![]() You may know where everything is in the pile, but if you want to work with others you’ll need to organize it. To illustrate the difference, imagine all of your inventory is under one roof, but in a big pile. What differentiates a Data Warehouse from a Data Lake, or other source, is that the Data Warehouse will provide a cleaner view of the data and is easier for users to query. When organizations have an initiative to empower users outside of the data science or engineering team to leverage data, they will move to a Data Warehouse. Data is inconsistent and unstructured, so it can be error-prone with users using the wrong columns or calculating metrics incorrectly. When the amount of data in your Data Lake reaches a level of complexity with irrelevant, unstructured data, it will be too confusing and messy for non-data analysts to use. Typically, organizations reach roadblocks in making sense of the data in their Data Lake. Before we dive in deep, let’s look at the data issues you face with a Data Lake. This section of the Data Governance book will explain why you should create a Data Warehouse, and how to implement it so that you get all the benefits it can deliver to your business. Queries don’t affect app performance, and aren’t affected by rapid changes in the data.
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