djouallah

djouallah / analytics-as-code

Public

https://djouallah.github.io/analytics-as-code/

15
1
100% credibility
Found Mar 11, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

This project creates a complete, code-defined pipeline to fetch, process, and visualize public Australian electricity market data like prices and generation in a no-server browser dashboard.

How It Works

1
🔍 Find the energy tracker

You stumble upon this handy project that lets everyday folks like you keep tabs on Australia's electricity prices and power plant activity without any hassle.

2
💾 Save your copy

Grab a personal copy of the project files from the webpage to make it your own.

3
🔗 Link your data home

Connect it to a secure online storage spot where all your organized data will live.

4
🚀 Start the data flow

Hit go, and it automatically grabs fresh market reports, cleans them up, and builds neat tables of prices and power output.

5
⏰ Turn on auto-updates

Flip the switch for regular refreshes every day or even more often, so your info is always up-to-date.

6
📊 Peek at your dashboard

Open a simple webpage in your browser to explore colorful charts of energy prices, generation by region, and trends over time.

✅ Dive in and decide

Pick any date range, download spreadsheets, and get clear insights into how the power grid works—easy as pie!

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Star Growth

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AI-Generated Review

What is analytics-as-code?

Analytics as code puts your entire analytics stack—ingestion, transformation, storage, and visualization—into a single Git repo, using dbt with DuckDB for ephemeral in-memory processing and an Iceberg REST catalog for persistent state. It pulls public energy market CSVs, runs SQL/Python models via GitHub Actions, and serves an interactive HTML dashboard at https://djouallah.github.io/analytics-as-code/ with DuckDB-WASM querying optimized files client-side. No servers, no orchestrators: just code that deploys anywhere.

Why is it gaining traction?

It ditches infrastructure for pure Git portability, with CI validating SQL changes and daily tests ensuring quality before dashboard updates. Grain reduction keeps browser queries snappy—5-min data for recent views, hourly/daily aggregates for history—while staying under GitHub Pages limits. Devs dig the zero-ops hook: tweak models, push, and get fresh analytics without managing clusters.

Who should use this?

Data engineers prototyping energy analytics pipelines, especially AEMO dispatch/SCADA data fans needing quick intraday dashboards. Solo analysts or small teams avoiding Airflow/Dagster overhead for file-based incrementals. Fabric users eyeing a dbt-DuckDB port.

Verdict

Grab it for proof-of-concept analytics as code if you're in energy data—solid design, easy local setup with env vars. With 10 stars and 1.0% credibility, it's early and niche; fork and harden tests before production.

(198 words)

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