What Is an Enterprise Data Warehouse (EDW)?

A one-tier enterprise data warehouse architecture represents the most basic structure, characterized by direct user interaction with the central data warehouse. This approach is typically suitable for organizations with relatively simple analytical needs and smaller data volumes.

A two-tier enterprise data warehouse architecture introduces an intermediary layer: data marts, which contain subsets of the main data warehouse. By offloading analytical workloads into data marts, this architecture enhances performance and reduces the strain on the central data warehouse. Overall, a two-tier architecture is best-suited for organizations with diverse and demanding analytical requirements.

A three-tier enterprise data warehouse architecture incorporates online analytical processing (OLAP) cubes, which enable users to conduct more in-depth, interactive analyses from various perspectives. The three-tier architecture offers the highest level of analytical sophistication.

A combination of hardware and software that offers organizations complete control over their data environment. Significant upfront investments are required for hardware, software and infrastructure. Additionally, maintaining an on-premises data warehouse necessitates dedicated IT staff to handle ongoing maintenance and updates. Scaling the infrastructure to accommodate growth can also be a time-consuming and expensive endeavor.

A single point of access to multiple data sources, which can help simplify data access and improve data consistency across systems. It can also eliminate the need to replicate data into multiple sources. However – especially for complex queries – performance can be impacted. Additionally, the ease of use and accessibility of a virtual data warehouse depends on the underlying systems.

A more cost-effective option, often with pay-as-you-go pricing. Cloud providers typically handle infrastructure maintenance and updates, which can drastically ease the burden on the organization itself. Cloud-based data warehouses are also highly scalable and can help facilitate faster time to market since resources can be rapidly deployed or provisioned. However, reliance on cloud providers can lead to vendor lock-in, and security concerns may arise due to reliance on the cloud provider's security measures. Network latency can also potentially impact performance.

A custom combination of on-premise and cloud-based solutions that enables organizations to control costs and control their data. A major benefit is enhanced disaster recovery, since data can be preemptively duplicated across multiple locations. Managing both on-premises and cloud infrastructure can increase management overhead and complexity. Ensuring seamless data integration between on-premises and cloud systems can also present challenges. A custom combination of on-premise and cloud-based solutions that enables organizations to control costs and control their data. A major benefit is enhanced disaster recovery, since data can be preemptively duplicated across multiple locations.