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What is an enterprise data warehouse, or EDW?
An enterprise data warehouse (EDW) is a database, or collection of databases, that centralizes a business’s information from multiple sources and applications, and makes it available for analytics and use across the organization. EDWs can be housed in an on-premise server or in the cloud.
The data stored in this type of digital warehouse can be one of a business’s most valuable assets, as it represents much of what is known about the business, its employees, its customers, and more.
Advantages of organizing an EDW
The maintenance of an enterprise data warehouse solution is advantageous to an organization for many different reasons. Commonly, this kind of data collection and storage is thought of from a marketing or customer relations perspective, and that is certainly one piece of the puzzle.
That is not the only utility of a data warehouse, however. It can also help to make sense of seemingly random pieces of data which are coming into the organization through various inputs, and it can save valuable time by aggregating that information automatically. Organizations are likely to be better positioned for future growth when their data is organized in such a systemic, automated fashion.
Structuring your data
Even for businesses which operate on a relatively small scale, organization and structure are extremely important when it comes to building and maintaining an EDW.
In order for the data to be useful, it has to be stored in a logical, consistent manner. Knowing where you can look for what data, and be sure that the data returned is accurate, is a huge part of the task.
What the data warehouse is good for … and what it’s not
To build a quality EDW, a system of “extract, transform, load” (ETL) is often put into place. ETL’s popularity is owed to the fact that it can help organizations create and manage an enterprise data warehouse successfully. However, as data volumes began to grow in the 2000’s, a trend emerged to leverage the database for more scalable data integration -- leading to “ELT” -- where data was Extracted (from the source applications), Loaded (into the EDW) and then Transformed (within the EDW).
Leveraging the EDW for heavy data transformation can have unintended consequences including greater costs and complexity, as well as processing bottlenecks and missed SLAs that cause business users to wait days, weeks, or even months for the reports they need.
In order to achieve the original intent of the Data Warehouse -- better analytics and business intelligence -- and the original intent of “ELT” (more scalability), many companies are using Big Data distributed frameworks like Hadoop MapReduce and Apache Spark plus ETL tools specifically designed for these Big Data environments. This frees up the Data Warehouse to do what it’s intended to do, deliver more timely insights and can drive down costs.
Precisely, a pioneer in the Big Data software market, offers high-performance data integration software that was built to run natively in Hadoop and Spark. Their products and experts have helped some of the largest organizations in the world get the most out of their EDW by shifting the ELT/ETL processing to Big Data frameworks.
How Precisely can help
Precisely offers data integration and data quality solutions to help you manage your enterprise data warehouse.
- Precisely Connect can help you optimize and offload your data warehouse.
- Spectrum Data Federation helps you access, integrate, virtualize, and synchronize data from a wide variety of applications and systems for more effective business intelligence, analytics, customer experience and operational efficiency.