Program

Modern analytics experiences demand “conversation-fast” backends—systems that serve the requests of an agent or LLM in real time at the speed of a natural conversation.

In this talk, we’ll get deep into an open-source reference architecture for powering conversational AI and real-time analytics in user-facing applications. We’ll get hands-on in the code, and explore practical patterns for integrating streaming and analytical infrastructure into your web application, including AI chat systems.

We’ll focus on a reference architecture that integrates transactional (Postgres) and analytical engines (ClickHouse®), along with RedPanda for streaming, a React web app frontend, and MCP interfaces for enabling LLM chat within the app.

The whole analytics stack is defined in code using the MooseStack open source framework. https://docs.fiveonefour.com/moose

This architecture ensures the low-latency responsiveness users expect with analytics visualizations and conversational AI, and a modern, local-first developer experience that integrates naturally into your stack.

This session explores how open-source analytics technologies are transforming the public sector through the lens of Electronic Income Verification (EIV) systems—platforms that process over 850,000 real-time verifications daily, integrate 40+ data sources, and maintain 99.95% uptime to support equitable, efficient public benefit delivery.

We’ll dive into the open-source stack behind these systems: event streaming with Apache Kafka, data orchestration with Airflow, analytics with Apache Superset and DuckDB, and ML-powered fraud detection using tools like scikit-learn and Hugging Face NLP. You’ll learn how public agencies are building scalable, secure, and cost-effective solutions by leveraging community-driven technologies and standards.

Topics include building modular data pipelines, real-time dashboards, anomaly detection, and managing governance and compliance (GDPR, HIPAA) in open environments. The talk will also highlight DevOps practices such as IaC, GitOps, and monitoring with Prometheus and Grafana to maintain visibility, security, and auditability in high-trust systems.

Ideal for data engineers, open-source practitioners, and civic tech innovators, this session offers a real-world case study on how open analytics infrastructure can power large-scale, high-impact digital public services.

This presentation showcases an open source healthcare analytics platform that reduced ICU transfers by 20% through real-time patient risk prediction. Built entirely with open source technologies, the system demonstrates how healthcare organizations can leverage community-driven tools to achieve clinical impact without vendor lock-in.

The architecture combines Apache Kafka for real-time EMR streaming, Apache Spark for ML model training, and PostgreSQL with TimescaleDB for time-series clinical data. Docker containerization ensures reproducible deployments across environments, while Kubernetes orchestrates auto-scaling during patient admission surges. The ML pipeline uses scikit-learn and XGBoost models trained on anonymized historical cohorts, with MLflow tracking experiments and model versioning.

Key open source components include: Apache Airflow for workflow orchestration, Grafana for clinical dashboards, and Apache Superset for analytics visualization. The platform implements FHIR standards through HAPI FHIR server, ensuring interoperability with existing hospital systems.

Critical lessons learned include: designing privacy-preserving analytics with differential privacy libraries, implementing federated learning across hospital networks, and maintaining sub-second latency for critical alerts using Redis caching. The session covers practical deployment strategies, cost optimization techniques, and governance frameworks for open source healthcare analytics.

Attendees will learn to build production-ready healthcare analytics platforms using exclusively open source tools, complete with code examples, architecture patterns, and regulatory compliance strategies that deliver measurable patient outcomes.

The regulatory compliance landscape generates massive datasets—over 300 million pages of regulatory documents globally with 200+ daily changes—creating both analytical challenges and opportunities for the open source community. This presentation demonstrates how open source analytics tools and frameworks can be leveraged to build sophisticated regulatory compliance systems that rival proprietary enterprise solutions.

This session showcases practical implementations using open source technologies including Apache Spark for large-scale document processing, Elasticsearch for regulatory search and retrieval, Apache Airflow for compliance workflow orchestration, and Hugging Face transformers for natural language understanding. We’ll explore how organizations across financial services, healthcare, and energy sectors have built cost-effective compliance analytics platforms using entirely open source stacks.

