How to Leverage and Understand Keypup's Various Datasets

Gain insights on leveraging Keypup's datasets to build efficient engineering dashboards. Discover how to query and filter data effectively, monitor performance, and drive success in your software development process. Unlock the power of data-driven decision-making with Keypup.

Celine Nabais
Celine Nabais LinkedIn
• 3 min read
How to Leverage and Understand Keypup's Various Datasets

​Understanding datasets and fields is essential for building efficient engineering dashboards that provide meaningful insights. When working with a powerful tool like Keypup, having a comprehensive understanding of the available datasets and fields enables you to query and filter data effectively. In this article, we will explore how to leverage and understand Keypup's various datasets, helping you to build insightful engineering dashboards tailored to your organization's specific needs.

1. When creating or editing a metric, the first step in the process is to select the dataset that contains the information you want to query. To access the datasets in this specific example, click on the edit (...) button located at the top right of any insight.

Image

2. Then, click on "Edit insight" from the drop-down menu.

Image

‍

3. Now that you're in the "Select dataset" section, click on the dataset drop-down menu, in this case, "Issues & Pull Requests".

Image

4. As you can see, you can select from four different pre-joined datasets that cover most of the possible engineering metrics use cases. Click on any dataset to see which fields you can query and filter on. The available fields of the selected dataset are listed at the right.

Image

5. If you are not sure about the fields and would like to better understand their type, how they are being populated, their source, and more, click on the "Help 👋" button located at the bottom right of your screen.

Image

6. Then, click on the "Help Content" icon located at the top left of the modal.

Image

7. Click on the "Data and Operations" section. From there, you can access all datasets, function, and operator documentation.

Image

8. For this specific tutorial, click on "Issues & Pull Requests dataset".

Image

9. Then, click on "Issues & PRs dataset Overview".

Image

10. A short description of the dataset and its associated fields can be found in this section. You can get a detailed description of any field by clicking on it. For instance, click "Assigned at".

Image

11. A list of details about the field will appear, such as the dataset, field type, description, and source. All articles have a small icon at the top right that allows you to open and enlarge them.

‍

Image

‍

Image

‍

With Keypup's user-friendly interface and comprehensive documentation, you have the tools to leverage and understand datasets more effectively. By building efficient engineering dashboards tailored to your organization's needs, you can monitor performance, identify areas for improvement, and drive success in your software development process.

Discover Keypup's various datasets today to unlock the power of data-driven decision-making in your engineering organization.

Ready to Transform Your Analytics?

Join teams already using AI to make data-driven decisions faster than ever.

Most Recent Articles

The Bug SLA Mirage: Why Priority Fix Targets Are Missed and Nobody Knows

The Bug SLA Mirage: Why Priority Fix Targets Are Missed and Nobody Knows

Your bug SLA report says 91%. Your customers experienced 65%. Learn what engineering leaders on Reddit say about P1 fix-time targets, SLA clocks that start late and bugs closed to stop the timer at enterprise scale, and how Keypup MCP measures real SLA attainment by priority and team from Jira workflow history and linked pull requests.

Thomas Williams
The Dependency Bot Backlog: Why Dependabot and Renovate Pull Requests Pile Up Unmerged

The Dependency Bot Backlog: Why Dependabot and Renovate Pull Requests Pile Up Unmerged

Dependabot and Renovate open thousands of pull requests across enterprise repositories, and most of them are never merged, merged with no review, or left open for months. Learn what engineering leaders on Reddit say about dependency bot noise, unowned major upgrades and auto-merge risk, and how Keypup MCP measures the bot backlog by version bump, ecosystem, merge path, next action and owner.

Liam Davis
The Vendor Black Box: How to Measure Outsourced Engineering Teams with Git and Jira Data

The Vendor Black Box: How to Measure Outsourced Engineering Teams with Git and Jira Data

Enterprises spend millions on outsourced and offshore engineering vendors, yet most can only measure them through hours billed and status reports. Learn what engineering leaders on Reddit say about vendor black boxes, hidden review load and untraceable work, and how Keypup MCP compares vendor and in-house pull requests, review wait times, review load and Jira traceability to show what each vendor actually delivers.

Stephane Ibos