Apache Spark

The Advanced Spark Pipeline: Unified Streaming and Batch: The bank uses Spark's Structured Streaming to ingest the live firehose of 50,000 transactions per second from a messaging queue like Apache Kafka. Distributed Joins: Spark distributes this live stream across a cluster of 50 powerful servers. While holding the live data in memory, Spark instantly joins it with a massive 10-terabyte historical database (stored in a data lake like AWS S3 or Hadoop) to check the customer's past spending habits. Machine Learning at Scale: Still running in parallel across all 50 servers, Spark feeds the joined data into a distributed Machine Learning model (using Spark MLlib). The model scores each transaction for fraud probability. Fault Tolerance (Resiliency): If Server #14 literally catches fire and dies in the middle of processing its chunk of transactions, Spark's core engine notices instantly. It grabs the lost chunk of data, reroutes it to Server #15, and finishes the calculation without crashing the application or losing a single swipe. Micro-batch Output: The flagged fraudulent transactions are instantly written to a database that triggers an immediate text message to the customer ("Did you just spend $5,000 in another country?").