The presentation includes hands-on demonstrations of processing regulatory datasets, implementing change detection algorithms, and building interpretable ML models for compliance risk assessment. Attendees will see real architectures processing documents from regulatory bodies like CFTC and ESMA, with techniques for handling multi-jurisdictional requirements and maintaining audit trails using tools like Apache Kafka and PostgreSQL. Key technical topics include distributed text processing strategies, implementing semantic search for regulatory content, building alerting systems for regulatory changes, and creating compliance dashboards using open source visualization tools like Apache Superset and Grafana. We’ll address common challenges including data quality, model interpretability requirements, and scaling analytics workloads for enterprise compliance needs. The session emphasizes community-driven approaches to regulatory analytics, including contributing to open source compliance frameworks, sharing anonymized datasets for research, and collaborative development of regulatory parsing libraries. Practical takeaways include reference architectures, code samples, and deployment strategies that attendees can immediately apply in their organizations.

As regulatory technology spending approaches $204 billion by 2026, this presentation demonstrates how open source analytics can democratize advanced compliance capabilities, making sophisticated regulatory intelligence accessible to organizations of all sizes while fostering innovation through community collaboration.

What happens when databases don’t just store data—but help analyze it intelligently? This talk explores emerging trends in AI-powered database intelligence, from schema optimization and real-time query tuning to transforming unstructured content using techniques like sentiment analysis and entity recognition. We’ll also dive into the future of self-driving databases and multi-modal analytics that integrate text, images, and more. Attendees will leave with a forward-looking view of how AI is reshaping database engines into context-aware, insight-generating platforms—laying the foundation for the next generation of open-source analytics systems.

As enterprises grapple with an explosion of data and increasing pressure to make rapid, informed decisions, traditional Business Intelligence (BI) tools are reaching their limits. Static dashboards and complex query interfaces often exclude non-technical users, creating friction between data and action. Enter AI-native analytics—a transformative approach that integrates natural language interfaces (NLIs) with scalable machine learning (ML) to deliver intelligent, conversational decision systems.

This keynote explores how organizations can reimagine their analytics infrastructure by embedding AI into the very fabric of user interaction. Drawing on real-world implementations and cutting-edge research, we’ll unpack the architectural foundations needed to operationalize NLIs at scale—spanning natural language understanding (NLU), data context alignment, governance, and high-performance compute. We’ll address the core challenges of conversational systems in the enterprise: query ambiguity, semantic grounding, explainability, and scalability under dynamic workloads.

With AI-driven interfaces, business users can shift from passively consuming reports to actively engaging with data through dialogue—unlocking faster insight discovery and empowering decision-making at all levels. Attendees will leave with a strategic framework for building next-generation analytics platforms that are intelligent, adaptive, and truly human-centric.

Retrieval-Augmented Generation (RAG) is transforming analytics applications — but implementing it often means managing multiple systems: OLTP, vector DBs, and orchestration tools.

In this session, we’ll show how OceanBase simplifies this stack by supporting both structured and vector data natively, enabling developers to build real-time RAG pipelines using just one open-source database.

We’ll walk through a working demo that combines OceanBase with OpenAI and popular Python frameworks like LangChain, demonstrating how to perform vector search and retrieval directly using SQL.

Unlike traditional setups that require combining a relational database and a separate vector database, OceanBase handles both transactional and semantic search in a single engine — with consistency, availability, and simplicity.

Joseph Cambell’s story-circle describes the journeys of epic heroes from the known into the unknown in search of rewards. Maybe it’s not so different from the Gartner Hype Cycle that describes the journeys of innovations; And as individual developers we also go through a journey of discovery when adopting new tools and technologies.

Data teams continue to face long-standing challenges: their customers often distrust their results, data providers frequently ignore their existence, and teams spend more time firefighting than creating insights. The demand for AI just makes it more complicated: no wonder many data teams experience PTSD.

The solution is simple: identify problems before they reach your customer. You need to implement data quality tests—lots of them. Check every table and column. See if anything is incorrect. Run these tests in production and incorporate them into the development process. Use the results to obtain data quality scores and implement improvements to your source systems.

Data teams work with hundreds or thousands of tables and often lack sufficient time to achieve data test coverage. That’s why, after decades of data engineering, we released an open-source tool that handles this for them.

