Documentation Site Resources Index

This page gives you an at-a-glance overview of the resources available on the Quantexa Documentation site, which are useful in the setup, configuration and use of The Quantexa Decision Intelligence Platform.

Best practice, reference, and resources

You can find some of our most popular and useful resources at the bottom of our home page.

To help you understand, set up, and use the platform more easily, our technical documentation contains best practice content, tools and other resources, including:

Supported software

A table listing the versions support for different third-party software, including browsers, development tools such as Scala, Apache Spark and Kubernetes and software used within the platform itself including Elasticsearch, Gradle and Node.js.

Project Example

An example project with accompanying documentation that provides guidance and best practices for particular features of The Quantexa Platform. This includes Smoke Tests, Scoring, and Task Loading. Project Example also includes migration branches that provide examples of how to perform product upgrades for each Quantexa release.

API reference

A complete list of the API (or Application Programming Interface) types used in The Quantexa Platform - including REST, Scala Client and Protobufs (Protocol Buffers) - with detailed sub-sections including full lists of the specific APIs for different services and features.

Data Fusion Core Library

A series of installation instructions for the core library, a series of models and parsers that you can use in your projects. Data Fusion is a framework for data modeling and ingestion used within the transformation and load stages of ETL. A step-by-step guide to the process of setting up Data Ingestion in the platform using Data Fusion is also available.

JSON Configuration

A reference for our JavaScript Object Notation (JSON), a syntax for storing and exchanging data. JSON can easily be sent to and from a server, and used as a data format by any programming language.

Helm Charts

A collection of YAML manifests that describe and deploy Quantexa App Tier resources into Kubernetes. The Chart is highly dynamic and facilitates resource provisioning and management.

Parsers

An out-of-the-box solution for cleansing and parsing the data types typically used for Entity Resolution in Quantexa deployments. Data for Entities such as addresses, businesses, and individual names can vary in structure, format, and completeness. Standard Parsers, including for each of these Entity types, enable you to standardize the Entities in your data to improve the matching accuracy in Entity Resolution.

Data Packs

A collection of resources that provide the following for the third-party or open source data sources most often used on Quantexa deployments:

Pre-written data models
ETL code
Data Generators
UI components

Detection Packs

An Early Access Scoring solution that enables projects to progress more quickly from resolved Entities and generated egocentric Networks to Alerts ready for investigation. Using Detection Packs avoids the time and cost associated with writing the same Scores and Pipelines multiple times on different projects. This out-of-the-box Scoring solution provides a set of baseline Scores, so individual projects can focus on developing the complex Scores that match their specific needs.

QPython

A library of helper functions for analyzing Quantexa-processed data using Python, including Scoring graphs, Batch Resolver outputs, and Scoring outputs, and for integrating the analytical results with The Quantexa Platform.

Find out more

You can find out more about everything mentioned on this page on our Documentation site. If you are unable to access the documentation site, please contact your Quantexa point of contact or the Community team at community@quantexa.com.

Published 8 days ago