Quantexans, like our users across industry, Government, and academia, are passionate about open-source software (OSS) and communities. We believe that technology works best when we all, as developers, share, contribute and collaborate together, not in siloes.
How does Quantexa use open-source software in our technology stack and ecosystem?
Quantexa’s Decision Intelligence Platform is built on an open architecture leveraging popular source technologies, building on Scala, OpenJDK (Java and the JVM), and Python-based ecosystems. Our approach ensures flexibility, scalability, and seamless integration with technologies already deployed in your organization. Whether you do or don’t use Quantexa, your platform architecture, data engineering, and data science teams are likely familiar with our open-source-centric stack.
Open-source technologies used include Apache Spark, Elastic, OpenSearch, Apache Kafka, PostgreSQL, Spring Boot, and Kubernetes. An analytics layer that tightly integrates with the Scientific Python stack and its popular graph and ML libraries is also used.
For more information on our stack, see our reference architecture and our documentation.
How does Quantexa support technical users, such as data engineers and data scientists, to use open-source software in our ecosystem?
Quantexa deploys and promotes the use of open-source software, so your teams can use the tools they are familiar with and choose the best technology to solve the problem at hand. We help technical users, such as data engineers and data scientists, by:
- Publishing our data assets in Apache Parquet format, consumable by data management, data lake, and data science tools via Parquet and Spark-supported distributed filesystems and object stores
- Providing knowledge graph generation and analytics tooling from within standard Python and Jupyter Notebook ecosystems, including NetworkX
- With QPython, REST APIs and an open data interface, supporting open-source models and big data libraries and languages such as Python (e.g. Scikit-learn), R, Scala, and Spark-native applications e.g., MLib, users can:
- Use the output of these models to drive Quantexa's processing and analytics, e.g., for scoring and monitoring
- Take Quantexa data products to your models, e.g., for predictive modelling
- Enabling data, including audit log and output from analytical and scoring processes, to be available for querying or export from/to open-source reporting and intelligence tools, for example Kibana, Grafana, Streamlit, Shiny, and BIRT
How does Quantexa contribute to the open-source community?
Quantexa encourages its developers to be proactive with open-source communities. Here are some of the ways in which we demonstrate our commitment.
- Quantexa developers publish fixes to popular open-source communities, such as Apache Spark and NetworkX
- Quantexa developers lead and contribute significantly to open-source Github-hosted projects, for example GLiREL, GrandCypher, STAGE, and News Signals
- Quantexa hosts Developer events, such as the London Scala User Group, and has hosted ‘Spark in the city’ in its London HQ, to drive engagement with and advocate for the open-source community
- Quantexa developers, data scientists, architects, engineers, and other technical professionals are encouraged to attend PyData MeetUps, Scala and Java User Groups (JUGS), such as the London Java Community
- Quantexa's world-class research and innovation team publishes peer-reviewed publications with many thousands of citations, for example, STAGE: Simplified Text-Attributed Graph Embeddings Using Pre-trained LLMs and KGValidator: A Framework for Automatic Validation of Knowledge Graph Construction
