[PLDG Submission]: Data Source - @GautamBytes

[GautamBytes]

PLDG Proof-of-Work Submission

Contributor

• GitHub Username: @GautamBytes
• Discord Username (if applicable): gautam_092528

Gist Link

Link to your notebook gist: https://gist.github.com/GautamBytes/3b9b6f424159a6f030ee3756c203db7b

Data Source Audited

Pipeline audited:

• Staging: N/A (Mart Analysis)
• Intermediate: N/A (Mart Analysis)
• Mart: oso.timeseries_metrics_by_project_v0', 'oso.projects_v1
• Metrics Analyzed: GITHUB_commits_monthly, GITHUB_full_time_developers_monthly, GITHUB_active_developers_monthly

Key Findings

Provide a 2-3 sentence summary of your analysis:

• Velocity Resilience: Filecoin's commit velocity has remained consistent throughout 2024 despite market volatility, indicating a decoupled relationship between price and engineering activity.
• Identified a clear distinction in maturity stages: drand operates as a specialized team with a high Core Ratio (~38%), while Filecoin functions as a broad ecosystem with a lower ratio (~9%).
• Data Completeness: Confirmed data availability for all target projects back to Jan 2023, with successful cross-reference between projects_v1 and timeseries_metrics_v0.

Checklist

Before submitting, verify that your notebook:

• Runs successfully with uv run marimo edit your_notebook.py
• Passes structure validation with uv run marimo check your_notebook.py
• Includes all 3 required checks (presence, date coverage, duplicates)
• Audits all 3 pipeline stages (staging → intermediate → mart)
• Contains visualizations using plotly
• Has clear markdown documentation explaining analysis
• Separates concerns (markdown, SQL, and Python in separate cells)
• Follows naming convention: {data_source}_{username}.py
• Pipeline documentation section is complete

Additional Notes

Any additional context, questions, or issues encountered:
NA

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Next Steps: OSO maintainers will review your submission. If approved, you'll be asked to submit a PR with your notebook.

[ccerv1]

Thanks for the submission @GautamBytes! I can see you put effort into the Protocol Labs ecosystem analysis, and the plotly visualizations look good.

However, this submission doesn't quite meet our proof-of-work requirements yet. The main issue is that this is a mart-level analysis rather than a pipeline audit. Let me break down what needs to change:

What's Missing

1. Pipeline Coverage (Critical)

You marked staging and intermediate as "N/A (Mart Analysis)" - but auditing the full pipeline is the core requirement. You need to trace data through:

• Staging: oso.stg_github__events or related staging tables
• Intermediate: oso.int_* models
• Mart: oso.timeseries_metrics_by_project_v0

The goal is to understand and validate how data flows through our pipeline, not just analyze the end result.

2. Required Quality Checks (Critical)

The 3 required checks are:

1. Presence - Row counts at each pipeline stage (staging → intermediate → mart)
2. Date Coverage - Time-series range validation and gap detection
3. Duplicates - Primary key uniqueness checks

Your "smoke tests" check the analysis outputs, not the pipeline data quality. We need SQL queries that validate the underlying data.

3. Cell Organization

Per our guidelines, cells should separate concerns:

• Markdown cells: Pure mo.md() only
• SQL cells: Pure mo.sql() queries
• Python cells: Pure logic/visualization

Right now several cells mix all three together.

4. pyoso Setup

Please use the standard setup pattern from the template:

@app.cell
def setup_pyoso():
    from pyoso import Client
    client = Client()
    pyoso_db_conn = client.dbapi_connection()
    return mo, pyoso_db_conn

The custom API key input flow won't work during our review process.

Recommendation

I'd suggest starting fresh from health_check_template.py. Pick the GitHub data pipeline you were analyzing and trace it from staging through to mart. The template has the structure and required checks built in.

Once revised, update your gist and comment here - happy to take another look!

[GautamBytes]

Hey @ccerv1 , sorry for the delay. I was out of hometown due to my mom's medical checkup. Will be improving based on your suggestions and let you know soon. Thanks!

[GautamBytes]

I have completely refactored the notebook (protocol_labs_gautam.py) to meet the Pipeline Audit requirements. You can have a look whenever feasible.

[kenyiu]

@GautamBytes hi, i just tried pulling the notebook and run locally, but it's not working. May I know if you have uploaded the right version that could be run? Thanks.

[GautamBytes]

@GautamBytes hi, i just tried pulling the notebook and run locally, but it's not working. May I know if you have uploaded the right version that could be run? Thanks.

Hey Sorry for the inconvenience, just updated the gist, you can check it here - https://gist.github.com/GautamBytes/3b9b6f424159a6f030ee3756c203db7b

[kenyiu]

@GautamBytes hi, i just tried pulling the notebook and run locally, but it's not working. May I know if you have uploaded the right version that could be run? Thanks.

Hey Sorry for the inconvenience, just updated the gist, you can check it here - https://gist.github.com/GautamBytes/3b9b6f424159a6f030ee3756c203db7b

Thanks for resubmitting. May I know the environment for you to run the notebook? Since the notebook looks quite broken (e.g. the markdown and graph cannot be displayed) when i am testing it locally. Thanks.

[GautamBytes]

I am running on Local VS Code using uv . Its displaying graphs and charts correctly

[GautamBytes]

you just need to put your api key and i believe it should work fine. Let me know if it still seems broken

[kenyiu]

I see that the notebook is updated 6 hours ago after my comment. Is this the correct version I should check? Thanks.

[GautamBytes]

I see that the notebook is updated 6 hours ago after my comment. Is this the correct version I should check? Thanks.

yes

[kenyiu]

Thanks for the revision @GautamBytes — good improvement from the original submission. A few things to revisit before we can approve:

Must fix:

• The presence check (Check 1) counts rows across the entire pipeline, not just for Filecoin. Think about what it actually means to compare those three numbers — do they represent the same population of records? Better get them scoped so you are comparing the data pipeline correctly.
• The summary conclusion about "pipeline integrity" is based on those same counts. Once you reconsider the scoping, revisit whether that conclusion still holds.

Good to fix:

• Your markdown cells use @app.cell but the template uses @app.cell(hide_code=True) for all markdown header cells for readability. Good if you can follow the template convention.
• The template includes a written interpretation after each check result. Yours shows the numbers but doesn't explain what they mean. Add a sentence or two per check.
• The notebook's __generated_with is 0.15.0 but the template uses 0.19.2.

Once these are addressed, update your gist and let us know! Thanks.

[GautamBytes]

Thanks for the review @kenyiu , I have updated the gist as per your suggestions!