Data Warehouse supports business intelligence activities such as data analysis and reporting. It is a large, structured database that stores historical and current data from various sources within an organization, in a format that is optimized for querying and analysis. Unlike transactional databases that are designed for day-to-day operational tasks, data warehouses are designed to support complex analysis and reporting tasks. In this article, we will explore the key features and benefits of data warehouses, as well as the different types of data warehouses and their architectures. We will also discuss some common challenges associated with building and maintaining data warehouses, and the best practices for overcoming these challenges.
What is Data Warehouse?
A Data Warehouse is a secure electronic storage of information by an organization to collect, clean and transform data from multiple sources and store it in a single repository. It enables and supports business intelligence activities such as reporting, analysis and decision-making. A Data Warehouse can provide useful insights into the organization's operations by allowing fast and complex queries by allowing fast and complex queries on a large amount of historical data.
A Data Warehouse is important for business intelligence and analysis because it allows organizations to:
Query and analyse historical data that can reveal trends, patterns and insights.
Consolidate data from different systems and formats into a single repository
Improve data quality and consistency by applying standard rules and transformations.
Enhance data security and access control by storing sensitive information in a separate location.
Speed up query performance and data throughput by using specialized techniques and technologies.
Characteristics of Data Warehouse:
Subject-oriented: It provides information about a specific subject or domain instead of the whole organization's ongoing operations. Examples of subjects include product information, sales data, customer and supplier details etc.
Integrated: It creates consistency and standardization among different data types and formats from disparate sources. it applies common rules and transformations to integrate data from various systems and applications.
Time-variant: It stores and maintains historical data that can be analyzed over time. It records the changes and trends in data over different periods such as daily, weekly, monthly or yearly.
Non-volatile: It is stable and secure as data is not affected by the operations or updates of the source systems. It preserves the integrity and quality of data by preventing unauthorized access or manipulation.
Architecture
Data warehouse systems can be constructed in three different ways based on the number of tiers in their architecture. The three approaches are:
One-tier architecture
Two-tier architecture
Three-tier architecture
1. One-tier Architecture
The single-tier data warehouse architecture reduces the amount of data stored in a data warehouse by building a more compact data set. The main goal of having such an architecture is to remove redundancy by minimizing the amount of data stored.
The one-tier architecture has three layers:
A source layer
A data warehouse layer
An analysis layer
Advantages: It reduced data redundancy by minimizing the amount of data stored.
Disadvantage: It does not separate analytical and transactional processing which can affect the performance and scalability of the system. It requires a single hardware layer that can be expensive and difficult to maintain.
To overcome this disadvantage, you need two-tier Architecture
2. Two-tier Architecture
The two-tier Architecture is a client-server approach that includes a staging area for data sources and a data warehouse layer. The staging area is used to extract, transform and load (ETL) data from different sources and ensure its quality and consistency. The data warehouse layer is usually a relational database system that stores the cleansed and integrated data.
Advantages: It separates analytical and transactional processing by adding a staging area for data sources. It also improves data quality and consistency by using ETL tools.
Disadvantage: You cannot expand it to support a large number of users. It does not have a front-end layer for displaying and analyzing data.
3. Three-tier Architecture
The three-tier architecture is a widely used approach for data warehouse systems. It consists of three layers:
Bottom Layer: Where data is cleansed and transformed data is loaded from different data sources. It may also include a metadata repository that stores information about the data warehouse structure and contents.
Middle Layer: It is an application layer that provides an abstract view of the database and allows fast querying of the data using OLAP servers. The OLAP servers can be implemented using relational or multi-dimensional models.
Top Layer: It is a frontend layer that contains tools for displaying and analysing the data such as dashboards, reports and data mining applications. This layer enables users to interact with the data warehouse and gain insights from it.
Advantages: It separates analytical and transactional processing by adding an application layer that provides an abstract view of the database. It also allows fast querying for data using OLAP servers. It supports a large number of users and can be scaled easily.
Disadvantage: It is more complex than two-tier architecture. It requires more communication points between the tiers. The client does not maintain a persistent database connection.
Data Integration and ETL Tools
Data integration is the process of combining data from multiple sources into a unified view that can be used for analysis and reporting. The data warehousing layer is responsible for extracting data from various source systems, transforming the data to meet the needs of the data warehouse, and loading the data into the data warehouse. This process is commonly referred to as ETL (extract, transform, load) and is often implemented using specialized ETL tools.
