Data stores are more important for storing, retrieving information and integrating with big data tools for analytics and insights. Depends on the functionality, there are different forms like data warehouse, data lakes, data swamps, data mart, data cubes. Let’s see the difference between each of these terms.
Data Warehouse :
Data warehouse is a structured repository which is designed to store large amount of data from data can be directly processed and analyzed. Data from different sources can be ingested and it can feed directly into processing or analytics layers.
Data Lakes :
Data lakes preserve original form of data. It can be structured or unstructured. It supports collecting data from multiple sources like social media, logs, sensors. They can store data as long as they are required by leveraging scale out, typically hdfs or s3.
Data mart :
Data marts are typically are subset of data warehouses, designed to serve for a particular segment or business unit like sales, marketing, finance.
Data Swamp :
Data lakes can accept any data without any structure. When it is poorly designed like without governance or not maintaining metadata it becomes data swamp. Data swamp describes the failure to the stored data like inability to analyze or not using it efficiently. Actual data may remain, but without metadata data swamps cannot retrieve it.