What is a Logical Data Warehouse, What Are Its Key Benefits, & How Does It Differ from a Traditional One?

Data warehouses have been around for a while now, in internet years. The idea of organizing all of an organization’s data in one central location where you could access that data on demand was first set forth by two IBM data scientists in the late 1980s. The first true data warehouse architecture dates from 1992, when it was first described by William Inmon.

Data warehouses are a data storage architecture that allows businesses to systematically organize, understand, and use their data to make strategic decisions. (For details, see our blog on data warehouses).

The traditional enterprise data warehouse, which houses data from transactional databases, line-of-business applications, CRM systems, and ERP systems, uses ETL (extract, transform, and load) processes to standardize, clean, and transform data. Due to the increased volume, variety, and speed of large data sets from the cloud, social networks, mobile devices, and Internet of Things (IoT) devices, such data warehouses have become less effective.

However, basic data warehouse architecture has adapted in the 21st century to handle ever-increasing volumes of data and the challenges of fragmented information.

One such adaptation, providing unified data access and analysis that can include the cloud, is the logical data warehouse.

What is a logical data warehouse?

A logical data warehouse (LDW) is a data warehouse that provides a virtual data layer on top of existing data sources. This layer enables users to interact with data from diverse sources without the need for ETL, which eliminates the need for data to move through ETL processes. That means that data can reside in local operational systems or even within cloud services but be accessed from within the same infrastructure.

LDWs abstract the complexities of large data using a combination of data virtualization, metadata management, and distributed processing. They employ SQL interfaces, REST APIs, or web portals to help users analyze their data using visual tools.

Logical data warehouse architecture

Core components of a logical data warehouse include:

• Virtual data layer (or logical layer): Provides various mechanisms for viewing data in the data warehouse and elsewhere without the need to relocate and transform the data first – most notably, via data virtualization. It complements the traditional central warehouse with functions that search and transform data in real time.
• Data sources: The data that populates the warehouse, which can originate from any number of internal and external sources, such as relational databases, Hadoop-LIKE clusters, and NoSQL databases.
• Logical data architecture: Abstracts the inherent complexities of big data using a combination of data virtualization, metadata management, and distributed processing, presenting the data as if it were all contained in a single data warehouse.

Key benefits of a logical data warehouse

This section describes some of the key benefits of LDWs.

Improved data integration

LDWs seamlessly integrate data from diverse sources, including streaming sources, improving productivity and making data easier to find. This enables shared access to data across an entire organization, which leads to better collaboration between business teams. When there is a shared understanding of data assets across the entire enterprise, the business makes better decisions.

Enhanced data governance

An LDW provides the ability to collect and consolidate the entirety of an organization’s data with a single technology, including historical data, and perform a unified analysis that no one system could do alone. This architecture makes trusted and reusable data services available to users throughout the enterprise. They can perform self-service analytics safely without risking the consistency and accuracy of the data itself.

Real-time analytics

LDWs’ ability to leverage data virtualization provides native support for real-time access to data and real-time analytics. Data integration is simplified, data security is unified, and data delivery is delivered in precisely the format required.

Increased scalability and flexibility

LDWs enable companies to leverage legacy investments such as data warehouses, data marts, sandboxes, and data lakes while still meeting evolving data requirements. The data virtualization layer of an LDW can incorporate new data sources as they arise without disrupting any existing processes.

Reduced costs

An LDW can help a company modernize its data approach and analytics architecture without disrupting legacy investments. It can also further enable them to eliminate physical data warehousing infrastructure altogether, streamlining data management. Because LDWs employ a common analytic data management architecture across all its diverse data types, technologies, users, and use cases, they enable a company to answer questions about the business itself, analyzing past performance and predicting future outcomes. This helps the business scale with confidence, adding or changing the design as its priorities change.

Logical data warehouse vs. traditional data warehouse

The following table summarizes the key differences between a traditional data warehouse and an LDW.

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