What Is Stream Processing? A Beginner's Guide
This article provides a comprehensive introductory overview of stream processing, covering what stream processing is, the architecture and basic principles of stream processing, its differences from batch processing, and various typical use cases, allowing beginners to quickly get started with stream processing.
Data science is one of the fastest-growing industries, and the number of data scientists in the US is expected to grow by 35% between 2022 and 2032. This is significantly faster growth than most other industries, and it's due to the importance of data in today's world.
All kinds of companies make use of big data to gain better insights and make business decisions. Combining data science with stream processing can provide organizations with valuable information. This allows them to improve services and operations.
Keep reading to get a better understanding of stream processing and what it's used for.
What Is Stream Processing?
Stream processing involves acting on a particular data set at the time it's created. In the past, data practitioners would use the term "real-time processing" to describe data that was processed as often as required for a particular application. Modern technologies allow for stream processing, which is more specific and typically more useful.
The incoming data is known as the data stream, and with stream processing, various tasks are typically performed on it. This may happen in sequence, parallel, or both.
The workflow is known as the stream processing pipeline. This covers everything, including the data stream generation, data processing, and data delivery to its final location.
Some of the tasks that stream processing comprises of include:
- Aggregations (including various calculations such as standard deviation, mean, and sum)
- Transformations (e.g., converting numbers into currency format)
- Analytics (e.g., predicting future events through pattern observation)
- Ingestion (e.g., data entry)
- Enrichment (e.g., combining data points and data sources for further information)
With Stream processing, an application can respond to new data events immediately.
Stream Processing vs. Batch Processing
Before data streaming, data practitioners would process data in batches. These would be based on a predefined threshold or schedule. With time, the pace and volume of data have increased, and batch processing is often insufficient.
Stream processing is now the go-to solution for modern applications. Technology that can respond to data in real time is far more efficient and now has multiple use cases.
Apps can respond to new data events instantaneously with stream processing. Batch processing groups data and collects it at a predetermined interval. Stream processing collects and processes data as soon as it's generated, so there's no wait time.
How Does Stream Processing Work?
Most of the time, stream processing is the chosen process for data that's generated as a series of events. This typically comes from things like payment processing systems, IoT (Internet of Things) sensors, and server and application logs.
A source (or publisher) generates events that are delivered to a streaming database. At this point, the data may be augmented, tested against fraud detection algorithms, or transformed in some other way. The application then sends the results to a sink (or subscriber).
Stream Processing Architecture
Stream processing relies on a streamlined architecture to ensure the whole process is fast and seamless. This helps solve multiple challenges that batch processing faces.
The steps are as follows:
- An event happens
- Insight is derived
- Action is taken
Stream processing ensures there's no lag time between these steps. It can also handle incredibly large data volumes, so high-demand systems aren't an issue.
Another advantage over batch processing is that stream processing can model most data in a far more usable way. This makes it easier to understand and act on.
Example Use Cases
Stream processing is most suitable for use when generated data requires immediate action. There are various use cases for this practice.
Real-Time Fraud and Anomaly Detection
Fraud is a major issue in today's world. With the increased use of electronic and online payment systems, fraudsters are capable of stealing huge amounts of funds. Stream processing has proven to be an effective method of combatting this.
Before solutions like stream processing, credit card providers would use batch processing for fraud detection processes. This was seen as inefficient and could often leave people waiting.
Credit card payment delays are inconvenient for both customers and vendors. Being able to handle all credit card processing as fast as possible is ideal.
Stream processing allows credit card providers to process data as soon as you swipe your card. The system will run the data through algorithms to recognize any signs of fraudulent behavior. It can then automatically block fraudulent charges and initiate alerts for any payments that merit investigation without making users wait.
Internet of Things Analytics
IoT devices are becoming increasingly popular in homes as well as in commercial and industrial applications. Companies in a range of industries use such devices to improve systems and business operations.
One example of this is anomaly detection in manufacturing. This can help indicate problems that are negatively affecting operations and productivity.
Real-time stream processing can show a manufacturer if there are a high number of anomalies on a production line quickly. Batch processing may only highlight this at the end of the day, resulting in a significant drop in productivity. The faster a manufacturer can identify such an issue, the quicker they can deal with it, reducing waste and maintaining efficiency.
Real-Time Personalization, Marketing, and Advertising
Marketing has come a long way in recent years, and the effectiveness of a marketing campaign can have a huge impact on the success of a business. Real-time stream processing makes it easier for brands to produce and deliver personalized marketing content. Providing custom, contextual experiences can help boost engagement from users significantly.
An example of this would be to analyze data in a customer's shopping cart. If they add an item but don't purchase it, a brand could then create marketing content offering a discount on this item, helping to drive more sales.
Using Stream Processing
Stream processing offers several advantages over other methods that your organization could benefit from. Being able to process data instantaneously will help you streamline operations and make better-informed business decisions.
RisingWave is an efficient stream processing solution that offers a fully managed service. You can try it now to get an idea of how much it can help your organization.
This article provides a comprehensive foundational introduction to stream processing, including what stream processing is, the architecture and basic principles of stream processing, its differences from batch processing, and more. Additionally, it briefly introduces the role of stream processing in scenarios such as real-time fraud detection, Internet of Things analytics, and personalized marketing.