Data integration is the process of combining data from different sources into a unified view. This involves moving data between databases, whether they are internal, external, or both. The goal is to create a cohesive dataset that can be used for analysis, reporting, and decision-making. However, the process is more complex than it may seem, as it requires careful consideration of various factors, including the source systems and the methods used for integration, such as ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) ².

In the context of big data, traditional data integration methods have evolved to accommodate the unique challenges posed by large volumes of structured and unstructured data. This has led to the development of new tools and techniques that support real-time processing and ensure data quality across the enterprise ⁴. As the data landscape continues to change, the integration space is becoming increasingly diverse, with a growing number of niche players and open-source solutions emerging ³.

If you have more specific questions about data integration, feel free to ask!