Structured Streaming in Spark

What distinguishes Structured Streaming from Spark Streaming in terms of processing model?

In Structured Streaming, what mode outputs only new rows appended to the result table?

Which of the following best describes how data streams are modeled in Structured Streaming?

What advantage does Structured Streaming have over traditional Spark Streaming regarding API usage?

What type of data source is described as unbounded in the context of Structured Streaming?

Which feature of Structured Streaming contributes to its optimized performance?

Which mode in Structured Streaming outputs the entire result table upon updates?

How does Structured Streaming treat data compared to Spark Streaming?

What is a limitation of Spark Streaming compared to Structured Streaming?

What does Structured Streaming use to ensure high-level data transformations?

Structured Streaming (Spark Streams)

Structured Streaming is a Spark SQL-based advanced streaming engine
It processes real-time data streams using high-level abstractions like DataFrames and Datasets.
Unlike Spark Streaming, which uses micro-batches, Structured Streaming employs a continuous data model.

Key Concepts

Unbounded Data Streams: Data streams are continuously flowing into the system from various sources (e.g., web server logs, financial transactions, IoT sensor data)
Example Unbounded Data Sources: Logs from web servers, financial transactions, sensor data from IoT devices
Streaming as Append-Only Table: Streams are managed as continuously expanding tables, with new rows appended automatically as data arrives

Modes of Operations

Complete Mode: Produces the entire updated result table.
Update Mode: Outputs only the updated rows within the results table.
Append Mode: Only returns the newly added rows to the results table

Advantages

Unified API: Uses the same DataFrame/Dataset APIs for both batch and streaming processing. Allows for queries written for batch processes to be adapted for streaming input.
Declarative Query Language: Writes high-level SQL-like queries for stream transformations
Optimized Performance: Makes use of the Catalyst Optimizer for improved efficiency, using fewer resources compared to older systems like Spark Streaming (DStream)

Differences between Structured Streaming and Spark Streaming

Feature	Structured Streaming	Spark Streaming (DStream)
API	DataFrame/Dataset	DStream API (RDD-based)
Processing Model	Continuous (row-by-row)	Micro-batches
Abstraction	Infinite Table	Batch RDDs
Performance	More optimized	Less optimized

Explore the advanced concepts of Structured Streaming in Spark. This quiz covers essential features such as unbounded data streams, modes of operation, and how data is managed as continuously expanding tables. Test your understanding of real-time data processing with Spark SQL.