Filter-Map-Reduce is a programming paradigm commonly used in data processing, where data is first filtered to select relevant elements, then transformed using a mapping function, and finally aggregated using a reduction function. This approach is highly efficient for parallel and distributed computing, making it ideal for handling large datasets in frameworks like Apache Hadoop and Apache Spark.