an invention by terezacerna
Description
This project aims to explore the popularity and developer sentiment around SUSE and its technologies compared to Red Hat and their technologies. Using publicly available data sources, I will analyze search trends, developer preferences, repository activity, and media presence. The final outcome will be an interactive Power BI dashboard that provides insights into how SUSE is perceived and discussed across the web and among developers.
Goals
- Assess the popularity of SUSE products and brand compared to Red Hat using Google Trends.
- Analyze developer satisfaction and usage trends from the Stack Overflow Developer Survey.
- Use the GitHub API to compare SUSE and Red Hat repositories in terms of stars, forks, contributors, and issue activity.
- Perform sentiment analysis on GitHub issue comments to measure community tone and engagement using built-in Copilot capabilities.
- Perform sentiment analysis on Reddit comments related to SUSE technologies using built-in Copilot capabilities.
- Use Gnews.io to track and compare the volume of news articles mentioning SUSE and Red Hat technologies.
- Test the integration of Copilot (AI) within Power BI for enhanced data analysis and visualization.
- Deliver a comprehensive Power BI report summarizing findings and insights.
- Test the full potential of Power BI, including its AI features and native language Q&A.
Resources
- Google Trends: Web scraping for search popularity data
- Stack Overflow Developer Survey: For technology popularity and satisfaction comparison
- GitHub API: For repository data (stars, forks, contributors, issues, comments).
- Gnews.io API: For article volume and mentions analysis.
- Reddit: SUSE related topics with comments.
This project is part of:
Hack Week 25
Activity
Comments
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4 days ago by terezacerna | Reply
This project provides a comprehensive, data-driven assessment of SUSE’s presence, perception, and alignment within the global developer and open-source landscape. By integrating insights from the Stack Overflow Developer Survey, Google Trends, GitHub activity, GitHub issue sentiment, and Reddit discussions, the analysis offers a multi-layered view of how SUSE compares with key competitors—particularly Red Hat—and how the broader technical community engages with SUSE technologies. It is important to note that GitHub Issues and Reddit data were limited to approximately one month of available data, which constrains the depth of historical trend analysis, though still provides valuable directional insights into current community sentiment and interaction patterns.
The Developer Survey analysis reveals how Linux users differ from non-Linux users in terms of platform choices, programming languages, professional roles, and technology preferences. This highlights the size and characteristics of SUSE’s core audience, while also identifying the tools and languages most relevant to SUSE’s ecosystem. Analyses of DevOps, SREs, SysAdmins, and cloud-native roles further quantify SUSE’s addressable market and assess alignment with industry trends.
The Google Trends analysis adds an external perspective on brand interest, showing how public attention toward SUSE and Red Hat evolves over time and across regions. Related search terms provide insight into how each brand is associated with specific technologies and topics, highlighting opportunities for increased visibility or repositioning.
The GitHub repository overview offers a look at SUSE’s open-source footprint relative to Red Hat, focusing on repository activity, stars, forks, issues, and programming language diversity. Trends in repository creation and updates illustrate innovation momentum and community engagement, while language usage highlights SUSE’s technical direction and ecosystem breadth.
The SUSE GitHub Issues analysis deepens understanding of community interaction by examining issue volume, resolution speed, contributor patterns, and sentiment expressed in issue titles, bodies, and comments. Although based on one month of data, this analysis provides meaningful insights into developer satisfaction, recurring challenges, and project health. Categorization of issues helps identify potential areas for product improvement or documentation enhancement.
The Reddit analysis extends sentiment exploration into broader public discussions, comparing SUSE-related and Red Hat–related posts and comments. Despite the one-month limitation, sentiment trends, discussion categories, and key influencers reveal how SUSE is perceived in informal technical communities and what factors drive positive or negative sentiment.
Together, these components create a holistic view of SUSE’s position across developer preferences, market interest, community engagement, and open-source activity. The combined insights support strategic decision-making for product development, community outreach, marketing, and competitive positioning—helping SUSE understand where it stands today and where the strongest opportunities exist within the modern infrastructure and cloud-native ecosystem.
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4 days ago by terezacerna | Reply
Demo View: LINK
Full Power BI Report: LINK (additional access may be required)
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4 days ago by terezacerna | Reply
Obstacles and limitations I have encountered:
I was limited with the amount of items I could have scraped with API from GitHub and Reddit and I only could have got the last month of data from both platforms.
Since I last explored, the cognitive AI analysis like sentiment analysis or categorization was moved by Microsoft behind a separate licensing, which we don't have available in SUSE. Thus I had to change my plan and use Gemini outside of Power BI for these analysis.
Analyzing Stack Overflow could and should take much longer to really get a real profile of a SUSE community user. I would however need a help from a person who knows SUSE products technically well and potentially have some marketing knowledge as well.
The next steps of this analysis could be to analyze when a community user becomes a paying customer.
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about 15 hours ago by ovalants | Reply
I found the analysis on SUSE vs. Red Hat fascinating! It's intriguing to see the comparison basketball stars using Google Trends and GitHub data.
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