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1. Generating Data for the Quantexa Platform
It is standard practice for Quantexa engineers to develop in non-production environments with no access to real data. These environments are used to validate code changes and assess regression impacts prior to production releases. Generated data is used as a substitute for real data in these cases to enable running ETL and…
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2. Best Practice: Data Generators
Data generation can be an invaluable and often crucial part of a Quantexa deployment. This post aims to explore the benefits of adding data generators. It will provide guidance on best practices for implementation and go over some custom examples. Why Generate Data? Either out of necessity or to improve the development…
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3. Data Generators: Enhancing Data Generation with Seeding
This article covers how a deployment used the JavaFaker library and seeding to enhance Entity Generation from data generators. Specifically, in ensuring that consistent information is generated alongside each Entity and between Document types to create realistic Networks. Prerequisite Reading To ensure you have a solid…