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Dataintegration stands as a critical first step in constructing any artificial intelligence (AI) application. While various methods exist for starting this process, organizations accelerate the application development and deployment process through data virtualization. Why choose data virtualization?
Predictive analytics uses methods from datamining, statistics, machine learning, mathematical modeling, and artificial intelligence to make future predictions about unknowable events. It creates forecasts using historical data. A data lake and a data warehouse are integrated into the Lakehouse Platform.
The advent of relational databases and data warehouses in the 1970s and 1980s set the stage for the next wave of advancements in data engineering, including the development of datamining techniques, the rise of big data, and the evolution of data storage and processing technologies.
Let’s explore some key features and capabilities that empower data warehouses to transform raw data into actionable intelligence: Historical DataIntegration Imagine having a single, unified platform that consolidates data from all corners of your organization – sales figures, customer interactions, marketing campaigns, and more.
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