Structured Streaming vs. DLT Live Table – Declarative Pipeline
Conceptually, DLT and Structured Streaming differ in who is in control, not in formats or parsing.
The one core idea: Structured Streaming is imperative — you write how to run the stream. DLT is declarative — you write what the table should contain, and the framework runs it.
From that single distinction, everything conceptual follows:
- Execution control — In Streaming, you start the query, set the trigger, and call awaitTermination(). In DLT, the framework plans and runs the queries; you never start or stop them.
- State management (checkpoint & schema location) — In Streaming, you must specify and manage checkpointLocation and schemaLocation. In DLT, these are managed internally. (Conceptual: manual vs. managed state.)
- Orchestration & dependencies — In Streaming, you wire tables together manually. In DLT, you reference one table from another (dp.read/readStream) and DLT builds the dependency graph and runs them in order.
- Data quality — A first-class concept in DLT (expect, expect_or_drop). Streaming has no built-in notion; you filter manually.
- Observability — DLT emits a structured event log as part of the framework. Streaming exposes runtime hooks (StreamingQueryListener) you attach yourself.
Auto Loader / design choices that work the same way in both. Auto Loader (cloudFiles) is a source and is independent of whether DLT or Streaming is driving.
One-line summary: Streaming = you orchestrate the pipeline; DLT = you declare the result and the framework orchestrates. That’s the only conceptual axis — format and parsing are orthogonal.
Which to use:
- Want a managed, declarative pipeline with built-in quality + event log → DLT.
- Want manual control, custom triggers, listener-based monitoring, or a simple scheduled batch-incremental job → this writeStream version.