Скачать или смотреть What is Spark Structured Streaming – learn basics: Azure Databricks for Data Engineers: Day 10

This video will explain *📹 Spark Structured Streaming – Basics | Hands-On Tutorial*

🔥 Welcome to our deep dive into *Spark Structured Streaming* – an essential tool for processing unbounded streams of data in near real-time! If you're curious about how to work with streaming data using **Databricks Community Edition**, this video has everything you need to get started. Let's explore how to ingest, process, and analyze live data streams using Spark! 🚀

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Git hub repository: https://github.com/sachin365123/DataB...

🧐 *What is Streaming Data?*
Streaming data is a continuous flow of information arriving in real-time from various sources like IoT devices, social media platforms, or e-commerce sites. For example:
🚗 IoT devices tracking vehicles on a road.
🛒 Clickstream data from users on an e-commerce site.

This data is endless, making it a challenge for traditional batch processing systems like Apache Hadoop. That's where *Spark Structured Streaming* shines! 🌟

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💡 *Why Use Spark for Streaming?*
Spark Structured Streaming offers numerous advantages over traditional systems:
1️⃣ **Fast Failure and Straggler Recovery**: Automatically recovers from failures to ensure uninterrupted data processing.
2️⃣ **Dynamic Load Balancing**: Adapts resource allocation to avoid bottlenecks.
3️⃣ **Unified Processing**: Combines batch, streaming, and interactive queries in a single engine.
4️⃣ **Advanced Analytics**: Enables machine learning and SQL queries on streaming data.

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🛠️ *Step-by-Step Hands-On*
📝 **Prerequisites**:
Use *Databricks Community Edition* to avoid high costs.
Upload the dataset files (`Countries1.csv`, `Countries2.csv`, `Countries3.csv`) to `FileStore` in `DBFS` under the `streaming` directory.

🔧 **Steps to Follow**:

1️⃣ *Create a Notebook*
Name it `Day 10 Streaming+basics.ipynb`.

2️⃣ *Upload Your Dataset*
Upload the first file (`Countries1.csv`) to the `streaming` directory.

3️⃣ *Read Streaming Data*
Use the `readStream` function.
Verify streaming jobs in the *Spark UI* (accessible via the Compute tab).

4️⃣ *Displaying Data*
Use `display(df)` instead of `df.show()` for real-time dashboards and statistics. 📊

5️⃣ *Monitor Jobs*
Upload `Countries2.csv` and observe spikes in *Input vs Processing Rate* graphs. Each file triggers a new micro-batch.

6️⃣ *Inspect the Streaming Query*
Check the `Display Query` section under *Structured Streaming* in Spark UI.
Upload `Countries3.csv` and observe the third micro-batch.

7️⃣ *Stopping the Query*
Stop the streaming query by clicking `Cancel` in the Spark UI.

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✨ *Understanding Checkpointing*
Checkpointing provides *fault tolerance* and *resiliency* in Spark Structured Streaming. Here's how:
**Stores Metadata**: Keeps track of the progress of the stream (not the data itself).
**Recovery from Failures**: If a failure occurs, Spark resumes from the last checkpoint, ensuring uninterrupted data processing. 💾

Example Code:
```python
WriteStream = ( df.writeStream
.option('checkpointLocation', f'{source_dir}/AppendCheckpoint')
.outputMode("append")
.queryName('AppendQuery')
.toTable("stream.AppendTable"))
```

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🌐 *Data Sources and Sinks*
**Sources**: File (DBFS), Kafka, Socket, Rate (useful for testing).
**Sinks**: File systems, databases, and live dashboards.

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🎯 *Key Takeaways*
Spark Structured Streaming processes unbounded streams in real-time.
It uses *micro-batches* as the fundamental unit for processing.
Checkpointing ensures fault tolerance and resilience.
*Databricks Community Edition* is a cost-effective platform to experiment with streaming data.

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🛑 Don’t forget to *Like 👍**, **Subscribe 🔔**, and **Comment 💬* on this video if you found it helpful. Let’s explore Spark Structured Streaming together! 🚀

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