Talks

Apache Ignite incorporates a scalable, efficient machine learning (ML) framework that enables data transformation, ML model training, and inference on Apache Ignite nodes (without data leaving the cluster). However, sometimes scalability in regard to data size and data-transfer cost is not as important as scalability in regard to the number of parallel requests and the ability to use the external, more sophisticated models that are implemented in Python and Julia. In this talk, Peter Gagarinov shares his experience with integrating Apache Ignite with external machine frameworks. Using an ML-based, automated issue-management system (Alliedium) as an example, Peter shows how the business task of building a distributed and scalable service can be efficiently performed by relying on Apache Ignite and ML frameworks from Python and Julia ecosystems. In the second part of his talk, Peter presents a lightweight Apache Ignite data migration tool that was developed in-house for the needs of Alliedium: https://github.com/Alliedium/ignite-migration-tool

Building a scalable, multi-tenant backend for a Java-based, ML-driven Jira Cloud application imposes many requirements on the underlying technology stack. It is not uncommon to fulfill the requirements by combining pieces of technology—such as SQL and NoSQL databases, ORM tools, message brokers, load balancers, caching layers, ML pipelines, and web servers. In this talk, Peter shares his Apache Ignite experience. He will show how one can minimize the number of blocks in a complex, scalable backend for an ML-based, automated issue-management system (Alliedium), as you stay within the Java ecosystem and the microservice paradigm. We show you how to integrate Apache Ignite with Atlassian Connect Spring Boot, and we discuss Apache Ignite features such as SQL and NoSQL queries, thin and thick clients, caching, distributed messaging and events, distributed computations, database-schema change tracking, and Kubernetes deployment.