From the use cases and examples presented above, it’s evident that data integration and ETL are closely related concepts. In fact, ETL can be thought of as a subset of data integration.

AI can improve data integration by automating and quickly accomplishing what used to be manual or time-consuming tasks.

Data practitioners come across many challenges throughout the data management lifecycle. Let us learn about the most common day-to-day challenges we face and how to overcome them.

Using the hub-and-spoke model of a centralized data hub, businesses can easily achieve their real-time data goals, says Hanadi Salameh, an Enterprise Digital Architect at Cognizant.

Even as bioprocessors collect ever more data and analyze it with AI-based methods, the industry is challenged by data integration.

Jakarta Data simplifies data integration in Java apps, supports polyglot persistence, and unifies Jakarta EE technologies. Jakarta Data is open-source and versatile.

The coolest data management and integration tool companies of the 2025 Big Data 100 include Informatica, Airbyte, Confluent and dbt Labs.

Scientific data integration entails making choices about the representation of data (for example, tables, matrices or graphs), each of which affords different integration and analysis techniques ...

Early integration of this data during the military decision-making process provides commanders with a holistic view of personnel, supply status, and maintenance requirements.