Spark Streaming integrates seamlessly with various data sources such as Kafka, Flume, and Kinesis, making it easy to consume and process streaming data from different sources. It supports both windowed and stateful operations, enabling advanced stream processing tasks like event time windowing and sliding window computations.

Spark provides the capability to process real-time data streams through its built-in streaming library, which allows developers to ingest and analyze streaming data in near real-time. By leveraging the micro-batch processing model, Spark Streaming enables the processing of data as it arrives in small, user-defined batches. This allows for low-latency processing and the ability to handle high-throughput data streams.

In addition, Spark's integration with the Spark Structured Streaming API enables users to write stream-processing queries using SQL-like syntax, opening up the power of Spark to those familiar with SQL and relational database concepts. This makes it easier to express complex streaming operations using a familiar querying language.