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Financial analytics using Bigtable on GCP

Why Choose This Project?

Financial institutions generate massive amounts of time-series data from stock prices, transactions, and market feeds. Google Cloud Bigtable is a high-performance, NoSQL, distributed database optimized for real-time analytics on large datasets.

This project is ideal for students to learn scalable data storage, real-time analytics, and cloud-native financial applications.

What You Get

Real-time ingestion of financial data (e.g., stock prices, transactions)
Scalable storage for millions of records using Bigtable
Querying and analytics on large datasets
Dashboard visualization of trends, averages, and alerts
Optional anomaly detection for unusual market activity

Key Features

Feature	Description
High-Speed Data Storage	Store large-scale time-series financial data efficiently
Real-Time Analytics	Query and analyze data in near real-time
Time-Series Data Support	Optimized for sequential financial data like stock prices
Data Visualization	Interactive dashboards with graphs, charts, and trends
Anomaly Detection	Identify unusual transactions or price fluctuations
Scalability	Handles millions of records without performance degradation
Integration Ready	Integrates with GCP services like Dataflow, BigQuery, and AI tools
Security & Compliance	Access control, encryption, and audit logs

Technology Stack

Layer	Tools/Technologies
Frontend	HTML5, CSS3, Bootstrap 5, JavaScript
Backend	Python (Flask/Django) or Node.js (Express)
Database	Google Cloud Bigtable (NoSQL, scalable)
Data Ingestion	Google Cloud Dataflow / Pub/Sub (optional)
Analytics	Pandas, NumPy, or BigQuery integration
Visualization	Chart.js, Plotly, or Google Data Studio
Authentication	Firebase Auth or Google Cloud IAM
Monitoring	Stackdriver / Cloud Monitoring

GCP Services Used

GCP Service	Purpose
Cloud Bigtable	Store high-volume financial time-series data
Cloud Dataflow	Stream or batch data processing for ingestion
Pub/Sub	Real-time message bus for incoming financial data
BigQuery	Optional analytics or aggregation queries
Cloud Monitoring	Monitor database performance and alerts
Cloud IAM	Access control for secure operations
Cloud Storage	Optional storage for raw or historical datasets

Working Flow

Data Ingestion
Financial data (e.g., stock tickers, transactions) is pushed to Cloud Pub/Sub.
Stream Processing
Cloud Dataflow pipelines process and transform data as needed.
Storage in Bigtable
Processed time-series data is stored in Bigtable for efficient querying.
Analytics Queries
Backend queries Bigtable for trends, averages, volatility, and anomalies.
Visualization
Results are displayed in dashboards using charts, graphs, or tables.
Alerts
Optional alerts for unusual market events or anomalies via email/SMS.