Day 24: Spark Structured Streaming
Source: Dev.to
Introduction
Welcome to Day 24 of the Spark Mastery Series.
Today we enter the world of real‑time data pipelines using Spark Structured Streaming.
If you already know Spark batch, good news:
You already know 70 % of streaming.
Let’s understand why.
Structured Streaming = Continuous Batch
Spark does not process events one by one. It processes small batches repeatedly, which provides:
- Fault tolerance
- Exactly‑once guarantees
- High throughput
Why Structured Streaming Is Powerful
Unlike older Spark Streaming (DStreams), Structured Streaming:
- Uses DataFrames
- Leverages the Catalyst optimizer
- Supports SQL
It also integrates with Delta Lake, making it production‑ready.
Sources & Sinks
Typical real‑world flow:
Kafka → Spark → Delta → BI / MLFile streams are useful for:
- IoT batch drops
- Landing zones
- Testing
Output Modes Explained Simply
- Append – only new rows
- Update – changed rows
- Complete – full table every time
Most production pipelines use append or update.
Checkpointing = Safety Net
Checkpointing stores progress so Spark can:
- Resume after failure
- Avoid duplicates
- Maintain state
No checkpoint → broken pipeline.
First Pipeline Mindset
Treat streaming as an infinite DataFrame processed every few seconds. The same rules from batch apply:
- Filter early
- Avoid shuffle
- Avoid UDFs
- Monitor performance
Summary
We covered:
- What Structured Streaming is
- Batch vs. streaming model
- Sources & sinks
- Output modes
- Triggers
- Checkpointing
- Building a first streaming pipeline
Follow for more content, and let me know if anything was missed. Thank you.