> For the complete documentation index, see [llms.txt](https://docs.tickertrends.io/tickertrends/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tickertrends.io/tickertrends/documentation-v2/2.-data-coverage-and-data-types/2.2-data-types/homebrew-installs-cask.md).

# Homebrew Installs (Cask)

#### Overview

Homebrew Installs (Cask) tracks installation activity for GUI-based applications distributed through Homebrew Cask. This dataset helps measure adoption trends for consumer-facing desktop software by monitoring install activity for applications commonly downloaded through the Homebrew ecosystem.

#### Historical Length

Historical coverage varies depending on the application, with multi-month to multi-year history available for supported packages.

#### Granularity

Data is available at daily granularity, allowing users to monitor adoption trends, product momentum, and shifts in developer or consumer software usage over time.

#### Update Frequency

Updated regularly as new Homebrew package installation data becomes available.

#### Methodology

Homebrew Installs (Cask) measures installation activity for applications distributed through Homebrew’s Cask package management ecosystem.

Homebrew Cask is commonly used to install GUI-based desktop applications such as browsers, productivity tools, communication platforms, and developer software including applications like Google Chrome, Slack, Visual Studio Code, Docker Desktop, and Figma.

Because Homebrew installs often reflect real user adoption behavior among technically engaged users, this dataset can serve as a useful proxy for desktop software adoption, developer workflow trends, and product momentum within the software ecosystem.

#### Use Cases

* Tracking adoption trends for desktop software applications
* Monitoring momentum for developer tools and productivity platforms
* Comparing installation activity across competing software products
* Identifying inflection points in software adoption
* Measuring traction for newly launched desktop applications
* Evaluating usage trends among technical and developer-heavy audiences


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