Data cleanup, or data rubbing, is the process of detecting and correcting or removing data or records that are incorrect from a database. It also includes correcting or removing unformatted or duplicate data or records. The data removed in this process is often referred to as “dirty data”. Data cleaning is a necessary process to protect data quality. Large businesses with extensive datasets or assets typically use automated tools and algorithms to detect such records and correct common errors (such as missing zip codes in customer records).
The most powerful big data circles have rigorous data cleanup tools and processes to ensure that data quality is protected and trust in datasets is high for all types of users.
No code is the next step in the stable completion of the development of intelligent software and application solutions. The development method also allows process specialists who develop without using code but have no IT background or expertise to design applications and software.
Latent space refers to a multidimensional space in the background of AI and machine learning models, representing the deeper relationships of data.
The public cloud is the cloud model offered by cloud service providers, where users share a common infrastructure. This service model reduces the technological workloads of companies on a large scale and offers a flexible infrastructure.
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