Anonymizing data involves removing personal identifiers to preserve privacy and enable businesses to use data without compromising compliance or security.
Edge Machine Learning enables devices to perform AI tasks locally, ultimately reducing latency, enhancing data privacy, and enabling real-time decision-making.
All data, any data, any scale, at any time: Learn why data pipelines need to embrace real-time data streams to harness the value of data as it is created.
Explore how the evolution of data pipelines in recent years requires the adaptation of organizations, teams, and engineers to use full potential of technology.
Databricks introduced Liquid Clustering at the Data + AI Summit, a new approach that enhances read and write performance by optimizing data layout dynamically.
This article explores ACID transactions in MongoDB. We break down how MongoDB ensures data integrity and consistency by supporting multi-document ACID transactions.
The Backend for Frontend (BFF) design pattern customizes backend services for specific frontends, enhancing efficiency and alignment with frontend needs.
A phased approach focused on quick wins and culture change to connect clinical data, architect it for IT, migrate systems to the cloud, and leverage AI to improve care.
Learn about modern data backup strategies for businesses, including cloud-based backups, data deduplication, blockchain for data integrity, and AI integration.
Optimizing digital applications involves Prefetching, Memoization, Concurrent Fetching, and Lazy Loading. These techniques enhance efficiency and user experience.