ETL tools are software applications that automate the ETL process, making it easier and more efficient to integrate data from multiple sources into the data warehouse. ETL tools typically provide a graphical user interface for designing ETL workflows, which specify the sequence of tasks to be performed during the ETL process.
Enterprise data warehouse (Structure, semi-structured and unstructured data)
Enterprise Data Warehouse is responsible for integrating data from various sources and storing it in a structured, unstructured and semi-structured format. The data is structured into tables, with each table representing a particular type of data. For example, there may be tables for customers, products, sales transactions, and so on.
To store semi-structured and unstructured data in the EDW, organizations often use specialized technologies such as data lakes or NoSQL databases. These technologies allow for flexible data storage and retrieval, making it easier to work with semi-structured and unstructured data.
Data Mart layer
In this layer, data is organized into smaller, more focused subsets of the overall data warehouse, known as data marts. The Data Mart layer provides a more focused view of the data, which makes it easier for end-users to access and analyze the data they need without being overwhelmed by irrelevant data. This layer is designed to be easily queried and analyzed by end-users using reporting and analytics tools.
The Data Mart layer extracts the data it needs from the Data Warehouse layer and transforms it into a format that is optimized for the specific business needs of each data mart.
OLAP Layer
The OLAP layer is responsible for providing multidimensional analysis capabilities, allowing users to perform complex queries and generate reports using data from the data warehouse. This layer also includes tools for data visualization, such as charts, graphs, and dashboards, which enable users to quickly and easily interpret and analyze data. By providing powerful analysis capabilities, the OLAP layer plays a critical role in enabling organizations to make informed business decisions based on data insights.
There are three types of OLAP Layer:
1. ROLAP (Relational OLAP)
This uses a relational database to store both the base data and the dimension tables. ROLAP servers can handle a large amount of data but they may have limited SQL functionality and difficulty updating aggregates tables.
2. MOLAP (Multi-dimensional OLAP)
This uses a multi-dimensional array structure to store data and pre-compute complex calculations. MOLAP servers can perform fast slicing and dicing operations but they may have difficulty changing dimensions and storing sparse data.
3. HOLAP (Hybrid OLAP)
This is the combination of ROLAP and MOLAP. Hybrid OLAP servers can store large and sparse data in a relational database while using a multi-dimensional cache for faster queries and calculations.
End-user Interface (reporting tools, interactive dashboards, ad hoc reports)
The end-user interface layer refers to the topmost layer of the architecture that provides access to the data stored in the data warehouse. This layer is also referred to as the presentation layer, as it is responsible for presenting data in a format that is easy for end-users to understand and work with.
Reporting tools enable users to generate pre-defined reports that can be scheduled to run at regular intervals.
Interactive dashboards provide real-time access to key performance indicators (KPIs) and allow users to customize views based on their requirements.
Ad hoc reporting capabilities enable users to create custom reports on-the-fly based on specific data requirements.
Data Warehouse Best Practice
To effectively design a data warehouse, it is crucial to have a deep understanding of the unique business logic and requirements of your organization. However, there are certain best practices that can be applied:
Create a comprehensive data model: Begin by identifying the key aspects of your organization's business logic, and determine which data is most important and how it will be organized within the data warehouse.
Utilize a well-established data warehouse architecture standard: Employing a standardized architecture framework such as 3NF, Data Vault modeling or star schema, can help provide a set of best practices and guidelines for designing, troubleshooting and maintaining the data warehouse.
Develop a data flow diagram: Document the movement of data through the system, ensuring that it aligns with the organization's requirements and business logic.
Establish a single source of truth: With vast amounts of data being handled, it is important to consolidate it into a single repository to avoid inconsistencies and inaccuracies.
Utilize automation tools: Automation can be a powerful tool in managing large amounts of data and simplifying complex tasks.
Facilitate metadata sharing: Establish an architecture that promotes easy sharing of metadata between different components of the data warehouse.
Enforce coding standards: Implementing coding standards can help ensure system efficiency and consistency.
Conclusion:
A data warehouse is a crucial tool for organizations looking to gain insights and make informed decisions based on their data. With the right design, implementation, and maintenance, a data warehouse can provide significant business value and competitive advantage.