Java Web Development (JSP/Servlets) Services

In modern DevOps workflows, automating the build-test-deploy cycle is key to accelerating releases for both Java-based microservices and an Angular front end. Tools like Jenkins can detect changes to source code and run pipelines that compile code, execute tests, build artifacts, and deploy them to environments on AWS. A fully automated CI/CD pipeline drastically cuts down manual steps and errors. 

As one practitioner notes, Jenkins is a powerful CI/CD tool that significantly reduces manual effort and enables faster, more reliable deployments. By treating the entire delivery pipeline as code, teams get repeatable, versioned workflows that kick off on every Git commit via webhooks or polling.

Testing GET requests is a fundamental part of API automation, ensuring that endpoints return the expected data and status codes. With REST Assured in Java, sending GET requests with query and path parameters, extracting data, verifying the status code, and validating the response body is quite simple.

This tutorial walks through practical approaches to efficiently test GET APIs and build reliable automated checks, including:

Apache Spark 4.0 represents a major evolutionary leap in the big data processing ecosystem. Released in 2025, this version introduces significant enhancements across SQL capabilities, Python integration, connectivity features, and overall performance. However, with great power comes great responsibility — migrating from Spark 3.x to Spark 4.0 requires careful planning due to several breaking changes that can impact your existing workloads.

This comprehensive guide walks you through everything you need to know about the Spark 3 to Spark 4 migration journey. We'll cover what breaks in your existing code, what improvements you can leverage, and what changes are mandatory for a successful transition. Whether you're a data engineer, platform architect, or data scientist, this article provides practical insights to ensure a smooth migration path.

As agentic AI shifts from prototypes to enterprise production, Java emerges as a powerful alternative to Python-centric stacks. This article looks into building robust agentic applications using LangChain4j for orchestration, Quarks for high-performance deployment, Model Context Protocol (MCP) for standardized tool and data access, and OpenTelemetry for comprehensive observability. Through practical code examples — including tool definitions, agent creation with memory, RAG integration, and production patterns — the guide demonstrates Java's advantages in type safety, low-latency execution, deep system integration, and audit-ready tracing. This is ideal for developers seeking scalable, reliable agentic solutions in mission-critical environments.

Agentic AI — autonomous systems that reason, plan, use tools, remember context, and execute complex multi-step tasks — is moving from experimental prototypes to production workloads in enterprises. While Python ecosystems (LangChain, LlamaIndex, CrewAI) led the early wave, Java is emerging as a serious contender for mission-critical agentic applications.

Modern APIs often need to support dynamic filtering, sorting, and pagination without creating dozens of custom endpoints. One of the most widely used standards for this is OData (Open Data Protocol). OData has established itself as a powerful standard for building and consuming RESTful APIs. It provides a uniform way to query and manipulate data, offering clients unparalleled flexibility through system query options like $filter, $select, and $expand.

Example: