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Welcome to Databucket wiki!

Databucket is a flexible test data manager designed for the software development teams. It delivers features to effectively create and maintain test data and metadata for extensive tests on mutliple projects, multiple environments and various tools at the same time. Databucket stores data in a database as elastic structures which gives you ready approach for constanly changing test data.

Who is this wiki for?

This wiki is dedicated for users who want to use or administrate Databucket. Here you can find detailed explanation for every Databucket feature. Furthermore, if there is anything unclear, you can also create Github task with your question or just ask on this Slack channel.

Components

• Database

• Databucket 2 -> MySQL (v5.7^)
• Databucket 3 -> PostgreSQL (v13.x^)

• RESTfull API

The base API endopoints and methods are documented using Swagger. These allows to use create, read, modify, delete and reserve data. The access to this documentation is available directly from Databucket frontend.

• GUI

In Databucket fontend, you can administrate projects and work on collected data.

Key features

• Project managment For better data management, you can create multiple isolated projects in one Databucket instance. Each Databucket user can have access to many projects.
• Two levels of user privileges First, you set up user roles witch grant access to API, fontend, project setttings or projects management. Second, in the project settings, you can set up user access to groups, buckets or views, indirectly by user teams or user roles and directly to user.
• Data reservation In addition to the standard CRUD methods, you can use "reserve" method. Reserve method ensures to get unreserved data.
• Not reserved data protection You can hide not reserved data, then users without ADMIN role can see data only after reservation, when they are owners of the data. Users with ADMIN role, see all data.
• History of data changes Enabling on bucket the history functionality, allows on detailed tracking of creating and modifying collected data.
• Multiple views You can define multiple views for buckets. Every view have predefined columns, can have filter and you can configure user access and enabled features.