warehouse architecture is a method of defining the overall architecture of data communication, processing, and presentation for end-client computing within the enterprise. Each data warehouse is unique, but they all share certain essential components.
The data warehouse architecture has been the pillar of corporate data ecosystems, and despite numerous changes in Big Data, cloud computing, predictive analysis, and information technologies over the last five years, data warehouses have only grown in importance.
Types of Data Warehouse Architecture
There are three types of data warehouse architecture
1) Single-Tier Data Warehouse Architecture
Single-tier data warehouse architecture, also known as basic data warehouse architecture, is a simple and straightforward approach to building a data warehouse. In this architecture, all the components required for data warehousing are installed on a single machine, and data is loaded from the operational systems directly into the data warehouse. The physical source layer, the virtual data warehouse, and the analysis layer, which may include reporting or OLAP tools, are all components. The data warehouse is virtual in this method of single-tier data warehouse architecture, which means that it is implemented as a multidimensional view of operational data, which is mostly created by specific middleware. It is also known as an intermediate processing layer, but it is not used on a regular basis in practice.
The goal of having only one physical source layer in a data warehouse architecture is to reduce the amount of data stored to achieve the goal, which eliminates data redundancies.
2) Two-Tier Data Warehouse Architecture
With the major disadvantage of single-tier architecture not having a separation of layers for analytical and transactional processing, the two-tier architecture of the data warehouse came into play. This two-tier architecture eliminates the disadvantage of the single-tier because it has a separation between the layers, which is essential in maintaining the two-tier architecture.
The two-tier data warehouse architecture comprises the following two tiers:
1. The Data Tier
This tier is responsible for storing and managing the data. It consists of the actual data warehouse database or databases, along with any associated data marts or data cubes. The data is stored in a structured format, usually a relational database
The Data Tier is a key component of a data warehousing architecture and typically consists of three main layers, as follows:
- The Source Layer: This layer is responsible for collecting data from various sources such as transactional systems, legacy systems, external sources, and other data repositories. The data in this layer is in its raw form and is often inconsistent, redundant, and incomplete. The source layer performs basic validation and cleansing of the data and prepares it for loading into the next layer.
- The Data Staging Layer: This layer is responsible for transforming and integrating the data from the source layer into a format that is optimized for loading into the data warehouse layer. The data staging layer performs data cleansing, data enrichment, data transformation, and data aggregation to create a consolidated and consistent view of the data. The data in this layer is often in a structured format and is optimized for loading into the data warehouse layer.
- The Data Warehouse Layer: This layer is responsible for storing the data in a format that is optimized for reporting and analysis. The data warehouse layer is designed to support complex data queries, analytics, and reporting. The data in this layer is often in a denormalized and aggregated format and is optimized for performance and scalability.
2. The Client’s Tier
The bottom tier of the architecture consists of the database of the Data warehouse servers. This database can be classified as a relational database system.
This layer is in charge of cleaning, transforming, and loading data from the bottom tier into the database using back-end tools. This layer is also known as the data warehouse layer (containing both data marts and data warehouses)
This tier is responsible for providing access to the data to end-users. The client tier may also include other types of applications that use the data warehouse as a data source, such as custom software applications or web-based portals.
This Client Tier is the front-end application that displays the clean data so that users/clients can begin their analysis. To display the required reports, the data warehouse reports are frequently hidden in the GUI.
The Client Tier is made up of a single layer known as the Analysis Layer.
- Analysis layer:
This is the fourth layer of the data warehouse’s two-tier architecture. The primary focus of this layer is the integration of data that is efficient and flexible enough to issue reports, simulate hypothetical business scenarios, and perform dynamic information analysis. The analysis layer of the data warehouse architecture includes aggregate information navigators and efficient query optimizers.
3) Three-Tier Data Warehouse Architecture
The three-tier data warehouse architecture is a more complex data warehousing architecture that consists of three main tiers, which are often referred to as:
- Bottom Tier: This tier is also known as the Data Source Layer or Operational Data Layer. It is responsible for collecting data from various sources, such as transactional systems, legacy systems, and external sources, and storing the raw data in its original format.
