Introducing Spark Streaming
As you’ve seen so far, Spark Streaming is a powerful real-time data processing framework built on Apache Spark. It extends the capabilities of the Spark engine to support high-throughput, fault-tolerant, and scalable stream processing. Spark Streaming enables developers to process real-time data streams using the same programming model as batch processing, making it easy to transition from batch to streaming workloads.
At its core, Spark Streaming divides the real-time data stream into small batches or micro-batches, which are then processed using Spark’s distributed computing capabilities. Each micro-batch is treated as a Resilient Distributed Dataset (RDD), Spark’s fundamental abstraction for distributed data processing. This approach allows developers to leverage Spark’s extensive ecosystem of libraries, such as Spark SQL, MLlib, and GraphX, for real-time analytics and machine learning tasks.
