Apache Flink, a popular framework for data stream processing, is adept at handling complex processing logic, including aggregations, joins, and windowing, thanks to its stateful processing capabilities and advanced state snapshotting mechanism, which ensures recovery with exactly-once semantics. This technical tutorial addresses the need to inspect or modify Flink job state snapshots, such as Savepoints and Checkpoints, using the State Processor API, which requires a deep understanding of Flink operator states. The tutorial provides an example of a Flink job that reads data from an Apache Kafka topic, illustrating the maintenance of KafkaSource state and the extraction of Kafka partition-offset state from Flink's savepoints or checkpoints using the State Processor API. It also details the serialization process of KafkaSource state into savepoints and checkpoints and demonstrates how to deserialize this data to extract specific state information, while highlighting the complexities and knowledge required to effectively use the State Processor API for analyzing and modifying Flink's state management in streaming data environments.