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Technology

A Modern Java Technology Stack, Short by Design

We would rather be genuinely deep in a stack suited to high-throughput, long-lived systems than shallow across everything. Every case study on this site runs on the technologies below, and the two largest clear a billion transactions an hour and 300 million queries a day.

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The stack in production

1,000,000,000
financial transactions in under an hourJava · in-memory grid · cloud deployedFastPost case study
300,000,000
travel search queries a dayJava · Kafka · in-memory cachingHotel search case study
Acquired
Recostream, sold to GetResponseJava and machine learning recommendation engineRecostream case study
High performance
Measured under real load: a billion transactions in under an hour
Modern Java
New work on Java 21 or 25 and Spring Boot, older systems moved forward
A short stack
A few technologies we know deeply, rather than a long list we know a little
Measured results
Every performance claim backed by a test harness the client keeps
Built to last
Systems documented for years of maintenance, by us or by your own team
Runtime and framework

Java at the core: the stack we know best

Java and Spring run through almost every system we build, where our experience goes deepest.

  • Java 17 to 25

    New work starts on Java 21 or 25, the current LTS releases. Virtual threads and generational garbage collection change how a JVM behaves under load.

    Java development services

  • Spring Boot

    Most of what we build sits on Spring: web services, batch, security and data. Spring Boot 4 needs Java 17 or later, which starts much of our modernization work.

    Application modernization

  • Spring AI

    Model calls, retrieval and agent tooling as Spring components, using the dependency injection, security and observability your system already has.

    AI integration

  • Kotlin on the JVM

    Where a client already uses Kotlin, our engineers work in it on the same JVM, libraries and build tooling as the rest of the system, with the same reviews.

    Java team extension

  • Stratoflow successfully completed numerous web-based projects. They can handle technical challenges very well. The team has exceptionally talented developers with a strong grasp of business objectives and proactively provided solutions.
    Arnd Jan PrauseChief Operating Officer, musQueteer
Data in motion and in memory

In-memory and streaming technology that keeps systems fast at scale

The technologies behind our fastest systems, chosen for throughput and predictable latency.

  • Apache Kafka

    Event streaming where ordering, delivery guarantees and replay matter. Topic and partition design is expensive to change later, so we make it deliberately.

    Kafka partitions guide

  • Hazelcast

    In-memory data grids for systems that cannot afford a database round trip per request: distributed caching, compute next to the data and careful cluster design.

    Performance engineering

  • Oracle Coherence

    For enterprises already committed to Oracle: cache topologies, near caches and upgrades without surprise serialization changes in production.

    Performance engineering

  • Apache Ignite

    The third grid we run, chosen when SQL over in-memory data matters more than the Hazelcast programming model, with persistence and compute running next to the data.

    Performance engineering

  • PostgreSQL

    The default relational store. Many problems labelled as caching turn out to be an unindexed query, so we measure the database before proposing a grid.

    Database guide

  • Elasticsearch

    Catalogue and document search where relevance and vocabulary matter more than raw speed: indexing, enrichment and synonyms designed around your data.

    Ecommerce engineering

Case studies

Our projects

All case studies

Where it runs

Platforms, delivery and quality

  • AWS and Azure

    Both, plus Google Cloud and dedicated hosting where a client already runs. Kubernetes when the system is large enough to justify it, plain machines otherwise.

    Cloud migration

  • Pipelines, observability and tooling

    Reproducible builds, dependency scanning, and logs, metrics and traces wired into the JVM from day one. AI coding tools stay inside data boundaries you approve.

    Platform engineering consulting

  • Testing

    JUnit and Cucumber for behaviour, JMeter and custom harnesses for load. Every performance claim on this site was measured with a harness the client kept.

    Software testing guide

  • Openkoda

    The insurance policy administration system our sister company builds. We implement it, integrate it with your systems and write the modules around it.

    Openkoda implementation

Integration platforms

Salesforce and MuleSoft, as proof rather than as an offer

We spent years building on Salesforce and MuleSoft: RAML-driven data stores, machine learning pipelines between Salesforce and AWS, a MuleSoft plugin, an NGO's Salesforce implementation, a healthcare IoT SaaS on the Salesforce platform. That work is consolidated into one case study and it is why our integration layers are designed by people who have run a commercial integration platform in anger.

We no longer sell Salesforce or MuleSoft development as a standalone service. When an integration problem lands inside a Java system, the same engineers handle it with MuleSoft, Apache Camel or plain Spring Integration, whichever your systems already use.

Read the Salesforce and MuleSoft case study

Go deeper

Java stack guides from our engineers

All articles

Written by , Co-founder and CTO, Stratoflow. Published . Updated .

Next step

Put our Java stack to work on your system

Bring the system and the numbers that matter: throughput, latency or running cost. We will show where this stack makes the difference.

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