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• While executing any streaming aggregation query, the Spark SQL engine internally maintains the intermediate aggregations as fault-tolerant state. This state is structured as key-value pairs, where the key is the group, and the value is the intermediate aggregation.

• These data streams can be nested from various sources, such as ZeroMQ, Flume, Twitter, Kafka, and so on. Spark Streaming breaks the data into small batches, and these batches are then processed by Spark to generate the stream of results, again in batches.

• The sum and count aggregates are theb performed on partial data – only the new data. The Spark Streaming engine stores the state of aggregates (in this case the last sum/count value) after each query in memory or on disk when checkpointing is enabled.

While executing any streaming aggregation query, the Spark SQL engine internally maintains the intermediate aggregations as fault-tolerant state. This state is structured as key-value pairs, where the key is the group, and the value is the intermediate aggregation.

Spark streaming is an extension of the core Spark API. It can be used to process high-throughput, fault-tolerant data streams. These data streams can be nested from various sources, such as ZeroMQ, Flume, Twitter, Kafka, and so on.

These data streams can be nested from various sources, such as ZeroMQ, Flume, Twitter, Kafka, and so on. Spark Streaming breaks the data into small batches, and these batches are then processed by Spark to generate the stream of results, again in batches.

Note that this is a streaming DataFrame which represents the running word counts of the stream. This lines SparkDataFrame represents an unbounded table containing the streaming text data. This table contains one column of strings named “value”, and each line in the streaming text data becomes a row in the table.