Data Lake vs Data Warehouse vs Data Mart: características y beneficios

And so it is, data is the oil of this era, but there are many ways to store and analyze this data. If the organization chooses poorly among the alternatives, it could lead to a very costly problem. [Music]

Hello, it's me at 30, and this is also 3 News. Don't forget to subscribe to the channel. Today we're going to talk about data warehouses, data lakes, and data marts: what they are, what they're for, and how they should be used. In general terms, an organization's data is generated by different systems such as CRM, ERP, among many others. But

to go from being just information to generating business intelligence, it must be centralized in one place from which all kinds of data analysis can be run to discover trends that help in decision-making. That place can primarily be a data lake or a data warehouse. In a data mart, all forms of data generated

throughout the company are stored. This includes structured data sources, conversation logs, emails, images, audio, videos, and practically anything you can think of. Therefore, this results in a very large amount of accumulated information. All very nice on paper, but data lakes handle a quantity The overwhelming volume of information is so high that traditional databases

can take days to process a single request. Therefore, specialized hardware and significant investments in storage are inseparable in data lakes. On the other hand, unlike the dark depths of unstructured data in a data lake, a data warehouse has clean shelves and organized data for much faster information retrieval. However, it's not just any data; data warehouses typically

store information that has already been structured. And what about data marts? This third analysis and storage strategy is often considered a subsection of data warehousing or used for a very specific departmental need. Unlike a data warehouse and a leit, where information is stored in a single, centralized file, data marts have a distinct and