What are the differences between databases, warehouses, and lakes?

Databases

Databases are collections of structured data stored and accessed using predefined queries. They are usually used to store and retrieve data relating to some kind of business transaction or application. A database typically contains related information organized in tables and associated with key fields, to facilitate easy access and retrieval of specific data. These tables are closely monitored and managed by database administrators, who update and protect the information they contain.

Data Warehouses

A data warehouse (DW) is a central repository for all the data collected from a series of sources. This data is transformed into a more useful form, and organized in a way that facilitates decision-making and data analysis. Data warehouses are commonly implemented as a layer between a source of data and a decision-making platform. In other words, the data warehouse helps to filter and transform raw data into more meaningful and organized information. This information is then delivered to applications and business intelligence (BI) systems.

Data Lakes

Data lakes are massive repositories of unstructured data. Unlike databases and data warehouses, they are not rigidly structured, allowing them to collect multiple types of data from disparate sources, in any format. This makes it easier to capture variety of data, as well as unstructured data such as web analytics, images, and machine logs. Furthermore, data lakes are readily scalable and flexible, meaning that more data can be added as needed. This ensures that a data lake can be used for long-term storage of your ever-growing data collection. Overall, data lakes may bring both structure and analytics to unstructured data, making them a great choice for deep insights.