Big Data – codewindow.in

Introduction: 

Data denormalization is the process of adding redundant data to a database to improve data retrieval times. It involves storing data in multiple tables to avoid complex join operations, which can be time-consuming and resource-intensive.

Specification: In the context of Big Data, data denormalization is critical because of the large amount of data that is generated and stored. Big Data sources can include structured and unstructured data from various sources, and processing this data can be time-consuming and resource-intensive.

Implementing data denormalization can help organizations improve data retrieval times and reduce processing costs by eliminating complex join operations. It can also help improve system scalability by enabling faster access to data.

Data denormalization can be achieved through various techniques such as horizontal and vertical denormalization, depending on the type and structure of the data.

Overall, data denormalization is essential in Big Data because it enables organizations to process large datasets more efficiently, leading to faster data processing and analysis. It can also help improve system scalability and reduce processing costs by eliminating complex join operations. Without proper data denormalization measures in place, Big Data processing can be significantly impacted, leading to inefficiencies and increased costs. However, it should be noted that data denormalization can also introduce data redundancy and potential data consistency issues, so it should be used judiciously and with careful consideration.