- Middle Tier: This tier is also known as the Data Integration Layer or Data Staging Layer. It is responsible for transforming and integrating the data from the bottom tier into a format that is optimized for loading into the top tier. This tier performs data cleansing, data enrichment, data transformation, and data aggregation to create a consolidated and consistent view of the data.
- Top Tier: This tier is also known as the Data Presentation Layer or Data Warehouse Layer. It is responsible for storing the data in a format that is optimized for reporting and analysis. The top tier is designed to support complex data queries, analytics, and reporting. The data in this tier is often in a denormalized and aggregated format, and is optimized for performance and scalability.
The three-tier data warehouse architecture provides greater flexibility and scalability than the two-tier architecture, allowing for more complex data integration and analytics capabilities.
Properties of Data Warehouse Architecture
The properties of data warehouse architecture that you have listed are generally accurate, although they are not an exhaustive list.
- Security: The architecture of a data warehouse should include security measures to protect the confidentiality, integrity, and availability of data. This includes access controls, data encryption, and audit trails.
- Administerability: The architecture of a data warehouse should be designed to be easily managed and maintained, with tools and processes in place to monitor and troubleshoot the system. This includes features such as backup and recovery, performance monitoring, and resource management.
- Scalability: The architecture of a data warehouse should be scalable, and able to handle large volumes of data, increasing user demands. This can be achieved through techniques such as distributed processing, partitioning, and clustering.
- Extensibility: The architecture of a data warehouse should be designed to support future growth and expansion. This includes features such as modularity, flexibility, and the ability to add new data sources or applications.
- Separation: The architecture of a data warehouse should separate the analytical processing from the operational processing. This helps to ensure that the performance of the data warehouse is not affected by operational activities and that users can perform complex queries without impacting the performance of operational systems.
Overall, these properties are important for ensuring that the architecture of a data warehouse is effective, efficient, and reliable, and can support the needs of the organization over the long term.
In conclusion, we can say that a Data warehouse is an information storage repository that contains data from single or multiple sources that can be dated historically or recently. The data warehouse architecture can be defined as the overall architecture of data communication, processing, and final records being presented to end clients computing within the enterprise. Data warehouse architecture is classified into three types: single-tier architecture, two-tier architecture, and three-tier architecture.
Security, Administerability, Scalability, Extensibility, and Separation are the five pillars of Data Warehouse architecture. It is critical to be aware of the properties and characteristics that must be considered when designing the data warehouse architecture.
Frequently Asked Questions(FAQs)
Here are some FAQs on data warehouse architecture:
Q1. What are some common data sources that are integrated into a data warehouse architecture?
Ans. Common data sources that are integrated into a data warehouse architecture include transactional databases, flat files, spreadsheets, and other data storage systems.
Q2. What is the role of ETL in a data warehouse architecture?
Ans. ETL, which stands for extract, transform, and load, is a crucial part of a data warehouse architecture. ETL ensures that data is consistent and accurate and can be easily analyzed.
Q3. What are some common tools used in a data warehouse architecture?
Ans. Common tools used in a data warehouse architecture include ETL tools, data modeling tools, data quality tools, and data visualization tools.
Q4. What is the role of data modeling in a data warehouse architecture?
Ans. Data modeling involves defining the structure of the data warehouse and its relationships with other data sources. This is important for ensuring that data is consistent and accurate and can be easily analyzed and accessed.
Q5. What is a data warehouse appliance?
Ans. A data warehouse appliance is a pre-configured hardware and software system that is designed specifically for data warehousing. It typically includes hardware, such as servers and storage devices, and software, such as database management systems and ETL tools, that are optimized for data warehousing.
Q6. What is the role of data governance in a data warehouse architecture?
Ans. Data governance is the process of managing the availability, usability, integrity, and security of the data used in an organization. In a data warehouse architecture, data governance is important for ensuring that data is consistent, accurate, and secure. It involves establishing policies and procedures for data management, as well as monitoring and enforcing those policies.