Spring Cloud Data Flow in action

Spring Cloud Data Flow(SCDF) is a cloud native framework for data processing use cases, from import-export tasks to event streaming and predictive analytics. With SCDF, we can build data integration and real-time data processing using data pipelines that consist of a collection of Spring Boot applications built using the Spring Cloud Stream or Spring Cloud Task micro-service frameworks.

But how does it work? What does SCDF add to your distributed stream and batch data processing architecture? In this session, we will dive deep into the core concepts of this framework, walk through a streaming application and how that is managed within SCDF with Kafka as a message broker, with lots of live coding and demos.

AI Orchestration with Semantic Kernel

Semantic Kernel is an open-source SDK that combines AI services such as OpenAI, Azure OpenAI, and Hugging Face, enabling developers to create AI apps by integrating them with their existing code. It supports Java, Python, and C# and offers connectors for adding memories and AI services to simulate the "brain" of the application. Additionally, AI plugins can be added as the "body" of the app. The SDK simplifies the integration of AI services, allowing developers to leverage recent AI advancements and build intelligent pipelines. This workshop focuses on how to use Semantic Kernel within your existing application and how to build and orchestrate AI applications.

Organizing your cloud-native Java backend

Are you grappling with the complexities of managing multiple microservices and shared libraries in your projects? Do you find yourself constantly in search of standards and rules for optimal repository management and release strategies? Are you looking to adopt a contract-first approach to enhance service interactions and streamline API definition releases and sharing? Furthermore, are you faced with the challenge of effectively managing configuration properties across different environments?

Let me share my experience implementing large-scale projects with Java/Spring backend with multiple DBs and Kafka as the heart of the application(s). Join me as I reveal proven strategies, essential principles, and real-world examples regarding Maven, the GitHub workflow/GitLab pipeline, service-to-service communication standards, the release and consumption of API definitions, and—most importantly—the administration and deployment of functional and technical configuration between cloud environments.

Accelerate Spring App Deployment with Azure Spring Apps

Are you ready to supercharge your enterprise application development? Combining the strengths of the Spring framework, Azure Spring Apps offers an efficient path to production for Spring developers. This session will focus not only on enhancing the developer experience and minimizing infrastructure concerns by eliminating app lifecycle, monitoring, and infrastructure worries, but also on readiness features like polyglot multi-services, rate-limiting, service discovery, centralized config, scaling, monitoring, tracing, secrets management, connectivity, and deployment.

Join us on this deep-dive session to accelerate your Spring app deployment with detailed insights, live coding, and valuable tips and tricks.

GraphQL-ify your APIs

GraphQL is query language for APIs, but what are the advantages and how would one implement such in their microservices/APIs. In this session, I will go through the basics of GraphQL, different aspects of GraphQL and architecture of such APIs. There will be demo/live-coding on, how 4 different ways we can implement GraphQL for a Springboot microservice/API. Lots of examples, live coding and helpful comparison on structure, usage and implementations of GraphQL in Springboot & Java world.

APIs to GraphQL in 16 minutes

It was designed by Facebook to allow its mobile clients to define exactly what data should be sent back by an API and therefore avoid unnecessary roundtrips and data usage, GraphQL is a JSON-based query language for APIs. Since it was open-sourced by Facebook in 2015, there has been a lot of innovation in this area, and now building a GraphQL server is possible in almost all programming languages.
A year ago Netflix open-sourced their GraphQL frame­work, DGS Framework giving GraphQL in the Java microservice world an extra boost. This is an annotation-driven library for Spring Boot.
Join this session to learn how to convert your traditional Spring Boot microservice to a GraphQL server in 16 minutes. Learn tips and tricks and most importantly experience how your APIs become flexible, robust, and future-proof by GraphQL-ify.

[SPONSORED] Are you testing your unit tests?

Unit tests are the de-facto way to validate the code we write. But do you know how good are your unit tests? How much coverage do you have? Do you have tests for all possible scenarios? What if I say with a simple maven plugin you can validate and put a coverage percentage to your unit tests. In this 16 minutes let me take you on a quick journey on pi-test/mutation test and show you how you can easily achieve this on your build pipeline.

RAG-atouille

Retrieval Augmented Generation (RAG), similar to the artistry of Remy’s Ratatouille, combines the brilliance of Large Language Models (LLMs) with the precision of information retrieval. Just as Remy layers flavors in his dish, RAG-fusion seamlessly blends vectorized documents, images, audio, and video to craft nuanced responses in AI-powered applications. The RAG-multi index, like Gusteau’s secret spice blend, optimizes data organization, allowing LLMs to access a rich pantry of knowledge. And much like Anton Ego’s discerning palate, RAG-search ranking ensures that the most relevant insights rise to the top. Vector DB, our culinary laboratory, refines this recipe for a delectable user experience.

This presentation focuses on the latest architectural pattern called Retrieval Augmented Generation (RAG). I’ll start with a beginner-friendly introduction to why RAG is essential. Then, we’ll dive into practical implementation and design considerations, leveraging various data stores—both vectorized and non-vectorized. Finally, I’ll explore different RAG variations, including RAG-fusion, multi-index, and search ranking. Throughout, I’ll share real-world examples from my own experiences working with diverse customers and applications in this field, all of this with a pinch of "Ratatouille" (as the movie).