Update README.md

Author: Amar825

README.md

@@ -156,8 +156,8 @@ All transformations were written in modular `.sql` models, configured via `dbt_p
 
 It shows how each model in the pipeline is derived from raw external source tables in BigQuery:
 
-- ✅ **Sources** (`SRC`) like `claims_data_external`, `patient_data_external`, and `ehr_data_external` represent external tables that directly query files stored in Google Cloud Storage
-- ✅ **Models** (`MDL`) like `high-claim-patients`, `chronic-conditions-summary`, and `health-anomalies` represent transformed tables built using SQL logic in DBT 
+-  **Sources** (`SRC`) like `claims_data_external`, `patient_data_external`, and `ehr_data_external` represent external tables that directly query files stored in Google Cloud Storage
+-  **Models** (`MDL`) like `high-claim-patients`, `chronic-conditions-summary`, and `health-anomalies` represent transformed tables built using SQL logic in DBT 
 
 
 
@@ -172,7 +172,7 @@ It shows how each model in the pipeline is derived from raw external source tabl
 To ensure data quality and trust in the pipeline, I implemented column-level tests and added documentation using `schema.yml` files in DBT.
 DBT allows us to define tests and metadata **alongside our models** — all inside YAML. These tests run automatically using `dbt test`.
 
-#### ✅ Why I Used `schema.yml`:
+####  Why I Used `schema.yml`:
 
 - To enforce data integrity on critical columns (`not_null`, `unique`)
 - To validate raw data coming from external sources
@@ -222,7 +222,7 @@ This ensures:
 
   <p align="center"> <img src="./images/github-actions-ci-success.png" alt="CI Passing" width="700"/> </p>  `
 
-✅ Example snippet from `ci.yml`:
+ Example snippet from `ci.yml`:
 
 ```yaml
 on:
@@ -259,7 +259,7 @@ Triggered on push to main
 
   <p align="center"> <img src="./images/github-actions-cd-success.png" alt="CD to Production" width="700"/> </p>
 
-✅ Secure deployment:
+ Secure deployment:
 
 GCP credentials stored as GitHub Secrets
 
@@ -273,15 +273,15 @@ gcp-key.json and profiles.yml are generated at runtime (not stored in repo)
 - Practiced writing modular, testable SQL models with automated validations
 - Built an end-to-end pipeline that mirrors real-world engineering workflows
 
-## ✅ Conclusion
+##  Conclusion
 
 This project started as a hands-on learning exercise and became a full-stack, automated data engineering pipeline. I worked with industry-standard tools (GCP, DBT, GitHub Actions), built my own data sources, and pushed transformations all the way to production.
 
 It reflects both the technical skills I’ve developed and my drive to learn independently and build real, usable solutions.
 
 ---
 
-## 🙏 Acknowledgements
+##  Acknowledgements
 
 This project was built by closely following a YouTube tutorial by [DATA TIME](https://www.youtube.com/playlist?list=PLs9W2D7jqlTXbHWkpNUzIC_G8KpLMH6yZ), which covered how to build an end-to-end data pipeline using DBT, BigQuery, and GitHub Actions.