Processing large amounts of data efficiently is a common challenge in modern systems. Many workloads spend most of their time performing simple mathematical operations on large arrays of numbers. Modern CPUs can speed this up using SIMD instructions, which allow a single instruction to process multiple values at once. Traditionally, accessing these capabilities from Java has been difficult and often required platform-specific native code.

The Panama Vector API changes this by allowing Java developers to express these operations directly in Java while the JVM turns them into efficient CPU instructions.

Elasticsearch provides an interesting real-world example. Modern features such as vector search rely on large numbers of vector calculations, and analytical queries written in ES|QL often process large batches of numeric data. In both cases the system repeatedly performs simple operations over large arrays of values. Elasticsearch and its underlying search engine Lucene use the Vector API to speed up these operations by taking advantage of SIMD instructions available on modern CPUs.

Using an incubating API in production software is unusual and normally avoided. In this case, however, the potential performance gains are significant enough to make it worthwhile. This talk explores how Elasticsearch integrates the Vector API, how it safely enables and disables SIMD acceleration at runtime, and what this tells us about the future of high-performance computing in Java.

Kibana is a powerful tool for exploring data, building dashboards, and interacting with Elasticsearch. But when developers want to include it in automated tests, demos, or reproducible local environments, the setup often involves multiple moving parts: Elasticsearch, security configuration, and the right connection settings.

In this session we will see how Testcontainers can turn that setup into a simple and repeatable developer workflow.

Using the recently proposed KibanaContainer for Testcontainers Java as a real-world example, we will explore what it takes to make a sophisticated tool easy to start from code. We will walk through the design decisions behind it: automatically pairing with ElasticsearchContainer, connecting to external clusters, handling authentication and TLS, and providing sensible defaults that work well in automated tests.

Finally, we will see how this approach allows developers to start Kibana from Java with just a few lines of code and use it in integration tests, demos, and reproducible development environments.

The result is not just a new container class, but a pattern for turning multi-service setups into clean, developer-friendly APIs.