Streaming data refers to a continuous flow of data that is constantly being generated and grows over time. Unlike traditional batch data, which is collected and processed in chunks, streaming data is processed in real-time as it arrives.
New Orders: For a retail company, each new customer order represents a data stream.
Real-Time Stock Prices: Financial markets generate continuous streams of stock price updates.
Social Media Feeds: Platforms like Twitter generate a constant stream of posts.
Sensor Readings: Machines and IoT devices continuously send sensor data.
Streaming data can be in various file formats such as Parquet, Delta, CSV, or JSON, and can come from multiple sources like Event Hubs, IoT Hub, Kafka, and others.
In the upcoming lectures, we will focus on Spark Structured Streaming, a powerful tool for processing streaming data in near real-time.
Real-Time Processing: Spark Structured Streaming processes data as it arrives, enabling near real-time analytics.
Fault Tolerance: The system is designed to recover from failures without losing or corrupting data. This is crucial for maintaining the integrity of the data being processed.
– **Exactly-Once Processing Guarantee:** Spark ensures that each record in the data stream is processed only once, preventing duplicate processing. This is especially important for applications where data accuracy is critical, such as financial transactions or sensor data analysis.
#### 3. **How Spark Structured Streaming Works**
Spark Structured Streaming leverages the same Spark SQL engine and programming model used for batch processing but adapts it for streaming data. Here’s how it operates:
Micro-Batches: The streaming data is divided into small chunks called micro-batches. Each micro-batch contains a small portion of the streaming data.
Processing with DataFrames and Datasets: These micro-batches are processed using Spark’s DataFrame and Dataset APIs, allowing for the manipulation and analysis of structured data with a high-level, declarative programming model.
This approach allows you to build scalable, real-time applications that can handle large volumes of streaming data while maintaining high performance and reliability.
#### 4. **Summary**
Spark Structured Streaming is a robust and widely-used tool for building real-time streaming applications that are scalable and fault-tolerant. By using this tool, you can process streaming data in real-time with strong guarantees of data accuracy and reliability.
In the following lectures, we’ll dive deeper into Spark Structured Streaming by simulating a streaming dataset. This hands-on experience will help you understand how to use Spark Structured Streaming to build powerful streaming data applications.