Kafka → Spark → Delta → BI / ML

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. This gives:

• Fault tolerance
• Exactly-once guarantees
• High throughput

🌟 Why Structured Streaming Is Powerful

Unlike older Spark Streaming (DStreams):

• Uses DataFrames
• Uses Catalyst optimizer
• Supports SQL
• Integrates with Delta Lake This makes it production-ready.

🌟 Sources & Sinks

Typical real-world flow:

Kafka → Spark → Delta → BI / ML

File 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

Same rules apply:

• Filter early
• Avoid shuffle
• Avoid UDFs
• Monitor performance

🚀 Summary

We learned:

• What Structured Streaming is
• Batch vs streaming model
• Sources & sinks
• Output modes
• Triggers
• Checkpointing
• First streaming pipeline

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