Stream processing 101

motivation

In the modern era of big data and instant analytics, stream processing has emerged as a crucial technology for handling and analyzing data in real-time. Let’s get into the core concepts, advantages, and real-world applications of stream processing.

so what’s stream processing?

Stream processing is a method of continuously ingesting, processing, and analyzing data in real-time as it flows from various sources. Unlike traditional batch processing, where data is collected and processed in large, discrete chunks, stream processing handles data incrementally and instantaneously, making it ideal for applications requiring quick insights and actions.

key concepts of stream processing

stream/unbounded data: A stream can be a set of continious events that come through a logical channel. These are an ever growing set of data which can grow infinitely. Website analytics information such as clicks, Continuous IOT sensor data like temperature, pressure and humidity, GPS location data and even Credit card purchase data are some of the example of unbounded data. Streaming is a type of data processing tailored for handling infinite or unbounded data.

real-time processing: It processes data on-the-fly as soon as it arrives, without waiting for the data to accumulate.

scalability: Designed to handle high volumes of data with minimal latency, making it suitable for applications with large-scale data inputs.

fault tolerance: Stream processing systems are built to be resilient, ensuring continuous operation even in the event of system failures.

how stream processing works?

In a typical stream processing setup, producers generate data streams, which are then sent to a broker. The broker acts as an intermediary, ensuring efficient and reliable data transfer to the consumers. These consumers are tasked with processing the data in real-time, performing operations like filtering, aggregation, and analysis.

stream processing in action: web analytics system

To understand the importance of Stream processing, let’s take an example of a scenario where you are requried to build a web alalytics system that would track users clicks in a web app.

traditional approach

Whenever a user clicks a webpage, the frontend of the application keeps track of the clicked data. You might temporarily store the click data in a queue or buffer before sending it to the server. This could be a browser in-memory cache or any local storage mechanism. Now, periodically, the frontend/ client application might send the queued data to the server through REST-APIs for processing. These data might be collected for a period of time. Upon receiving the batched click data, the server processes it. This processing might involve data validation, aggregation, filtering, and eventually storing the data in a database. After the click data is stored, it’s available for analytics and reporting purposes. Now, if you want to generate reports, perform data analysis, or extract insights, you would run batch jobs on the collected data.

using this approach brings several challenges for our system

1. delayed insights: Batch processing typically involves waiting for a fixed period before analyzing the collected data. This will result in an outdated insights.
2. scalability issues: As the website receives sudden spikes, the system will struggle to handle the increased workload efficiently. Scaling these systems to accomodate the traffic can become complex and costly.
3. inefficient resource utilization: Batch processing often requires processing the entire dataset, including redundant or irrelevant data. This inefficiency can lead to increased processing time, storage costs, and resource consumption.

Seems inefficient eh? This is where Streaming comes into action. Streaming/Data processing engine allows us to take continuous sets of infinite data and process it in our system in real-time.

So in our hypothetical application, if we were to use a Stream processing system, the system might work with this approach: Similar to the previous approach, the frontend collects click data when a user interacts with the website. Instead of batching the data and periodically sending it to the server, the frontend immediately sends each click event to a stream processing system. This could be done using a message broker, or a dedicated event streaming platform. The server, or a dedicated stream processing service, continuously processes incoming click events in real-time. This processing can include data validation, aggregation, and storage in a database as individual events arrive.

advantages of using stream processing

1. instant analytics: Stream processing allows for the immediate analysis and utilization of data.
2. improved user experience: Real-time processing enables dynamic responses, like instant website personalization based on user actions.
3. resource efficiency: Eliminates the need for large-scale data storage and processing, reducing costs and time.
4. flexibility: Easily adapts to changing data formats and processing needs.

stream processing vs. batch processing

Stream processing excels in scenarios where speed and immediacy are paramount, such as real-time monitoring or instant decision-making. In contrast, batch processing is more suitable for complex analysis and reporting tasks where immediacy is not critical.

tldr;

Stream processing is a game-changer in the world of data analysis, offering the power to process and analyze data as it happens. This real-time capability opens up new avenues for innovation across various industries, from finance to e-commerce. As the volume and speed of data continue to grow, mastering stream processing will be crucial for any data-driven organization.