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.
What is a Data Warehouse and Why is it Important?
A data warehouse is a repository that includes past and commutative information from one or multiple sources. It streamlines reporting and BI processes of businesses. Instead of processing transactions, a data warehouse works as a relational database that is intended for querying and analysis.
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.
Characteristics of Data Warehouse Design
The following are the main characteristics of data warehouse design:
A data warehouse design is focused on 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 the business operations or transactions, data warehousing emphasizes on business intelligence (BI) that is, displaying and analyzing data for decision-making. It also offers a straightforward and succinct interpretation of the particular theme by eliminating data which may not be useful for decision makers.
A data warehouse design unifies and integrates all analogous data from different databases in a collectively acceptable way. It is established by incorporating data from diverse sources such as , relational and non-relational databases, flat files, mainframe, cloud-based systems, etc. Besides, a data warehouse must maintain consistent nomenclature, layout, and coding to facilitate effective data analysis.
Unlike other operational systems, a data warehouse stores data collected over an extensive time horizon. The data gathered is identified with specific time duration and provides insights from the past perspective. Moreover, when data is entered in the warehouse, it cannot be restructured or altered.
Another important characteristic 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.
Types of Data Warehouse Architecture
A data warehouse architecture defines the arrangement of data and the storing structure. As the data must be organized and cleansed to be valuable, a data warehouse architecture centers on identifying the most effective technique of extracting information from raw data and converting it into a simple consumable structure that delivers valuable business intelligence.
When designing a company’s data warehouse, there are three main types of architecture to be considered.
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 architecture is not suitable for businesses with complex data requirements and numerous data streams.
This architecture splits the tangible data sources from the warehouse itself. 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.
This is the most commonly employed data warehouse architecture as it produces a well-organized data flow from raw information to valuable insights.
The bottom tier typically comprises the databank server that is used to abstract 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.
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.
Main Constituents of Data Warehouse Architecture
Now that we have discussed the three data warehouse architectures, let’s look at the main constituents of a data warehouse.
A data warehouse design mainly consists of five 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 which are the row-centered databases you perhaps use on an everyday basis. For example, Microsoft SQL Server, SAP, Oracle, and IBM DB2.
- Analytics databases which are precisely developed for data storage to sustain and manage analytics. For example, Teradata and Greenplum.
- Data warehouse applications which aren’t exactly a kind of storage databases, 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 which 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, Microsoft Azure SQL, and Google BigQuery.
2. Extraction, Transformation, and Loading Tools (ETL)
ETL tools are often regarded as the spine of a 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
Metadata describes the data warehouse and offers a framework for data. It is utilized for constructing, preserving, handling and making use of the data warehouse.
It can be characterized into two types:
- Technical Metadata, which comprises information that can be used by developers and managers when executing warehouse development and administration tasks.
- Business Metadata, which comprises information that offers easily understandable standpoint of the data stored in the warehouse.
Metadata plays an important role for the businesses as well as the technical teams to understand the data present in the warehouse and to convert it into information.
4. Data Warehouse Access Tools
A data warehouse is constructed on top of a database or group of databases at the back end. Corporate users generally cannot work with databases directly. This is why there are several tools intended to assist them. Some of these tools include:
- 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, which systematize the procedure of identifying arrays and links in huge quantities of data using cutting-edge statistical modeling methods.
- OLAP tools, which help construct a multi-dimensional data warehouse and allow analysis of enterprise data from numerous viewpoints.
5. Data Warehouse Bus
It defines the data flow within a data warehousing architecture and includes a data mart. Data mart is an access level used to transfer data to the users. It is used for partitioning data which is produced for the particular user group.
Automate the Process of Data Warehousing with Astera
An enterprise data warehouse is one of the pre-requisites to establish an effective BI process. However, building a data warehouse can be a time-consuming process, particularly without automation. Astera combines automated data warehousing with data virtualization to expedite the design, development, and maintenance of an EDW, helping businesses bridge the data-to-insight gap faster.