analyticsdurgesh

analyticsdurgesh / DBT-Project-Pipeline

Public

Public Snowflake and dbt pipeline transforming Blinkit retail sales data into staging and mart analytics models.

analytics-engineering data-pipeline dbt retail-analytics snowflake
15
1
100% credibility
Found May 31, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A public portfolio project that loads retail sales data from cloud storage into a data warehouse, then transforms it through cleaning and aggregation steps to produce ready-to-use analytics tables for reporting.

How It Works

1
🔍 Explore the project

You discover a data project that transforms retail sales into clean analytics tables you can share with your team.

2
🔗 Connect your data tools

You create a simple local file with your cloud storage and warehouse details so the project can access your data.

3
▶️ Run the pipeline

With one click, your raw sales data flows through cleaning and transformation steps until everything is organized and ready.

4
🧪 Watch quality checks pass

The project automatically verifies that your data has no missing important fields and all values make sense.

📊 Your analytics are ready

You now have clean sales tables and outlet summaries that are ready for dashboards and business reports.

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Star Growth

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AI-Generated Review

What is DBT-Project-Pipeline?

A complete analytics pipeline that moves retail sales data from AWS S3 into Snowflake, then transforms it through dbt into clean staging views and aggregated mart tables ready for dashboards. Built in Python, it handles the full workflow: Snowflake infrastructure setup, raw data loading, dbt transformations, and row-count verification. The project includes a single command that bootstraps everything and prints final row counts for quick validation.

Why is it gaining traction?

The one-command runner is the hook. Instead of manually executing SQL scripts, managing dbt profiles, and running builds separately, you execute one Python script that handles environment setup, Snowflake bootstrap, dbt debug, dbt build, and verification. The architecture diagram and clear README make the data flow obvious. Credential handling uses local-only files that are git-ignored, which is the right pattern for public repositories.

Who should use this?

Analytics engineers early in their dbt journey will find this most useful. The project demonstrates the staging-to-mart pattern, key generation for fact tables, and dbt schema tests in a contained, runnable example. Data engineers evaluating Snowflake for the first time can see the full ingestion pattern from S3 through to analytics-ready tables. This is a learning project, not a production template.

Verdict

With 15 stars and a 1.0% credibility score, this is a portfolio piece for the author rather than a battle-tested tool. The documentation is solid and the code is readable, but there is no CI/CD, limited test coverage, and no community feedback. Use it as a learning reference for dbt project structure and Snowflake integration patterns, but do not adopt it as-is for production workloads.

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