Data integration is a family of techniques, most commonly including ETL (extract, transform, and load), data federation, database replication, data synchronization, sorting, and changed data capture.

Data integration and data ingestion are two IT disciplines that are often confused. Here's how they differ and challenges you may encounter.

If you're considering using a data integration platform to build your ETL process, you may be confused by the terms data integration and ETL. Here's what you need to know about these two processes.

Renewable energy technology portfolios are becoming increasingly complex with multiple asset types like solar, wind and ...

Here are three steps toward data integration for good decision-making. 1. Create One Version Of The Truth. You need to start by identifying what you want to use the data for.

Modern data integration tools differ from previous ETL (extract, transform, load) processes. They are able to ingest and process data as it comes in, and they can work with structured and ...

A modern data integration strategy is critical to support the new generation of data and analytics requirements, including support for real-time customer 360, data intelligence, and modern edge ...

The proliferation of data sources, types, and stores is increasing the challenge of combining data into meaningful, valuable information. The need for faster and smarter data integration capabilities ...

Data Automation: Through integration with Dropbox and Box, ChatGPT can automate routine tasks such as file organization, tagging, and metadata management, liberating human resources for more ...

Even as bioprocessors collect ever more data and analyze it with AI-based methods, the industry continues to face a crucial hurdle—data integration. “In most industrial biotech companies or ...