I recently explored two approaches for ingesting and transforming data using Databricks and Snowflake. Here’s a hands-on comparison based on my implementation:
🧪 Approach 1: Databricks + Snowflake
- Used Databricks to ingest files and apply Spark-based transformations
- Loaded the processed data into Snowflake
- Connected Power BI for dashboarding and reporting
✅ Best suited for: Robust, scalable pipelines with complex transformation logic
⚡ Approach 2: Snowflake-only (UI-driven)
- Uploaded files to internal stages using the Snowflake Web UI
- Loaded data using built-in
COPY INTOcommands - Performed light SQL-based transformations inside the Snowflake UI
✅ Best suited for: Quick POCs and no-code prototyping
Architecture: Comparative setup of Databricks + Snowflake vs Snowflake-only Ingestion for scalable data pipelines.
🛠️ Orchestration
Both approaches can be automated using Azure Data Factory or other orchestration tools for daily data pipeline execution.
💡 Key Takeaway
Use Snowflake-only ingestion for simplicity and speed in prototyping. Choose Databricks + Snowflake when scalability and advanced transformations are needed.
📂 GitHub Repository
All steps, configurations, and code are available here:
👉 Explore the comparison project on GitHub
🤝 I'd love to hear how you're leveraging Snowflake or Databricks in your own data workflows. Feel free to share your experiences!
#DataEngineering #Snowflake #Databricks #PowerBI #ETL #DataPipelines #BigData #ADF #CloudAnalytics #UIbasedIngestion #NoCode #ModernDataStack