Large language models are becoming more and more popular. However, their development also makes us face certain challenges. GPT models aren't the only approach.
Success stories from data streaming journeys with Apache Kafka across banking, retail, insurance, manufacturing, healthcare, energy, and software companies.
Learn how to debug mobile app database problems and optimize storage performance with monitoring, optimization techniques, and efficient data retrieval methods.
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.
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