How Does Stream Processing Work?

What is Stream Processing?

Stream processing is a technique of data management including a continuous data stream and analyzing, transforming, filtering, or enhancing it in real-time. Once processed, the data is sent to an application, data storage, or another stream processing engine. It is also known by several names, including real-time analytics, streaming analytics, Complex Event Processing, real-time streaming analytics, and event processing. Although various terminologies have previously differed, tools (frameworks) have converged under the term stream processing....

How Does Stream Processing Work?

Stream processing is frequently used with data created as a sequence of events, such as data from IoT sensors, payment processing systems, and server and application logs. Common paradigms include publisher/subscriber (also known as pub/sub) and source/sink. A publisher or source generates data and events, which are then provided to a stream processing application. The data can be enhanced, evaluated against fraud detection algorithms, or modified before being sent to a subscriber or sink. Stream processing is often used interchangeably with real-time analytics, which is a relative word. Real-time could mean five minutes for a weather analytics tool, millionths of a second for an algorithmic trading program, or a billionth of a second for a physics researcher....

How is Stream Processing Used?

Modern stream processing tools are an extension of several publish-subscribe frameworks that provide data processing while it is in transit. Stream processing can save data transmission and storage costs by distributing processing over edge computing infrastructure. Streaming data architectures can also make it easier for you to combine data from many business applications or operational systems. For example, telecom service providers use stream processing technologies to collect data from many operations support systems....

When to Use Steam Processing?

Stream processing is perfect for obtaining real-time analytics results. Streaming data processing systems in big data are effective solutions for scenarios that require: minimal latency, built-in features for dealing with imperfect data, SQL queries on data streams to build extensive operators, and guaranteed ability to generate predictable and consistent result. Stream processing is particularly effective for algorithmic trading and stock market surveillance, computer system and network monitoring and wildlife tracking, geographic data processing, predictive maintenance, manufacturing line monitoring, and smart device applications....

What are the Stream Processing frameworks?

Spark, Flink, and Kafka Streams are the most popular open-source stream processing frameworks. Furthermore, all of the main cloud services include native services that make stream processing development easier on their respective platforms, such as Amazon Kinesis, Azure Stream Analytics, and Google Cloud Dataflow. Apache Storm provides real-time computation features such as online machine learning, reinforcement learning, and continuous computation. Delta Lake has a single architecture to handle both stream and batch processing....

Stream Processing Architectures

Kappa Architecture: Kappa Architecture simplifies data processing by merging batch and real-time analytics into one. Data flows into a central data queue, such as Apache Kafka, and is translated into a format that can be immediately fed into an analytics database. Lambda Architecture: Lambda Architecture is a data processing technology, that combines real-time stream processing for insights and batch processing. In addition to the batch and speed layers, Lambda architecture adds a data-serving layer for responding to user queries. The Lambda architecture’s batch layer handles historical data using fault-tolerant, distributed storage, assuring a low error rate even if the system crashes....

History of Stream Processing

Computer scientists have explored many frameworks for analyzing and processing data since the evolution of computers. In the beginning, this was referred to as sensor fusion. In the early 1990s, Stanford University professor David Luckham invented the term “complex event processing” (CEP). This contributed to the development of service-oriented architectures (SOAs) and enterprise service buses (ESBs). The rise of cloud services and open-source software resulted in more cost-effective techniques for managing event data streams, such as publish-subscribe services based on Kafka....

Differences between Stream and Batch processing

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Stream Processing in Action

Internet of Things (IoT) edge analytics: Companies in manufacturing, oil and gas, transportation, and architecture utilize stream processing to manage data from billions of things. Real-time personalization, marketing, and advertising: Companies can deliver personalized, contextual consumer experiences through real-time processing streams....

Conclusion

Stream processing is computer programming in which data is computed immediately as it is generated or received. A stream processing framework simplifies parallel hardware and software by limiting the performance of parallel computing....

Frequently Asked Questions on Stream Processing – FAQs

What is the purpose of stream processing?...