DataKitchen’s open-source data quality and data observability tools aim to help data teams automatically generate 80% of the necessary data tests with just a few clicks, while providing an easy-to-use UI for collaborating on the remaining 20% of tests that are unique to their organization.

Recently, several open source companies attracted a lot of attention after their announcements of license changes. Not surprisingly, these shifts sparked backlash from open source enthusiasts, prompting some to create community-driven forks under open source foundations.

Now there is growing skepticism toward (single) company backed open source projects, with many arguing that open source projects should be run by neutral foundations to prevent future bait-and-switch tactics. But is foundation backing really the answer?

Drawing on over a decade of experience in both open source foundations and companies, Fatih and Ray will compare foundation-backed and company-backed projects across key areas such as governance, roadmap planning, community, and funding. They’ll explore real-world examples of successful—and not-so-successful—projects in both models.

Finally, Fatih and Ray will discuss why funding models should be just one of several factors in assessing the long-term viability of open source projects. They’ll offer a holistic approach for evaluating open source projects, helping developers and decision-makers make informed choices about which projects to adopt, support, or contribute to.

Financial analytics platforms face unprecedented data challenges, processing millions of transactions while delivering real-time insights to traders, risk managers, and compliance teams. Open source distributed databases have emerged as the backbone of modern FinTech analytics, enabling organizations to scale cost-effectively while maintaining full control over their data architecture.

This presentation explores how open source technologies like Apache Cassandra, PostgreSQL with Citus, and ClickHouse® power financial analytics through strategic sharding, replication, and consistency models. We’ll examine how leading financial institutions leverage these tools to handle 65,000+ transactions per second during peak trading periods while maintaining sub-millisecond query performance for real-time risk assessment and fraud detection.

Key topics include horizontal scaling strategies for multi-terabyte financial datasets, implementing cross-region replication for regulatory compliance, and balancing consistency requirements between transactional accuracy and analytical responsiveness. Through real-world case studies, attendees will discover how open source database architectures enable sophisticated financial analytics—from high-frequency trading algorithms to regulatory reporting pipelines—while reducing infrastructure costs and avoiding vendor lock-in.

Whether you’re building trading platforms, risk management systems, or compliance dashboards, this talk provides practical insights into architecting scalable, fault-tolerant analytics infrastructure using proven open source technologies that power today’s most demanding financial applications.

I was fed up with FAQ search that spits back junk or nothing at all. TSIA says over sixty percent of support tickets could be solved by our own docs, so why isn’t that happening? In this talk I’ll show how I built a simple semantic search app with just three hundred lines of Python. I’ll demo it live answering three differently worded questions, and all in under a hundred milliseconds using TiDB Open-Source and Amazon Bedrock embeddings. At the end i’ll provide the steps you need to take so you can clone the repo and run it on your docs tonight.

Magenta Telekom ingests many terabytes of new data every day, and every downstream consumer wants it immediately. The real bottleneck turned out not to be hardware but humans wrestling with hidden, hard-wired dependencies in hundreds of heterogeneous pipelines and sometimes tool silos.

Our fix was to treat every data asset as a node in a data-dependency graph and every transformation as an edge. Ingestion, Transformation, AI and BI are all part of the same executable graph. By using suitable abstractions and dependency injection less technical people are empowered to contribute business logic which can be operationalized efficiently.

This talk covers:

• Unified asset graph – ingest → transforms → reports → ML all in one lineage-aware DAG.
• Event-based pipelines: Events propagate state changes across the enterprise via the edges in the graph in near real-time
• Dependency-injection: By using the right abstractions we can empower more users along the data value chain to contribute.

Operational challenges are handled by the abstractions and analysts only focus on the business logic.

Structured Query Language’s Window Functions are a powerful tool for analytics. They let you get more granular insight than a GROUP BY clause. But the syntax is obtuse, the terms used are nebulous (unbounded previous anyone?), and the results can be much less insightful than expected. This session is a quick introduction to and explanation of how to use Window Functions efficiently to better investigate your data.