What is a Logical Data Warehouse?

A logical data warehouse (LDW) is a data management architecture in which an architectural layer sits on top of a traditional data warehouse, enabling access to multiple, diverse data sources while appearing as one “logical” data source to users. Essentially, it is an analytical data architecture that optimizes both traditional data sources (databases, enterprise data warehouses, data lakes, etc.) and other data sources (applications, big data files, web service, and the cloud) to meet every analytics use case. The term was coined in 2009 and continues to gain traction in the market as data complexity becomes a growing problem for many companies.

Logical Data Warehouse

The logical data warehouse is being called the next generation of data warehouse with the ability to meet companies growing data management needs. Combining multiple engines and various data sources across the enterprise, logical data warehouse components can be combined in one place logically instead of physically. The modern LDW has advanced to support today's wide variety of available data sources, data platforms, and business use cases. It helps organizations digitally reinvent, enable real-time streaming analytics, and optimize operations with smarter, data-driven decision making.

What are the benefits of a logical data warehouse?

Meet Evolving Data Demands

The logical data warehouse approach allows companies to meet evolving data requirements while taking advantage of existing investments in physical approaches such as data warehouses, data marts, sandboxes, data lakes, and others. As a multi-engine approach, the logical data warehouse allows businesses to meet all of their varying analytics requirements. It’s important to note that these different components (enterprise data warehouses, data lakes, data marts, etc.) are not mutually exclusive and can actually complement each other in a strategic data management approach. The enterprise data warehouse (EDW), for example, is not gone. It still serves useful for managing a business’s data is just now a part of the greater whole—the logical data warehouse.

The logical data warehouse ensures that your analytics strategy is agile and flexible for new data demands. It keeps your team from being locked into one technology or approach no matter how the market shifts in the future. This goes back to the complimentary design of the different components mentioned earlier. Companies can make decisions about which components to use for different data management tasks to meet their requirements. As the business grows and new data is generated, the data virtualization layer can incorporate these new data sources without disrupting any existing processes.

Modernize Your Data Approach

A logical data warehouse allows companies to modernize their data approach and analytics architecture by deploying a common analytic data management architecture across all of their diverse data types, technologies, users, and use cases. The logical data warehouse enables a company to answer questions about the business, analyze past performance, and predict future outcomes by incorporating all of its data across various sources. Also, LDW can help a business scale its data management strategy as it grows, starting with its current data and easily adding or changing design as priorities change. This dynamic approach is key for any modern data management solution.

Empower Data Consumers

The LDW approach also helps empower users of varying skill levels by making data easier to find and to understand. The logical data warehouse can improve the productivity of all users by integrating all data sources, including streaming sources, into one comprehensive, “logical” source. This allows for shared access to data across an entire organization, allowing different business teams to do their own analyses. In turn, businesses are able to make better decisions based on a consistent understanding of its data across every department and team.

With an increasingly diverse variety of data available, the logical data warehouse has become even more necessary since its creation. It provides one technology or tool to collect and consolidate all of an organization's data, including historical data, and perform unified analyses that no one system could do alone. LDW grants many different data consumers the ability to use trusted and reusable data services. By democratizing access to an organization’s data in this way, it allows for self-service analytics while ensuring consistency and accuracy in the data used by the business.

Common Characteristics of a Logical Data Warehouse

As the logical data warehouse has evolved since 2009, its fundamental purpose has not changed, but its key characteristics have grown and adapted to enterprise requirements. Modern LDW tools now usually include the following characteristics:

  • Application access through a single interface
  • Existing enterprise data warehouse remains
  • Contains one or more data lakes as repositories
  • Uses an operational data store (ODS)
  • Ensure consistency with data marts
  • Set metadata and governance policies

Logical Data Warehouse Use Cases

Almost any business or industry could benefit from connecting all of its data and allowing access across the organization for better analysis and decision making. The following are just a few of the use cases where LDW can be applied:

  • Risk management
  • Monitoring KPIs
  • IoT edge analytics
  • Predictive analytics
  • Data mining
  • Self-service analytics