5000+ Computer Science Projects | Degree | Diploma | MCA | BCA

CLOUD COMPUTING & DEVOPS
Reviews

Serverless data warehouse solution with Snowflake on AWS

Why Choose This Project?

Modern organizations need to analyze large volumes of structured and semi-structured data without worrying about managing infrastructure. Snowflake is a serverless cloud data warehouse that runs on AWS and allows scalable analytics, data sharing, and ETL processing.

This project is ideal for students to learn cloud data warehousing, serverless architecture, and analytics pipelines.

What You Get

Fully managed, serverless data warehouse
ETL/ELT pipeline for ingesting structured and semi-structured data
Real-time or batch analytics on large datasets
Scalable queries with separate compute clusters
Integration with BI tools or dashboards
Data sharing between teams or external partners

Key Features

Feature	Description
Serverless Architecture	No infrastructure management; compute scales automatically
Data Ingestion	ETL/ELT pipelines from sources like S3, databases, APIs
Structured & Semi-Structured Data	Support for JSON, CSV, Parquet, Avro, etc.
Scalable Analytics	Multi-cluster compute allows concurrent queries without bottlenecks
Data Sharing & Collaboration	Share data securely across departments or organizations
Time-Travel & Versioning	Query historical data and recover from mistakes
Secure Access & Compliance	Role-based access control, encryption, audit logs
Integration with BI Tools	Connect to Tableau, Power BI, or Looker for visualization

Technology Stack

Layer	Tools/Technologies
Data Storage	AWS S3 (landing/raw zone), Snowflake Staging & Tables
Data Processing	Snowflake SQL for transformations, Snowpipe for streaming
ETL/ELT Pipeline	Python + Snowflake Connector, AWS Glue (optional)
Analytics	Snowflake SQL, BI Tools (Tableau, Power BI, Looker)
Authentication	Snowflake Users, AWS IAM for S3 access
Monitoring	Snowflake Resource Monitors, CloudWatch (optional)

AWS & Snowflake Services Used

Service	Purpose
Snowflake	Serverless data warehouse, data processing, analytics
AWS S3	Landing zone for raw data and backups
AWS Glue (optional)	ETL orchestration and transformation
Snowpipe	Continuous ingestion for streaming data
AWS IAM	Secure access to S3 buckets for Snowflake
CloudWatch / Snowflake Monitors	Monitor queries, compute usage, and storage

Working Flow

Data Collection & Landing
Source data from applications, databases, or external feeds is stored in AWS S3.
Ingestion to Snowflake
Snowpipe continuously or batch-loads data from S3 into Snowflake tables.
Transformation & Analytics
Data is transformed using Snowflake SQL queries or Python ETL scripts.
Querying & Dashboarding
Business users query the warehouse directly or via BI tools for reports and insights.
Scaling & Management
Snowflake automatically scales compute clusters for concurrent queries without downtime.