Data gravity occurs when the volume of data in a warehouse increases and the number of uses also increases. In some cases, copying or moving data can be troublesome and expensive. Therefore, data tends to pull services, applications and other data into its warehouse. Primary examples of data gravity are data warehouses and data lakes. Data in these systems is inert. Scalable volumes of data often break existing infrastructure and processes, requiring risky and expensive fixes. Therefore, the best practice is to move design processing to data, not the other way around.
The data lake is where long-term data containers gather that capture, clean, and explore any raw data format at scale. Data subsets are powered by low-cost technologies that many downstream possibilities can benefit from, including data warehouses, and recommendation engines.
A relational database consists of tables that are related to each other, and each table contains data of a specific data type - an entity. The relational model defines reality and usually has as separate tables as the number of entities. A relational database attempts to display all data items only once.
Master Data Management (MDM) provides a unified view of data across multiple systems to meet the analytics needs of a global enterprise. Whether MDM identifies customers, products, suppliers, locations, or other important attributes, MDM creates single images of master and reference data.
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