From c20557a036b31a1781a168dc3cb941673750690b Mon Sep 17 00:00:00 2001 From: Cameron Date: Sun, 11 Jan 2026 21:06:10 +0000 Subject: [PATCH] Update readme.md --- readme.md | 276 +++++++++++++++++++++++------------------------------- 1 file changed, 116 insertions(+), 160 deletions(-) diff --git a/readme.md b/readme.md index 6b9048d..b235c1d 100644 --- a/readme.md +++ b/readme.md @@ -1,65 +1,39 @@ - - - - -[![Contributors][contributors-shield]][contributors-url] -[![Forks][forks-shield]][forks-url] -[![Stargazers][stars-shield]][stars-url] -[![Issues][issues-shield]][issues-url] -[![MIT License][license-shield]][license-url] -[![LinkedIn][linkedin-shield]][linkedin-url] - -
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project_title

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Banking Transaction Pipeline (Python • Spark • S3)

- project_description + A Python-based Spark pipeline that ingests banking-style transactions into S3 and processes them through a Bronze → Silver → Gold architecture with data quality validation.
- Explore the docs » -
-
- View Demo - · - Report Bug - · - Request Feature +

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Table of Contents
  1. About The Project +
    2. +
  2. + Architecture +
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  4. Usage
    5. +
  5. Data Quality & Validation
    6. +
  6. Outputs
  7. Roadmap
  8. Contributing
  9. License
    10. @@ -83,165 +60,144 @@ ## About The Project -[![Product Name Screen Shot][product-screenshot]](https://example.com) +This project simulates a **banking transaction data pipeline** using **Python + Apache Spark** with an **S3-backed data lake**. It demonstrates how raw transactional data can be ingested, validated, transformed, and curated into analytics-ready datasets using a **Bronze → Silver → Gold** architecture. -Here's a blank template to get started: To avoid retyping too much info. Do a search and replace with your text editor for the following: `github_username`, `repo_name`, `twitter_handle`, `linkedin_username`, `email_client`, `email`, `project_title`, `project_description` +### Key Features + +- **Batch ingestion** of banking-style transaction data into an S3-backed Bronze layer +- **Bronze → Silver → Gold** lakehouse-style architecture +- **Data validation gates** (required fields, schema enforcement, duplicates, constraints) +- **Curated datasets** designed for BI and ad-hoc analytics +- Designed with **analytics engineering principles**: reliable outputs, repeatability, clear modeling

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    -### Built With +## Architecture -* [![Next][Next.js]][Next-url] -* [![React][React.js]][React-url] -* [![Vue][Vue.js]][Vue-url] -* [![Angular][Angular.io]][Angular-url] -* [![Svelte][Svelte.dev]][Svelte-url] -* [![Laravel][Laravel.com]][Laravel-url] -* [![Bootstrap][Bootstrap.com]][Bootstrap-url] -* [![JQuery][JQuery.com]][JQuery-url] +The pipeline follows a lakehouse pattern where each layer has a clear responsibility. + +### Bronze (Raw) + +**Purpose** +- Store transactions “as received” with minimal transformation + +**Why it matters** +- Preserves an auditable source of truth +- Enables reprocessing into Silver/Gold without re-ingesting from the source + +--- + +### Silver (Clean & Validated) + +**Purpose** +- Standardize schema and datatypes +- Validate records and isolate invalid data +- Deduplicate and normalize for analysis + +**Typical transformations** +- Datatype casting (timestamps, numeric amounts) +- Standardized column names and formats +- Deduplication rules (e.g., transaction_id collisions) + +--- + +### Gold (Curated & Analytics-Ready) + +**Purpose** +- Create business-friendly datasets and aggregations for analytics and BI + +**Example outputs** +- Daily transaction counts & totals +- Account/customer-level summaries +- Error/invalid transaction metrics

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    - -## Getting Started +### Notes -This is an example of how you may give instructions on setting up your project locally. -To get a local copy up and running follow these simple example steps. +- **Bronze** should contain raw ingested data (audit layer) +- **Silver** should contain cleaned and validated records +- **Gold** should contain curated outputs ready for analytics and BI -### Prerequisites - -This is an example of how to list things you need to use the software and how to install them. -* npm - ```sh - npm install npm@latest -g - ``` - -### Installation - -1. Get a free API Key at [https://example.com](https://example.com) -2. Clone the repo - ```sh - git clone https://github.com/github_username/repo_name.git - ``` -3. Install NPM packages - ```sh - npm install - ``` -4. Enter your API in `config.js` - ```js - const API_KEY = 'ENTER YOUR API'; - ``` +For deeper implementation details, see the code in this repo.

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    +--- +## Data Quality & Validation - -## Usage +The pipeline applies checks to prevent bad data from reaching curated datasets. -Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources. +**Common checks include:** +- Required fields (e.g., `transaction_id`, `account_id`, `amount`, `timestamp`) +- Schema enforcement (consistent datatypes between runs) +- Duplicate detection (e.g., `transaction_id` collisions) +- Value constraints (e.g., amounts must be non-negative) +- Timestamp parsing and validation +- Quarantine routing for invalid records (optional, stored under `errors/`) -_For more examples, please refer to the [Documentation](https://example.com)_ +These checks keep the Silver and Gold layers consistent and trustworthy for downstream analytics.

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    +--- +## Outputs - -## Roadmap +**Example S3 layout:** +```text +s3:/// + bronze/banking/ + silver/banking/ + gold/banking/ + errors/banking/ +``` -- [ ] Feature 1 -- [ ] Feature 2 -- [ ] Feature 3 - - [ ] Nested Feature +Gold-layer datasets are structured to support: -See the [open issues](https://github.com/github_username/repo_name/issues) for a full list of proposed features (and known issues). +Business intelligence tools (Tableau / Power BI) + +Ad-hoc querying (Spark SQL / DuckDB) + +Downstream analytics and metric definitions

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    +Roadmap + Add orchestration (Airflow / Dagster) - -## Contributing + Implement incremental processing and partitioning -Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**. + Add automated pipeline health checks (row counts, null rates, duplicates) -If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". -Don't forget to give the project a star! Thanks again! + Add unit tests for validation logic -1. Fork the Project -2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`) -3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`) -4. Push to the Branch (`git push origin feature/AmazingFeature`) -5. Open a Pull Request + Add monitoring, alerting, and run logs + + Add CDC-style ingestion simulation + +See the open issues + for a full list of proposed features and known issues. + +

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    License + +Distributed under the MIT License. See LICENSE.txt for more information.

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    +Contact +Cameron Seamons +Ogden, Utah +Email: CameronSeamons@gmail.com +LinkedIn: linkedin_username - -## License +Project Link: https://github.com/github_username/repo_name -Distributed under the MIT License. See `LICENSE.txt` for more information. - -

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    - - - - -## Contact - -Your Name - [@twitter_handle](https://twitter.com/twitter_handle) - email@email_client.com - -Project Link: [https://github.com/github_username/repo_name](https://github.com/github_username/repo_name) - -

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    - - - - -## Acknowledgments - -* []() -* []() -* []() - -

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