Scenario: A financial institution wants to implement real-time fraud detection. Outline the key components and technologies you would recommend for building such a system.
- Apache Beam for data processing, RabbitMQ for message queuing, Neural networks for fraud detection, Redis for caching
- Apache Kafka for data ingestion, Apache Flink for stream processing, Machine learning models for fraud detection, Apache Cassandra for storing transaction data
- Apache NiFi for data ingestion, Apache Storm for stream processing, Decision trees for fraud detection, MongoDB for storing transaction data
- MySQL database for data storage, Apache Spark for batch processing, Rule-based systems for fraud detection, Elasticsearch for search and analytics
Implementing real-time fraud detection in a financial institution requires a robust combination of technologies. Apache Kafka ensures reliable data ingestion, while Apache Flink enables real-time stream processing for immediate fraud detection. Machine learning models trained on historical data can identify fraudulent patterns, with Apache Cassandra providing scalable storage for transaction data.
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