The primary difference between a data warehouse and a transactional database is that the underlying table structures for a transactional database are designed for fast and efficient data inserts and updates (it’s all about getting data into the database). The purpose of this post is to help the reader understand the differences between a data warehouse versus a traditional, transactional database. Many organizations don’t have a clear understanding of how a data warehouse is different from the various transactional databases that power things like CRM, ERP, Accounting, and other core infrastructure within an organization’s data landscape. The stored data is both historical and current, and supports analytical reporting, executive dashboards, “self service BI”, and data science. In the context of business intelligence, a data warehouse is a core repository that serves as a “single version of truth” that integrates and rolls up data across various data sources within an organization. For many folks, they just can’t understand why they need to build yet another database and start feeding it data. While the concept of a data warehouse is not new….I’ve been implementing them for nearly 30 years now….It still blows me away that we often find ourselves in a position where we are helping our client build consensus in the organization around the concept of a data warehouse and the overall business case. Finance/Accounting, Operations, Sales/Marketing, C-Suite, etc., are now able to produce reports and analytics from a single version of the truth.Īt the heart of these solutions is the data lake/data warehouse. This approach not only provides a richer data set for data science work, it automates a ton of manual processes required to cleanse and stitch the data together, and allows for operational Business Intelligence (BI) and self service reporting. In nearly every case, we found that there was a lack of data management infrastructure necessary for doing serious analytical work and they would be better served and able to address a wider set of stakeholders in the organization by tying together their various data sources into an integrated data lake/data warehouse as a first step. Over the past 24 - 36 months, the majority of our long term client engagements have started with our client wanting to address what they perceived to be a “data science” or big data problem.
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