Data Management | News, how-tos, features, reviews, and videos
How Gencore AI enables the construction of production-ready generative AI pipelines using any data system, vector database, AI model, and prompt endpoint.
The Apache Kafka, Apache Flink, and Apache Iceberg communities are developing new ways for engineers to manage data and meet application needs.
How can enterprises secure and manage the expanding ecosystem of AI applications that touch sensitive business data? Start with a governance framework.
Failed AI projects waste time and resources, damage reputations, and stifle innovation. To succeed with AI, put the necessary practices in place to ensure high-quality data.
Dataframes are a staple element of data science libraries and frameworks. Here's why many developers prefer them for working with in-memory data.
Disparate BI, analytics, and data science tools result in discrepancies in data interpretation, business logic, and definitions among user groups. A universal semantic layer resolves those discrepancies.
A significant chasm exists between most organizations’ current data infrastructure capabilities and those necessary to effectively support AI workloads.
Creating a center of excellence to manage generative AI effectively will increase the chances of success throughout your organization. Here’s how to get it right.
Web APIs facilitate the sharing of functionality and data across platforms, opening up new possibilities for business expansion and innovation. Here’s how to take advantage of them.
How high-quality, synthetically designed data sets enable the development of specialized AI models.
Sponsored Links