Using AI tools to help design, develop, modify, and deliver a microservice application requires the collaboration of stakeholders, SMEs, developers, and DevOps.
This article series addresses commonly asked questions, best practices, practical examples, and info on how to get started with event-driven architectures.
Market disruptors pave the way for innovation and break barriers once considered bulletproof. PuppyGraph uses market disruption and graph data to detect fraud.
Explore essential strategies for securing cloud environments, focusing on IAM, encryption, automation, and proactive monitoring to mitigate cyber threats.
Learn more about tokenization and embeddings, which play a vital role in understanding human queries and converting knowledge bases to generate responses.
Learn how to build generic, easily configurable, testable reactive consumers, producers, and DLT with Kotlin, Spring Boot, WebFlux, and Testcontainers.
DAO focuses on database operations (insert, update, delete), while Repository aligns with business logic in DDD. Learn the differences with a Java example.
The article discusses the need for streaming data processing and evaluates available options. It explains that one size fits all is approach is not appropriate.
Follow an overview of methods like TCP FastOpen, TLSv1.3, 0-RTT, and HTTP/3 to reduce handshake delays and improve server response times in secure environments.
Learn about the design patterns of microservice software architecture to overcome challenges like loosely coupled services, defining databases, and more.
Explore event-driven data mesh architecture, and how when combined with AWS, it becomes a robust solution for addressing complex data management challenges.
Here, explore various techniques for loan approvals, using models like Logistic Regression and BERT, and applying SHAP and LIME for model interpretation.
Set up a Java application with Hibernate, configure NCache as the second-level cache, and test the implementation to see how caching reduces the DB load.
Explore the strengths and limitations of symbolic and connectionist AI as well as the challenges AI faces in replicating human experience and reasoning.