Why Lifecycle Data Management is Critical to Get the Most Out of Streaming Edge Data
Making the most of streaming edge data requires a special infrastructure architecture that accommodates the data throughout its lifecycle.
Topic: Big Data
Big Data technologies and use cases for real-time analytics. Big Data technologies, market insights, and use cases for real-time analytics.
The Four Trends Driving Efficiencies and Business Change in 2023
When addressing business change and disruption, these four trends can transform how organizations operate, communicate, and make crucial business
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 …
What Is a Data-Informed Organization?
In data-informed organizations, AI/ML and workflow orchestration function as a kind of intelligent research assistant, operating at scale without interruption.
Where Is Our Data? Why Data Location Discovery Is Hard
Data location discovery is the major initial hurdle of achieving true data security; without discovery, detection of sensitive data and remediation of their …
Apache Kafka and RabbitMQ: Which Open Source Platform Will Fit My Needs?
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
Tapping into the Potential of Data Products in 2023
A data mesh architecture treats data-as-a-product as the new way to empower each stakeholder or domain to unravel insights from data.
Architectural Elements for Streaming Edge Data Analysis
Working with streaming edge data requires a solution with multiple elements to ingest, store, and perform real-time stream analysis.
Operationalizing Data and Analytics
Organizations need a data analytics platform and data analytics tools that assist throughout the entire journey from data ingestion to insights.
Data Quality Remains Biggest Detriment To AI Success
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