Data Management and AI: Creating Value at the Edge
By applying edge-specific capabilities and AI integration at the data source, businesses can eliminate inefficiencies, gain greater operational awareness, and …
Big Data technologies and use cases for real-time analytics. Big Data technologies, market insights, and use cases for real-time analytics.
By applying edge-specific capabilities and AI integration at the data source, businesses can eliminate inefficiencies, gain greater operational awareness, and …
In data-informed organizations, AI/ML and workflow orchestration function as a kind of intelligent research assistant, operating at scale without interruption.
Data location discovery is the major initial hurdle of achieving true data security; without discovery, detection of sensitive data and remediation of their …
By understanding the particular strengths of RabbitMQ and Apache Kafka, you can ensure that you’re using the right message queue platform for your use
A data mesh architecture treats data-as-a-product as the new way to empower each stakeholder or domain to unravel insights from data.
Working with streaming edge data requires a solution with multiple elements to ingest, store, and perform real-time stream analysis.
Organizations need a data analytics platform and data analytics tools that assist throughout the entire journey from data ingestion to insights.
The quality of data coming through the pipeline remains one of the key impediments to AI success, but it looks like businesses are more aware of data
Bill Schmarzo, the Dean of Big Data, talks about the challenges of managing streaming data and how businesses can monetize data by having the right data …
Companies using vectorized databases can derive actionable insights from their data streams in a time frame to take immediate actions as events occur.