Redpanda is a streaming data platform compatible with Apache Kafka, designed for high-performance, data safety, and transactional workloads, making it easy to integrate with existing infrastructures like Apache Spark. Spark, an analytics engine developed to overcome the limitations of the MapReduce algorithm, supports iterative functions on Resilient Distributed Datasets (RDDs) and has evolved to include Spark SQL, MLLib, and Spark Streaming, which allows for integration with live data streams from sources such as Redpanda. Spark Streaming processes streams in micro-batches using the Spark engine, while Structured Streaming extends the Spark SQL API to handle streams similarly to static RDDs, making it straightforward to read messages from a Redpanda topic by creating a SparkSession and a streaming DataFrame. The integration allows for building complex data processing pipelines with high performance, scalability, and durability. Moreover, Redpanda's Wasm Data Transforms offer a way to perform simple transformations directly within Redpanda using WebAssembly functions, potentially replacing common Spark functions like map() or filter(), and are ideal for tasks like data redaction for GDPR compliance. For more complex processing involving aggregations or joining streams, tools like Spark and Apache Flink are recommended as supplements to Wasm Transforms.