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1. Introducing QPython
In this article, we'll introduce QPython and give some examples of how your data scientists could use it for interacting with the Quantexa platform. The Quantexa Decision Intelligence Platform is a powerful tool underpinned by a set of capabilities for performing complex analytics with Quantexa-processed data, such as…
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2. Elasticsearch Considerations For Quantexa
This article discusses the key considerations for solution architects and data engineers when deploying Elasticsearch (sometimes referred to as Elastic) for Quantexa. It's useful, though not essential, to have a high level understanding of Quantexa in general, such as how we use Document, Entity and Compounds. 💡Notes /…
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3. Quantexa deployment for multiple use cases
The Quantexa platform is not a point solution; it has been developed on fundamental principles of flexibility and scalability, and supports multiple business applications or use cases in a single system. Many Quantexa deployments will originally serve a single use case, although some may be intended for broader use from…
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4. Quantexa Deployment Patterns Best Practice
Introduction Quantexa deployments require a simple set of underlying components: Distributed Spark cluster for Quantexa batch data processing Container platform to serve the Quantexa UI and microservices Elasticsearch for serving data to the mid-tier RDBMS for storing user state in the mid-tier Along side these you will…
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5. An Introduction to Knowledge Graphs and qKnowledgeGraph
This article introduces Knowledge Graphs at an introductory technical level. Note that: QKnowledgeGraph is the analytics library for working with Knowledge Graphs Knowledge Graphs created by Quantexa are the graphs created and analyzed using QKnowledgeGraphs In an introductory Quantexa blog, we introduced why knowledge…