Data gravity occurs when the volume of data in a warehouse increases and the number of uses also increases. In some cases, copying or moving data can be troublesome and expensive. Therefore, data tends to pull services, applications and other data into its warehouse. Primary examples of data gravity are data warehouses and data lakes. Data in these systems is inert. Scalable volumes of data often break existing infrastructure and processes, requiring risky and expensive fixes. Therefore, the best practice is to move design processing to data, not the other way around.
The logical analysis platform is a feature-rich technology solution designed to meet the needs of large enterprises.
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.
Dirty data refers to data that is wrong for a company. This inaccuracy not only means that the data is not correct, the correct data can also be “dirty”.
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