For the past three decades, the data warehouse architecture has been the pillar of corporate data ecosystems. And, despite numerous alterations over the last five years in the arena of Big Data, cloud computing, predictive analysis, and information technologies, data warehouses have only gained more significance. Today, there are more possibilities available for storing, analyzing, and indexing data, but the importance of data warehousing cannot be denied.
This article will discuss the basic concepts of data warehouse architecture, types of different enterprise data warehouse architecture, characteristics, and major components of data warehouse architecture and see how they can help in building a data warehouse from scratch. First, let’s get started by understanding the concept of a data warehouse.
What is a Data Warehouse (EDW)?
Let’s define the term ‘data warehouse.’
A data warehouse is a repository that includes past and commutative information from one or multiple sources. This repository can be used by the employees of the organization for analysis, drawing insights, and future forecasting.
The ETL process is a fundamental concept of a data warehouse:
- Extract: Gathering data from various heterogeneous sources
- Transform: Converting sub-standard data into clean, structured, and verified data that is ready to use
- Load: Loading the data onto a new destination
Enterprise data warehouses streamline the reporting and BI processes of businesses. Instead of processing transactions, a data warehouse works like a relational database and performs querying and analysis. The main difference between transactional databases and data warehouses is that transactional databases don’t result in analytics, while analytics is efficiently performed in the data warehouse. In short, transactional databases and data warehouses are similar with the exception of the analytical aspect.
A data warehouse typically includes historical transactional data. However, it can contain data from other sources as well. It distinguishes analytical capacity from transaction capacity and allows companies to amalgamate data from numerous sources. This way, it assists in:
- Preserving past records
- Evaluating the data to better understand and enhance the corporate operations
Along with a relational database, a data warehouse design can contain an extract, transform, and load (ETL) tool, numerical analysis, reporting capabilities, data mining abilities, and other applications that handle the procedure of collecting data, converting it into valuable information, and conveying it to the business analyst and other users. These capabilities enable multiple applications and uses of data warehousing. For example, in the banking sector, data warehouses can be used to create financial models that would improve cost efficiency. Another use case example of data warehousing is supply chain management, where data analytics and forecasting help in reducing lead times and streamline operations.
However, the beginning of any data warehousing initiative requires a holistic and rigorous assessment process. Data warehouse assessment templates are often used for this purpose. Using a data warehouse assessment template would offer in-depth information about the business needs, expectations, the technical aspects of building, planning, and operating the data warehouse. It is also important to note that data warehouse assessment is not a one-off event and is often dependent on a business’s unique needs. These assessments are necessary so that companies know what key issues they may face while planning to build a data warehouse from scratch.
Characteristics of Data Warehouse Design
Now that we are clear about the basic concepts of a data warehouse, let’s look at the important characteristics of a data warehouse or the data warehouse designs.
The following are the main characteristics of data warehousing design, development, and best practices:
A data warehouse design uses a particular theme. It provides information concerning a subject rather than a business’s operations. These themes can be related to sales, advertising, marketing, and more.
Instead of focusing on business operations or transactions, data warehousing emphasizes business intelligence (BI) i.e.; displaying and analyzing data for decision-making. It also offers a straightforward and succinct interpretation of the particular theme by eliminating data that may not be useful for decision-makers.
A data warehouse design unifies and integrates data from different databases in a collectively suitable manner using data warehouse modelling. It incorporates data from diverse sources, such as relational and non-relational databases, flat files, mainframe, and cloud-based systems. Besides, a data warehouse must maintain consistent classification, layout, and coding to facilitate efficient data analysis.
Unlike other operational systems, the data warehouse stores data collected over an extensive time horizon. Therefore, the data gathered is identified within a specific time duration and provides insights from the past perspective. Moreover, when data is entered into the warehouse, it cannot be restructured or altered.
Another important characteristic of a data warehouse is non-volatility which means that the preceding data is not removed when new data is loaded to the data warehouse. Moreover, data is only readable and can be intermittently refreshed to deliver a complete and updated picture to the user.
Define Data Warehouse Architecture
Data warehouse architecture is a data storage framework’s design of an organization. A data warehouse architecture takes information from raw sets of data and stores it in a structured and easily digestible format.
Types of Data Warehouse Architecture
A data warehouse architecture defines the arrangement of the data in different databases. As the data must be organized and cleansed to be valuable, a modern data warehouse structure centers on identifying the most effective technique of extracting information from raw data in the staging area and converting it into a simple consumable warehousing structure using a dimensional model that delivers valuable business intelligence. Moreover, unlike a cloud data warehouse, a traditional data warehouse requires on-premise servers for all components of the warehouse to function.
When designing a corporation’s data warehouse, there are three types of traditional data warehouse models to consider:
Single-tier data warehouse architecture
The structure of a single-tier data warehouse architecture centers on producing a dense set of data and reducing the volume of data deposited. Although it is beneficial for eliminating redundancies, this type of warehouse architecture is not suitable for businesses with complex data requirements and numerous data streams. This is where multi-tier data warehouse architectures come in as they deal with more complex data streams.
Two-tier data warehouse architecture
In comparison, the data structure of a two-tier data warehouse architecture splits the tangible data sources from the warehouse itself. Unlike a single-tier, the two-tier architecture uses a system and a database server. This is most commonly used in small organizations where a server is used as a data mart. Although it is more efficient at data storage and organization, the two-tier architecture is not scalable. Moreover, it only supports a nominal number of users.
