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1. Become a Quantexa deployment wizard! - launching Quantexa's Deployment Engineering Best Practices!
Want to know neat tricks for deploying Quantexa? Our architects have distilled their favourite deployment learnings into a brand new best practice section on the documentation site which will help you deliver and run high quality Quantexa deployments. These learnings come from deploying Quantexa over many years at dozens…
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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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1. Multi Use Case Best Practice
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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1. Release Notes overview
Our Release Notes have everything you need to know about the features, capabilities, fixes, and improvements in each release of The Quantexa Platform, so it's a very important and popular area of our Documentation Site. On Community, you'll find the latest Release Notes in the release announcements category. What can I…
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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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2. Multi Use Case Best Practice: People
This page describes Quantexa's people-related recommendations for multi use case deployments. Many of the practices described on this page will also benefit single use case deployments. These best practices are: Identify a single platform owner for the whole Quantexa service. Form a Design Authority to set the technical…
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2. 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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2. Underlying platforms used by Quantexa
Introduction Quantexa makes extensive use of underlying platforms to enable it to operate at reliably at scale. When choosing these platforms the key principles were that they should be: Open source such that customers would be able to deploy them without incurring licence costs Widely adopted such that there is a large…
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3. 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…
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3. Multi Use Case Best Practice: Processes
This page describes Quantexa's process-related recommendations for multi use case deployments. Many of the practices described on this page will also benefit single use case deployments. These best practices are: Ensure that each interface between components has a documented specification. Ensure that interfaces between…