> Markdown version of [/videos/1024-databaseless-data-processing-high-performance-for-cloud-native-apps-and-ai?t=718](https://www.wearedevelopers.com/videos/1024-databaseless-data-processing-high-performance-for-cloud-native-apps-and-ai?t=718). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Databaseless Data Processing - High-Performance for Cloud-Native Apps and AI

Stop letting databases bottleneck your Java apps. Eliminate ORMs entirely. Learn how EclipseStore treats object graphs as in-memory databases to deliver microsecond queries and cut costs by 90%.

- **Speakers:** [Markus Kett](https://www.wearedevelopers.com/@markus-kett)
- **Event:** World Congress 2024
- **Published:** August 20, 2024
- **Duration:** 30:21
- **URL:** https://www.wearedevelopers.com/videos/1024-databaseless-data-processing-high-performance-for-cloud-native-apps-and-ai

## Summary

Modern cloud-native and AI applications demand maximum performance, yet traditional data persistence models frequently bottleneck scaling. Relational and NoSQL databases suffer from an inherent impedance mismatch when interfacing with object-oriented languages like Java, forcing developers to rely on object-relational mapping (ORM) frameworks like Hibernate. While ORMs bridge this structural gap, they introduce significant CPU overhead and latency due to data translation. To compensate, engineering teams layer complex configurations of local caches, distributed caches, database clusters, and dedicated search servers, which drastically inflates cloud infrastructure expenses and multi-node sync complexity.

EclipseStore resolves this persistence friction by bypassing dedicated database servers and ORM layers functionality entirely. The open-source persistence framework enables systems to safely treat the native Java object graph as the primary in-memory database. Data changes are efficiently persisted as binary formats directly into highly affordable cloud object storage or distinct file systems. This databaseless methodology successfully merges the raw processing power of the JVM with the economic resilience of blob storage. By querying in-memory topologies via the native Java Streams API, query response times plummet from standard milliseconds to minimal microseconds, dynamically optimizing operations for complex distributed algorithms and AI datasets.

This deliberate architectural simplification eliminates the need for sprawling distributed caches or complex database pooling setups, making the solution functionally ideal for maintaining bounded context inside microservice architectures. Shifting to an embedded object storage model reduces necessary computing overhead, effectively cutting backend operational costs by up to 90% while substantially reducing data center carbon emissions. Because SQL queries and legacy database administration are intentionally discarded, external application interactions must be manually constructed using standard REST or GraphQL interfaces to extract datastore properties.

**Keywords:** databaseless architecture, eclipsestore framework, java in-memory data processing, java object graph persistence, impedance mismatch resolution, cloud object storage persistence, java streams api querying, orm framework latency, microservices data persistence, reducing cloud database costs, jvm performance optimization, binary data storage, eliminating distributed caches, sustainable it architecture

## Chapters

1. **Modern application performance and efficiency requirements** (00:19) — Modern applications require optimal performance combined with strict cloud cost reductions and lower energy consumption.
1. **Impedance mismatch between object models and relational databases** (02:38) — The fundamental incompatibility between object-oriented programming structures and traditional relational database tables prevents direct translation and slows computing execution.
1. **Complexities of object-relational mapping and distributed architectures** (05:09) — Using object-relational mapping frameworks alongside layered distribution caches to mitigate database bottlenecks heavily compounds architectural overhead.
1. **High cost of cloud database servers versus object storage** (10:13) — Dedicated cloud database hosting necessitates expensive compute processing units while basic object storage delivers cheap unmanaged file capacity.
1. **Processing data at microsecond speeds with Java Streams** (11:58) — In-memory object graphs structurally circumvent translation overhead and reduce latency significantly when sequentially queried using native data streams.
1. **Persisting native Java objects efficiently with Eclipse Store** (14:35) — Saving real in-memory object hierarchies directly into binary file formats eliminates operational scaling limits natively across enterprise platforms.
1. **Simplifying distributed architecture with stateful object graphs** (17:53) — Extricating cluster database dependencies minimizes infrastructure sprawl and empowers core language elements to efficiently manage concurrency replication instead.
1. **Performance benchmarks of relational databases against object storage** (21:32) — Bypassing legacy query translation logic to directly process complex in-memory types produces execution speeds far surpassing cached database interactions.
1. **Architectural benefits and implementation challenges of databaseless systems** (23:41) — Abstracting complex databaseless storage eliminates legacy operational components but concurrently mandates critical paradigm shifts like drafting unique microservice endpoints.
1. **Licensing and enterprise availability of Eclipse Store** (29:32) — The open-source core data persistence utility permits extensive local deployment while separately licensing optional commercial tooling tailored for massive production sets.

## Related Moments

- [Simplifying enterprise architecture with database-less data processing](https://www.wearedevelopers.com/videos/626-in-memory-computing-the-big-picture) (from "In-Memory Computing - The Big Picture")
- [Introducing MicroStream for rapid memory object graph persistence](https://www.wearedevelopers.com/videos/405-build-ultra-fast-in-memory-database-apps-and-microservices-with-java) (from "Build ultra-fast In-Memory Database Apps and Microservices with Java ")
- [Simplifying offline data persistence using AWS Amplify DataStore](https://www.wearedevelopers.com/videos/554-offline-first-automatic-data-synchronisations-for-your-web-and-mobile-applications) (from "Offline first & automatic data synchronisations for your web and mobile applications")
- [Modern application stacks and real-time data requirements](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) (from "Leveraging Real time data in FSIs")
- [The object-relational impedance mismatch in standard database programming](https://www.wearedevelopers.com/videos/405-build-ultra-fast-in-memory-database-apps-and-microservices-with-java) (from "Build ultra-fast In-Memory Database Apps and Microservices with Java ")
- [Optimizing Java startup processes for fast serverless workloads](https://www.wearedevelopers.com/videos/1008-serverless-java-in-action-cloud-agnostic-design-patterns-and-tips) (from "Serverless Java in Action: Cloud Agnostic Design Patterns and Tips")

## Related Articles

- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [What does the history of data storage tell us about the future?](https://www.wearedevelopers.com/magazine/495-what-does-the-history-of-data-storage-tell-us-about-the-future)
- [Why Event-Driven Architecture Isn’t About Speed (and When You Actually Need It)](https://www.wearedevelopers.com/magazine/745-why-event-driven-architecture-isn-t-about-speed-and-when-you-actually-need-it)
- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)

## Related Jobs

- [Data Engineer](https://www.wearedevelopers.com/jobs/ext/1276933-data-engineer) at **PROSOZ Herten GmbH**
- [Data Scientist](https://www.wearedevelopers.com/jobs/ext/1351648-data-scientist) at **Almedia**
- [Principal Engineer - AI Search & Vector Infrastructure](https://www.wearedevelopers.com/jobs/ext/353953-principal-engineer-ai-search-vector-infrastructure) at **Redis**
- [Data Engineer](https://www.wearedevelopers.com/jobs/ext/1261010-data-engineer) at **PROSOZ Herten GmbH**
- [Principal Engineer - AI Search & Vector Infrastructure](https://www.wearedevelopers.com/jobs/ext/319507-principal-engineer-ai-search-vector-infrastructure) at **Redis**
- [Cloud Data Engineer (all genders)](https://www.wearedevelopers.com/jobs/48216-cloud-data-engineer-all-genders) at **msg**