A database is typically structured with a defined schema so structured data can be fit in a database; items are organized as a set of tables with columns and rows, and columns indicate attributes, and rows indicate an object or entity. It has to be structured and filled in here within all these rows and columns. Columns represent attributes, and rows refer to an object or entity. The database is designed to be transactional and generally not designed to perform data analytics. Some examples are Oracle, MySQL, SQL Server, PostgreSQL, MS SQL Server, MongoDB, Cassandra, etc. It is generally used to store and perform business functional or transactional data.
Data warehouse exists on top of several databases, and it is used for business intelligence. Data warehouse gathers the data from all of these databases and creates a layer to optimize data to perform analytics. It mainly stores the processed, refined, highly modeled, highly standardized, and cleansed data.
A data lake is a centralized repository for structure and unstructured data storage. It can be used to store raw data as it is without any structure schema. There is no need to perform any ETL or transformation job on it. Any type of data can be stored here like images, text, files, videos, and even it can store machine learning model artifacts, real-time and analytics output, etc. Data retrieval processing can be done via export, so the schema is defined on reading. It mainly stores raw and unprocessed data. The main focus is to capture and store as much data as possible.
Data Mart lies between data warehouse and Data Lake. It’s basically a subset of filtered and structured essential data of a specific domain or area for a specific business need.
Posted Date:- 2021-10-26 10:53:59
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