In our previous blogs:
We got to know 2 major points about Structured Streaming –
- It is a fast, scalable, fault-tolerant, end-to-end, exactly-once stream processing API that helps users in building streaming applications.
- It treats the live data stream as a table that is being continuously appended/updated which allows us to express our streaming computation as a standard batch-like query as on a static table, whereas Spark runs it as an incremental query on the unbounded input table.
In this blog post, we will talk about the philosophy or the programming model of the Structured Streaming. So, let’s get started with the example that we saw in the previous blog post.
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