What Is a Logical Data Warehouse?

Big data can be a tremendous asset for companies to build for better business decisions and set themselves on the right path forward. Despite strides made in analytics and data management, the real-time capabilities for viewing data can still be overwhelming for newcomers to this technology. One tool that is helping some analysts and businesses better understand what’s at their disposal is the use of a logical data warehouse to bring traditional data sources into one consolidated, virtualized hub.

Understanding Logical Data Warehouses

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If you want to take the next steps into data integration or explore what exactly data management can do for your business, you should start by searching “what is a logical data warehouse?” online. A logical data warehouse, also known as an LDW, is a data management structure in which an architectural layer sits on top of a traditional data warehouse, enabling access to a multitude of data sources while appearing as a consolidated source to business users. This optimizes the likes of databases and data lakes, along with other data sources like web services and applications.


This form of a data warehouse is considered the next generation of data capability, helping companies of all sizes to meet their growing data management needs. With the help of various data sources, logical data warehouse components go through a process of consolidation that puts them in one place logically instead of physically. An LDW has advanced to support a wide variety of available data source systems. This helps businesses to digitally reinvent themselves, while enabling real-time streaming analytics, garnering business context, and optimizing workload.

Data Warehouse Uses and Characteristics

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Logical data warehouses allow business analysts to utilize an existing enterprise data warehouse, while allowing it to remain at their disposal for extraction. These warehouses contain one or more data lakes as repositories, ensuring consistency with data marts while setting governance policies. This data warehouse technology is beneficial to a variety of sectors and industries to make sure that they are making business decisions based on the variety of sources and the need for time-sensitive business processes to be completed.


Data mining through warehouses can help gain new insights from information held in a large database management system. This lets retailers, financial institutions, and other sectors conduct market research by analyzing user behavior to make business decisions.

In the insurance sector, data warehouses are used to assess risk management that can determine the insurability of those organizations. Through predictive analytics, warehouses offer assistance with handling insurance claims at a greater pace, allowing analysts and adjusters to avoid having to comb through large volumes of data.

Benefits of an LDW

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The LDW approach allows companies to meet evolving data requirements while taking advantage of existing analytic applications that have already been put in place. This includes enterprise data warehouses to help to manage business data and historical data. Companies can make decisions based on what’s available within that data virtualization layer that incorporates new data sources without disrupting any existing business processes. Data warehouse architecture allows data scientists and all with the access of that management system to highlight information based on the decisions that need to be made.


A logical data warehouse allows companies to gain business insights through diverse and multiple data sets. LDW can help a business scale its data management strategy as it grows, avoiding any inconsistencies. This empowers data consumers and analysts by making these facts easier to find and understand in real time. A data warehouse can improve productivity for all business users. By avoiding replication and redundancy, organizations are able to make the most of the data platforms that are available to them for a better strategy.

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