Highlights:

  • One of the prime reasons for the popularity of LDW is that their components can be conceptually combined in one location by combining numerous engines and various data sources from throughout the organization.
  • With LDW, organizations can manage and access each of the many data stores as if they were a single logical (or virtual) data store.

The concept of a logical data warehouse has recently been the subject of much discussion. But what is causing this buzz? Why are logical data warehouses considered to be so crucial for data professionals? To get an understanding, it is necessary to examine the function of a logical data warehouse — what is it, and what does it do?

A Logical Data Warehouse (LDW) is an architecture for data management where an architectural layer sits on top of a conventional data warehouse to enable access to several data sources while making them appear to users as a single, “logical” source of data. It essentially consists of an analytical data architecture that optimizes both conventional data sources (such as databases, enterprise data warehouses, data lakes, etc.) and other sources of data (such as applications, big data files, web services, and the cloud) to satisfy every analytics use case. The phrase logical data warehouse was first used in 2009 and is now more in demand as data complexity becomes a bigger issue for many enterprises.

LDW is often referred to as the data warehouse of the future since it can accommodate the expanding data management requirements of businesses. One of the prime reasons for the popularity of logical data warehouses is that their components can be conceptually combined in one location by combining numerous engines and various data sources from throughout the organization. Modern LDW has grown and now supports the many different data platforms, sources, and business use cases that are currently available. It assists businesses in digitally reinventing themselves, enables real-time streaming analytics, and optimizes operations through better, data-driven judgement.

Why is the logical data warehouse so important?

To understand why a logical data warehouse is important, it is essential to examine how data –storage and processing – has changed over the past few years and how those changes affect the data architecture. Of course, this is to do with the big data explosion that has led to a surge in interest in advanced analytics platforms to deal with big data.

According to IDC, as data volumes grow more than 50 times in the coming years, 85% of this data will be ‘new data’. New data will be unstructured or multi-structured, one that would not fit nicely into your existing databases or data warehouse. This means that the future data architecture will contain many data stores containing different types and formats of data. With all of these data stores, it is very easy to create more and more data silos and not get the full value out of all of the data.

A data lake can possibly resolve this issue, but the question remains as to how many organizations can acquire a data lake repository. The way data is used – processed, refined and aggregated to derive actionable insights – indicates that there will be several data repositories with different qualities unless we get to the ‘authoritative’ data store for regulatory and statutory reporting.

This is where LDW comes to play. Thanks to the logical data warehouse, organizations can manage and access each of the many data stores as if they were a single logical (or virtual) data store. All differences and complexities of different data stores are hidden via a logical data warehouse. The data can be combined and transformed to provide a common and consistent view of all data through the LDW. This enables the full utilization of data assets.

Benefits of Logical Data Warehouse

Logical data warehousing helps meet evolving data demands: The logical data warehouse model enables businesses to address changing data requirements while gaining from existing investments in physical approaches such as data warehouses, data marts, sandboxes, data lakes, and others. Its multi-engine approach allows businesses to fulfil various analytical needs. The various data management elements, such as enterprise data warehouses, data lakes, data marts, etc., are not mutually exclusive and can effectively complement one another.

A logical data warehouse ensures that your analytics strategy is agile and flexible for new data demands. It prevents the data management team from becoming stuck using a single technology or strategy, regardless of how the market evolves in the future. Businesses can choose which components to use for various data management tasks to fulfil their needs. The data virtualization layer can accommodate additional data sources as the business expands and more data is produced without interfering with any already-running operations.

Modernizing data approach with logical data warehousing: By implementing a uniform analytic data management architecture across all data types, technologies, users, and use cases, logical data warehouses enable businesses to update their data approach and analytics architecture. By combining all of the data from different sources, the logical data warehouse gives an organization the ability to examine past performance, forecast future results, and respond to queries about the business. As an organization expands, a logical data warehouse can help it scale its data management strategy by starting with the data it already has and easily adding new data or changing the architecture as priorities change. Any modern solution for data management must use this dynamic approach.

Logical data warehousing empowers data consumers: The LDW technique makes it easier to access data, thus empowering users of all skill levels. By combining all data sources, including streaming sources, into a single comprehensive “logical” source, the logical data warehouse can increase the productivity of all users. This allows shared access to data throughout the organization, enabling various business teams to conduct independent assessments. As a result, businesses are better positioned to decide thanks to a shared understanding of their data across all teams and departments.

The logical data warehouse has become even more essential due to the data boom and the wide range of data that is now readily available. It offers technology or tool that allows for the collection and consolidation of all the data within an organization, including historical data, as well as the performance of unified analysis that no one system could conduct independently. Utilizing dependable and reusable data services is made possible by LDW for a wide range of data users. Self-service analytics are made possible by democratizing access to an organization’s data in this fashion, ensuring that the data a business uses is reliable and consistent.

Final Word

The concept of data virtualization has been expanded to enable logical data warehouses in recent years. The logical step for organizations is LDW, which extends data virtualization to integrate, manage and govern enterprise data across a hybrid, multi-cloud environment.