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.
Generative AI is causing excitement but not success for most enterprises. This needs to change quickly, but it will take some work that enterprises may not be willing to do.
This issue showcases practical AI deployments, implementation strategies, and real-world considerations such as for data management and AI governance that IT and business leaders alike should know before plunging into AI.
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.
AI and other forces are lessening the gravitational pull of public cloud platforms. This trend might be good for enterprises.
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