---
title: "Discussion on \"Spark Structured Streaming: A Principal Engineer's Guide\" | Hashnode"
description: "Discussion on \"Spark Structured Streaming: A Principal Engineer's Guide\". It was 2 AM. The pipeline was green, but the numbers were wrong.\n\nImagine you're building a data platform for a financial services company.\n\nEvery second, thousands of transactions are happening. Customers are making payments, transferring money, placing orders, and checking account balances.\n\nThe business wants to see these transactions almost immediately.\n\nSo, you build a Kafka-to-Spark streaming pipeline.\n\nEverything looks good. Events are flowing, transformations are working, and Delta tables are getting populated.\n\nThen production happens.\n\nA transaction that occurred at 10:00 AM arrives at 10:15 AM. Another transaction arrives twice. A particular customer generates 50,000 events while most customers generate fewer than 10.\n\nYour aggregation starts consuming more memory. The processing time increases. The streaming query falls behind.\n\nAnd the business dashboard is now showing numbers that nobody trusts.\n\nThe pipeline is technically running. But the data platform is failing.\n\nThis is where understanding Spark Structured Streaming becomes important.\n\nStreaming is not just about reading data continuously. It is about handling late events, duplicates, state, failures, recovery, and data correctness without losing control of the system.\n\nLet's understand how Spark handles all of this, starting from the basics and gradually moving towards production architecture."
url: "https://hashnode.com/posts/spark-structured-streaming-principal-engineer-guide/6ae85012a01cc8295170e1ff"
---

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# [Spark Structured Streaming: A Principal Engineer's Guide](https://core-de-platform.hashnode.dev/spark-structured-streaming-principal-engineer-guide?utm_source=hashnode&utm_medium=feed)

It was 2 AM. The pipeline was green, but the numbers were wrong. Imagine you're building a data platform for a financial services company. Every second, thousands of transactions are happening. Customers are making payments, transferring money, placing orders, and checking account balances. The business wants to see these transactions almost immediately. So, you build a Kafka-to-Spark streaming pipeline. Everything looks good. Events are flowing, transformations are working, and Delta tables are getting populated. Then production happens. A transaction that occurred at 10:00 AM arrives at 10:15 AM. Another transaction arrives twice. A particular customer generates 50,000 events while most customers generate fewer than 10. Your aggregation starts consuming more memory. The processing time increases. The streaming query falls behind. And the business dashboard is now showing numbers that nobody trusts. The pipeline is technically running. But the data platform is failing. This is where understanding Spark Structured Streaming becomes important. Streaming is not just about reading data continuously. It is about handling late events, duplicates, state, failures, recovery, and data correctness without losing control of the system. Let's understand how Spark handles all of this, starting from the basics and gradually moving towards production architecture.

Oct 28 · 15 min read · core-de-platform.hashnode.dev  
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## Written by

[srkhan2809](https://hashnode.com/@shahida-khan)

@shahida-khan · Dubai, UAE

  
[View profile](https://hashnode.com/@shahida-khan)

Lead Data Infrastructure Architect | Databricks, PySpark & Cloud DW

I am a Lead Data Infrastructure Architect with 13+ years of experience engineering high-throughput data platforms for fintech and consumer apps. I specialize in the Databricks Lakehouse ecosystem, PySpark, BigQuery, and Snowflake. I write about modern data architecture, cloud cost optimization (FinOps), declarative ELT pipelines, and scaling systems to process 100K+ events per second. When I'm not untangling complex legacy databases, I'm building zero-loss data quality frameworks.

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