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What is an Enterprise Data Warehouse?

Enterprise data warehouse refers to a relational data warehouse that contains a company’s business data. Businesses often draw their insights from the analytics made from data stored in a data warehouse. Currently, more businesses are becoming data-driven, mainly relying on business data insights to improve most business processes.

Data stored in the enterprise data warehouse include customer spending habits. Authorized members of the organization can consistently access the data without hassles. This could be real-time data feeds or the latest snapshots from different sources.

Types of Enterprise Data Warehouse

There are generally three types of enterprise data warehouses. These could be on-premise, cloud data warehouses, and virtual data warehouses. In most cases, the enterprise warehouses contain data for storage, archiving, reporting, and historical analysis. On the other hand, an operational data store (ODS) is often used as the interim logical area for the data warehouse.

On-Premise Data Warehouse

The on-premise warehouse is mainly used within the organization’s firewall. It provides full control, which comes with a lot of responsibility. You need a full tech stack maintained by system administrators, network engineers, and database administrators.

Pros and Cons on-Premise Data Warehouse

With the on-premise warehouse, the organization has more control over the data stored because you can choose the kind of software that will be used to set up the data warehouse. There is also little to no network latency. Only authorized personnel connected to the network can access the data warehouse.

There are still some disadvantages to using an on-premise data warehouse, including the high investment cost. This data warehouse requires a lot of investment to purchase the necessary resources and set up the initial hardware and software. It’s nearly impossible to scale resources up and down as per the requirements of the organization due to the limited hardware.

Cloud Data Warehouse

With the advancing technology, more organizations have started investing heavily in cloud data warehouses. They provide organizations with great scalability, cost efficiency, and elasticity. Fortunately, you do not need additional tech resources and staff to manage the data.

Pros and Cons of Cloud Data Warehouse

With the cloud data warehouse, you can scale operations up or down as per the organization’s requirements automatically, and you only pay for what you use. There is also a low cost of investment as the organization doesn’t have to invest a lot of money in buying large storage spaces.

The disadvantages of the cloud data warehouse are insecurity issues and limited accessibility. There is also high latency when accessing the data.  

Virtual Data Warehouse

The organization’s data stays in the source systems for the virtual data warehouse, and you need to create a virtual layer for analyzing data and reporting. This is usually an easier and faster technique to get started though the virtualization process often causes many performance issues at scale. So, you have to rely on the source systems for querying the data.

Enterprise Data Warehouse and Business Intelligence

Business intelligence refers to an organization’s set of methods and software to summarize, aggregate, analyze and derive the value of its operations. Integrating data across an organization and sharing insights is difficult, especially when the data is collected from different sources.

You will need great data analytics and business intelligence to solve problems that are associated with data warehouses. This is achieved by bringing the relevant enterprise data into an easily accessible central repository for great analysis across the entire enterprise. Remember that data analysis is very vital for marketing for business growth.

Enterprise Data Warehouse and ETL

Extract, Transform, and Load are critical components of a functioning enterprise data warehouse. The ETL consolidates data from different sources and then transforms it into a useful, consistent, and modeled format. Some of the older on-premises data warehouses run various transformations in the data pipeline to ensure there are no limited analytical resources.

Enterprise Data Warehouse Architecture

Several architectures can be used for the enterprise data warehouse, including.

One-tier architecture

One-tier architecture is the most primitive form of EDW architecture, where reporting tools are connected directly to the data warehouse. It’s easier to set up this architecture and implement it for small datasets.

Though, for large datasets, it causes various issues, especially when dealing with organizations with hundreds of gigabytes of data. In this case, the Reporting Tool should go through all the data, which takes a lot of time. In addition, going through the large dataset for each query will result in low performance. Hence, this type of enterprise data warehouse architecture is primarily suitable for organizations with small datasets.

Two-tier Architecture

This enterprise data warehouse infrastructure implements Data Mart Layer between the reporting and enterprise data warehouses. The Data Marts are often viewed as smaller databases containing specific domain information. Information stored in the data warehouse is split into several Data Marts based on the domain information.

The reporting tool is then connected to the Dart Mart Layer. Performing queries on a single Data Mart requires much less time because it consists of only a small part of the data in the data warehouse. This architecture is more suitable for real-life scenarios.

Three-tier Architecture

Three-tier architecture implements an OLAP layer between the Reporting Layer and the Data Mart Layer. The OLAP layer consists of the cubes used to store data in a multidimensional form, thus allowing faster analysis to be performed on the data stored. Several operations can be performed on the OLAP cubes, including roll-up, slice, dice, and drill-down.

Roll-up involves reducing the attributes being measured through performing several integrations or performing grouping based on a specific order. For the slice, the dimension is removed by specifying a filter dimension. Dice operations involve specifying the filters for two or more dimensions, while the drill-down refers to increasing the number of attributes being measured for in-depth analysis.

Conclusion

This article provides you with in-depth knowledge of the enterprise data warehouse. It will help you understand what EDW is, the available different types, pros, and cons of the different kinds of EDWs. It will also help you understand the enterprise data warehouse architecture, which is vital in helping you choose the right architecture.

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