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.
Cloud-Native Data Platforms are data management platforms designed and optimized to work directly in cloud environments. These platforms take full advantage of the flexibility, scalability, and cost advantages of the cloud unlike traditional data infrastructures.
Penetration is a term often used to mean penetrating or entering something.
Bayesian Networks are one of the most widely used types of probabilistic graphical models. Providing effective solutions for decision making and inference under uncertainty, these networks play a critical role in artificial intelligence, machine learning and data analysis.
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