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.
Business intelligence (BI) is the process and methods that enable organizations to generate more meaningful results and make data-driven decisions using tools such as data mining, data visualization, business analytics on existing data so that they can make better decisions.
Explore the evolving world of Data Warehouse Modernization and its importance in leveraging big data. Learn how data warehouses work, their types, requirements in various industries, and application areas.
Veritabanı sistemlerinin temelinde yer alan Veri Tanımlama Dili (Data Definition Language - DDL) veri yapılarının oluşturulması, değiştirilmesi ve silinmesine olanak tanıyan komutlar bütünüdür.
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