The fundamental goal of data integration in data science and machine learning is to merge data from diverse sources into a unified and comprehensive dataset, enabling comprehensive analysis and modeling.

Data latency, which refers to the delay in data transmission or processing, is not typically considered a direct challenge in data integration. However, data heterogeneity, redundancy, and security are common integration concerns.

Data harmonization involves converting data from various sources into a uniform format, ensuring consistency in data structure, units, and semantics.

Data warehousing is a centralized approach where data from different sources is extracted, transformed, and loaded into a single repository, enabling efficient data storage and analysis.

Data lakes provide simplified data access by allowing users to store and process data in its raw format, eliminating the need for extensive data transformation and harmonization.

Data federation enables data integration by creating a virtual layer that provides a unified view of data from multiple sources without physically moving or copying the data.

Data deduplication involves identifying and removing duplicate or inconsistent data records from a dataset, ensuring data integrity and accuracy.

Data virtualization is well-suited for real-time data processing and analysis as it provides a unified view of data from multiple sources without the need for physical data movement or transformation.

Data transformation involves converting data from its original format into a format that is suitable for analysis and modeling, often involving data cleaning, aggregation, and feature engineering.

Data lakes are designed to store and process both structured and unstructured data, making them suitable for integrating data from diverse sources.

Data validation involves checking data for accuracy, consistency, and completeness, ensuring that it meets specific quality standards.

Data federation is commonly used to integrate data from multiple applications or systems by creating a virtual layer that provides a unified view of the data without physically moving or copying it.

Data normalization involves converting data into a consistent format, often by removing duplicate values and ensuring that data is stored in a structured manner.

Data federation is particularly suitable for integrating data from multiple sources that are geographically dispersed, as it allows data to be accessed and processed without the need for physical data movement.

Data integration involves combining data from multiple sources into a single, comprehensive dataset, enabling comprehensive analysis and modeling.