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Data virtualization unifies data for seamless AI and analytics

IBM Journey to AI blog

Data integration 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?

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Top Predictive Analytics Tools/Platforms (2023)

Marktechpost

Predictive analytics uses methods from data mining, 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.

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A brief history of Data Engineering: From IDS to Real-Time streaming

Artificial Corner

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 data mining techniques, the rise of big data, and the evolution of data storage and processing technologies.

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Exploring the Power of Data Warehouse Functionality

Pickl AI

Let’s explore some key features and capabilities that empower data warehouses to transform raw data into actionable intelligence: Historical Data Integration 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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