Hot data is the frequently accessed data, while cold data is the one you seldom visit but still need. Separating them is for higher efficiency in computation and storage.
Model Ops is a collection of procedures and equipment for managing and running models that are in use. ML teams deploy each model in production with the DevOps team.
Building an event-driven architecture using Kafka enables real-time data streaming, seamless integration, and scalability for applications and systems.
A real-time analytics database called Apache Druid can be leveraged very effectively where real-time ingestion, fast query performance, and high uptime are crucial.
Introducing the Metadata and Config-Driven Python Framework for Data Processing with Spark that offers a streamlined and flexible approach to processing big data.
Gain a comprehensive understanding of data warehouse tools and their importance in development. Explore key features, benefits, and considerations for developers in this comprehensive overview.
Introducing multi-tenancy architecture in MQTT offers a new choice. What is multi-tenancy architecture in MQTT and its benefits and challenges to users?
CORE
