Distributed Transaction Analytics and Monitoring System

Built a high-throughput, distributed transaction analytics platform designed for financial institutions to monitor, process, and analyze millions of transactions per day. The system provides real-time insights into transaction patterns, detects anomalies, and generates operational reports. Leveraging distributed data technologies, it ensures seamless scalability and fault tolerance while handling large volumes of streaming data.

Project Overview: Built a high-throughput, distributed transaction analytics platform designed for financial institutions to monitor, process, and analyze millions of transactions per day. The system provides real-time insights into transaction patterns, detects anomalies, and generates operational reports. Leveraging distributed data technologies, it ensures seamless scalability and fault tolerance while handling large volumes of streaming data.

Key Features:

  • Real-Time Transaction Monitoring: Continuously ingests and processes transaction streams to provide instant visibility into transaction volumes, types, and geographies.
  • Anomaly Detection: Detects suspicious or anomalous transaction patterns using customizable rule-based logic, triggering alerts for further investigation.
  • Scalable Data Processing: Utilizes distributed technologies to process millions of transactions per day with minimal latency and high reliability.
  • Historical Data Analysis: Allows users to run queries and generate reports on transaction history to identify trends and insights over time.

Technologies Used:

  • Backend: Java and Apache Kafka for ingesting real-time transaction streams, ensuring low-latency processing and fault tolerance.
  • Data Processing: Apache Spark for distributed batch and stream processing of large transaction datasets.
  • Database: Oracle/SQL for storing transactional data and maintaining long-term historical records.
  • Cloud: Hosted on AWS or Azure, utilizing cloud storage and services for seamless scalability.
  • CI/CD: Jenkins/Azure Pipelines for automated testing and deployment of updates to ensure continuous operational stability.

Exciting Feature: Includes real-time customizable dashboards where users can set up filters and rules to monitor specific transaction types, regions, or amounts. The system also provides anomaly detection alerts, enabling quick response to irregularities.

This project highlights my expertise in working with distributed systems, real-time data processing using Kafka and Spark, and handling large-scale financial transaction data, making it highly relevant for the fast-paced financial industry.