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.
AI-Assisted Analytics is an advanced analysis method performed through the use of artificial intelligence and machine learning algorithms in data analysis processes. This technology has the ability to detect complex relationships in large datasets, identify patterns, and make predictions for the future.
Domain Driven Design allows us to rethink the software development process by centralizing the business area. It focuses on a deep understanding of the business problem we are trying to solve before the technical details of the software and places this understanding at the heart of software design.
Automated machine learning, called AutoML (Automated Machine Learning) in the field of artificial intelligence and machine learning, describes integrated software platforms for the creation, training and optimization of a machine learning model.
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