This article gives you a look into the crucial comparisons between Cloud and AWS, evaluating compute services, storage options, support, and costing options.
This article discusses how to optimize Snowflake on AWS with advanced storage, compute, and query efficiency techniques with best practices and SQL examples.
Table-augmented generation (TAG) and LOTUS bridge AI and databases, enabling complex queries using LLMs. They address the limits of Text2SQL and RAG models.
Streaming SQL enables real-time data processing and analytics on the fly, seamlessly querying Kafka topics for actionable insights without complex coding.
Scalability ensures systems handle growth in traffic or data while maintaining performance. This can be achieved via vertical scaling or horizontal scaling.
Utilizing AWS SageMaker and Glue to create a fraud detection system using ETL, deep learning, and XGBoost for scalable, efficient, and accurate results.
The journey of a cloud incident that transformed fragile Liberty microservices into a resilient, self-healing system that scales effortlessly under load.
API management as code is a declarative approach to managing APIs at scale, providing benefits like automation, consistency, collaboration, and scalability.
Materialized views enhance data streaming by improving incremental computation, enabling efficient retrieval and calculation of aggregated or pre-processed data.
Micronaut is efficient, lightweight, and fast, making it a strong alternative, but Spring Boot remains dominant due to its robust, mature ecosystem and community support.
Handle embedded data in NoSQL with Java using Jakarta NoSQL. Compare flat vs. grouped structures using @Embeddable to optimize document storage and querying in MongoDB.