What is a Data Warehouse?

Data warehouses are a central repository for integrated data collected from various tools and software. The data warehouse architecture is robust and comprehensive, storing large amounts of data to support business intelligence (BI) tasks such as analytics.

The primary purpose of a data warehouse is to analyze historical data. Analysts assess the data over longer periods to determine common patterns and trends. This assessment can aid in uncovering valuable insights that might not be viewable in present-day data and help improve decision-making.

At its core, a data warehouse serves as a single source of truth, allowing businesses to remain competitive within an industry. Businesses can monitor performance more efficiently by using a data warehouse to accumulate data from internal spreadsheets, reports, and other analytics tools.

What is a cloud data warehouse?

A cloud data warehouse is a variation of a typical data warehouse that a third-party provider operates within the cloud.

The main difference between a data warehouse and a cloud data warehouse is the former was originally built with on-premises servers. While there are many advantages, on-premises data warehouses aren’t as flexible, requiring more detailed forecasting to scale the data warehouse efficiently.

Cloud data warehouses are typically easier to use, even for those new to data warehousing. They are also more scalable and can handle quick data processing loads. Because they are more modern, cloud data warehouse architecture typically has artificial intelligence (AI) integrations that assist with processing data more efficiently.

What do data warehouses support?

Data warehouses support BI by consolidating data gathered from multiple sources into a centralized format that ensures accuracy and consistency. They structure data to be assessed and queried to provide insights to analysts.

When paired with data mining, a data warehouse can assist with building a comprehensive dataset used to apply data modeling and predictive analytics to assess patterns. This provides a more detailed understanding of how to drive future business strategies to achieve goals.

Data warehouses can also help businesses optimize their overall performance. The data warehouse will use indexing to quickly gather historical and real-time data much more quickly than a team of analysts could do manually, improving efficiencies.

Businesses experience various benefits from utilizing data warehouses, such as increased data quality, simplified data, scalability, enhanced reporting and analytical capabilities, improved security, and identifying potential opportunities.

Once data is collected from several sources, the data warehouse performs a cleansing and transformation process. This means analysts receive accurate information they can rely on to make better-informed decisions. The data is also organized into a consistent structure that is easier to assess and report on while also eliminating silos.

Data warehouses make it easier to spot customer behavioral patterns and long-term performance trends by assessing historical data. Marketers can track and assess this information to identify opportunities for growth and plan more strategically.

A data warehouse also allows businesses to adhere to data governance. All data is stored within a centralized repository, ensuring better security of sensitive information.

The data warehouse architecture is how the system processes and organizes the data for analysis.

While every data warehouse is built differently to accommodate the needs of each user or business, they all share a standard variation of vital components.

• Data sources: Where the data is stored before being loaded into the data warehouse.
• Extract, Transform, Load (ETL): Data is extracted from data sources, transformed into a specific format, and loaded into the data warehouse. This component ensures accuracy and consistency across data.
• Data model: Organizes data logically for analysis.
• Data storage: Where data is stored within the data warehouse.
• Data access: How users can access the data within the warehouse.
• Data security: Ensures data is secure and cannot be accessed by unauthorized entities.
• Metadata: Details information about the structure and content of the data to make the information easier to manage.

There are three main types of data warehouse architecture: single-tier, two-tier, and three-tier.

Single-tier architecture

Single-tier data warehouse architectures are the least likely to be used for large businesses. This is because its main purpose is the simplify data management and reduce the amount of data stored and analyzed by building a more condensed data set.

There are three layers within this architecture type: the source layer, the data warehouse layer, and the analysis layer.

The benefit of a single-tier architecture is that it removes redundancies to improve data quality. This architecture tier would be considered too basic for larger organizations that handle large amounts of data and work with multiple data streams.

Two-tier architecture

In contrast to the single-tier architecture, the two-tier architecture has a data staging area. This is where ETL tools extract and cleanse the data before formatting it logically and consistently.

In the two-tier architecture, the data staging area comes after the source layer and before the data warehouse layer, ensuring data is high quality to assess and use for BI purposes. There is then an analysis layer where data efficiently integrates for analysis.

Some businesses use data marts as a server, which is a condensed version of a data warehouse that gives access to a specific set of data to authorized groups of users within an organization, such as a marketing department. In these cases, the two-tier architecture can be beneficial.

Three-tier architecture

The three-tier architecture is the most widely used structure and commonly used among enterprise businesses building a data warehouse system to store, process, and organize large amounts of data.

This structure includes the source layer, the reconciled layer, and the layer of the data warehouse. They are also referred to at the bottom, middle, and top tiers.

The reconciled layer comes after the source layer and before the data warehouse layer. The benefit of a reconciled layer is how it can assist with creating a standardized reference data model used across an enterprise. This layer also aids with operational tasks like reporting.

Bottom tier

The bottom tier is what we know as the warehouse layer. This is where data is extracted, transformed, and loaded into the main data repository using ETL tools.

Middle tier

The middle tier is when data is rearranged into a consistent structure. This makes the information easier to digest during the querying and analysis stage.

Data is arranged with an Online Analytical Processing (OLAP) server using two models.

The relational OLAP model is often referred to as ROLAP. This model allocated data processes that are multidimensional to relational operations. In contrast, the multidimensional OLAP (MOLAP) model implements multidimensional information and operations.

Top tier

The top tier in this type of data warehouse architecture is where a user can access and actively interact with and manage the data. This tier is APIs, and useful tools used for data analysis, data mining, and reporting are stored.

Effectively utilizing a data warehouse makes managing large volumes of historical and real-time data easy.

Take control of your data and gain valuable insights from historical trends and patterns. By having a detailed overview of your data, you can plan more effectively for the future.

Find out more about how data warehouses benefit businesses by requesting a demo with a member of our team.

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• What is Data Taxonomy?
• Advanced Data Structures Explained