Three-tier data warehouse architecture
The three-tier data warehouse architecture is the most common type of modern DWH architecture as it produces a well-organized data flow from raw information to valuable insights.
The bottom tier in the data warehouse architecture typically comprises the databank server that creates an abstraction layer on data from numerous sources, like transactional databanks utilized for front-end uses.
The middle tier includes an Online Analytical Processing (OLAP) server. From a user’s perspective, this level alters the data into an arrangement that is more suitable for analysis and multifaceted probing. Since it includes an OLAP server pre-built in the architecture, we can also call it the OLAP-focused data warehouse.
The third and the topmost tier is the client level which includes the tools and Application Programming Interface (API) used for high-level data analysis, inquiring, and reporting. However, people barely include the 4th-tier in the data warehouse architecture as it is often not considered as integral as the other three types of data warehouse architectures.
The DW diagram below illustrates the 3 tier architecture of a data warehouse:
These are the different types of traditional data warehouse architecture in data mining, as illustrated further by the data warehouse diagram. Now, let’s learn about the major components of a data warehouse (DWH) architecture and how they help build and scale a data warehouse in detail.
Main Components of Data Warehouse Architecture
A data warehouse (DWH) design consists of six main key components:
1. Data Warehouse Database
The central component of a data warehousing architecture is a databank that stocks all enterprise data and makes it manageable for reporting. Obviously, this means you need to choose which kind of database you’ll use to store data in your warehouse.
The following are the four database types that you can use:
- Typical relational databases are the row-centered databases you perhaps use on an everyday basis. For example, Microsoft SQL Server, SAP, Oracle, and IBM DB2.
- Analytics databases are precisely developed for data storage to sustain and manage analytics—for example, Teradata and Greenplum.
- Data warehouse applications aren’t exactly a kind of storage database, but several dealers now offer applications that offer software for data management as well as hardware for storing data. For example, SAP Hana, Oracle Exadata, and IBM Netezza.
- Cloud-based databases can be hosted and retrieved on the cloud so that you don’t have to procure any hardware to set up your data warehouse—for example, Amazon Redshift, Google BigQuery, and Microsoft Azure SQL.
2. Extraction, Transformation, and Loading Tools (ETL)
ETL tools are central components of an enterprise data warehouse architecture. These tools help with extracting data from different sources, transforming it into a suitable arrangement, and loading it into a data warehouse.
The ETL tool you choose will determine:
- The time expended in data extraction
- Approaches to extracting data
- Kind of transformations applied and the simplicity to do so
- Business rule definition for data validation and cleansing to improve end-product analytics
- Filling mislaid data
- Outlining information distribution from the fundamental depository to your BI applications
Before we delve into the different types of metadata in data mining, we first need to understand what is metadata. In the data warehouse architecture, metadata describes the data warehouse and offers a framework for data. It helps in constructing, preserving, handling, and making use of the data warehouse.
There are two types of metadata in data mining:
- Technical Metadata, which comprises information that can be used by developers and managers when executing warehouse development and administration tasks.
- Business Metadata comprises information that offers an easily understandable standpoint of the data stored in the warehouse.
Metadata plays an important role for businesses and the technical teams to understand the data present in the warehouse and convert it into information.
4. Data Warehouse Access Tools
A data warehouse uses a database or group of databases as a foundation. Data warehouse corporations generally cannot work with databases without the use of tools unless they have database administrators available. However, that is not the case with all business units. This is why they use the assistance of several no-code data warehousing tools, such as:
- Query and reporting tools, which help users produce corporate reports for analysis that can be in the form of spreadsheets, calculations, or interactive visuals.
- Application development tools, which help create tailored reports and present them in interpretations intended for particular reporting purposes.
- Data mining tools for data warehousing, which systematize the procedure of identifying arrays and links in huge quantities of data using cutting-edge statistical modelling methods.
- OLAP tools, which help construct a multi-dimensional data warehouse and allow the analysis of enterprise data from numerous viewpoints.
5. Data Warehouse Bus
It defines the data flow within a data warehousing bus architecture and includes a data mart. A data mart is an access level used to transfer data to the users. It is used for partitioning data that is produced for a particular user group.
6. Data Warehouse Reporting Layer
The reporting layer in the data warehouse allows the end-users to access the BI interface or BI database architecture. The purpose of the reporting layer in the data warehouse is to act as a dashboard for data visualization, create reports, and take out any required information.
Best Practices of Data Warehouse Architecture
- Create data warehouse models that are optimized for information retrieval in both dimensional, de-normalized, or hybrid approaches.
- Select a single approach for data warehouse designs such as the top-down or the bottom-up approach and stick with it.
- Always cleanse and transform data using an ETL tool before loading the data to the data warehouse.
- Create an automated data cleansing process where all data is uniformly cleaned before loading.
- Allow sharing of metadata between different components of the data warehouse for a smooth retrieval process.
- Always make sure that data is properly integrated and not just consolidated when moving it from the data stores to the data warehouse. This would require 3NF normalization of data models.
Build Your Data Warehouse with Astera Centerprise
From a list of warehouse tools, why should you go with Centerprise?
Astera Centerprise is an enterprise-grade ETL solution that integrates data across multiple systems, such as SQL Server, Excel, Salesforce, and more. It enables users to manipulate data using a comprehensive set of built-in transformations and helps move the transformed data to a unified repository, all in a completely code-free, drag-and-drop manner. This way you can build your three-tier enterprise data warehouse architecture with ease.