Comparing batch processing with stream processing

In 2024, global data consumption might reach 149 zettabytes, according to recent estimates. With these figures growing every year, it's no wonder that businesses are seeking the most efficient ways to process their data.

In this article, we will compare two approaches to data processing, namely batch and stream processing. Each of them has different characteristics and serves different purposes, but we will attempt to break them down to help you decide which one would better suit your project.

What is batch processing?

Batch processing is an approach in which a data pipeline executes tasks upon request or at scheduled intervals. It is suitable for scenarios where data can be delivered within a flexible timeframe, typically with a delay of several hours.

What is stream processing?

Stream processing handles incoming data in real time, facilitating continuous input and output of data. This method is perfect for projects demanding immediate insights, such as monitoring sensor information or tracking financial transactions.

The difference between these approaches lies in the timing of data processing, the volume of data handled, and the specific use case requirements. In some scenarios, such as the insurance industry, a lambda architecture that merges both stream and batch processing may be necessary to meet diverse data handling needs.

In the following chapters, we will discuss these differences in greater detail. However, let's first take a closer look at each processing approach to better understand how they can benefit different projects.

Batch processing and its benefits

As we already said, batch processing is about executing multiple tasks at the same time and distributing them to various computing devices (Hadoop, Spark). These tasks could be the collection, storage, and simultaneous handling of data.

Characteristics of batch processing

Batch processing handles data in grouped sets, called batches. It does that at specific intervals and is usually scheduled during off-peak hours, as it allows to save the strain on the system.

Working with batches involves handling large sets of data in group intervals, which can be more resource-efficient and allow for scheduled use of computing resources. This scheduled approach can give developers the flexibility to use and experiment with different hardware configurations without the need for real-time data delivery.

Batch processing is scalable because it can be extended across multiple systems to manage increasing volumes of data.

The downside of batch processing is that insights and outcomes of data analysis are available only after the entire batch has been processed.

Common use cases of batch processing

Batch processing is recommended for projects that handle large volumes of data and do not require immediate results following a request. For example, transactions in the finance industry are collected throughout the day and processed once markets close. Only then the data analysts can deliver important insights for the day.

Similarly, data warehouses benefit from regular updates. Batch processing helps keep datasets current without the need for developers to constantly monitor and add new data.

The benefits of batch processing

The very nature of batch processing is a source of benefits, and limitations, depending on a case.

Better resource management: Just like in the financial example, batch processing can be scheduled to run at a specific time when computing resources are unused and available.
Capacity to better manage large datasets: By dividing large datasets into smaller batches, this approach enables more efficient data management. It gives the developers time to identify issues, perform thorough analyses, and implement fixes without immediate pressure.
Easy implementation: Batch processing is easier to implement and maintain than stream processing.

The limitations of batch processing

Batch processing comes with certain limitations, the primary ones being the incapability to handle data in real-time and delayed detection of errors.

High-latency: This limitation stems from the nature of batch processing, which requires collecting enough data to form a batch before it can be processed.

As a result, there is a delay from the time data is collected to when it is ready for analysis, making it impossible to immediately draw conclusions or take action in real-time.

Delayed error detection: Errors often remain undetected until the entire batch is finalized. Potentially, this could delay corrections and affect further analyses.

What is stream processing and what are its benefits and best use cases?

Stream processing handles data continuously as it arrives, allowing for action based on immediate insights. It is best suited for rapidly changing environments where data constantly flows and evolves.

Characteristics of stream processing

Stream processing works by continuously updating data streams. It facilitates real-time analytics and delivers nearly instant feedback or actions. It supports quick decision-making and responsiveness to new information.

Common use cases of stream processing

Stream processing enhances the efficiency of operations it integrates with. It is crucial in applications requiring real-time data handling and in business sectors where the rapid exchange of information is essential.

For example, in the financial sector, stream processing aids in fraud detection. It identifies any suspicious activity by inspecting transactions as they occur. Another one is social media platforms, stream processing provides users with live updates and customized content on the spot.

Telecommunications companies also use this technology to keep a constant check on network traffic. It helps them enhance performance and avoid potential outages.

Stream processing is essential for systems such as smart homes and industrial automation where it ensures prompt responses to sensor data.

The benefits of stream processing

Stream processing presents numerous advantages, due to its ability to deliver low latency for real-time data analysis.

Asynchronous processing: Stream processing enables businesses to draw insights and address errors.
Immediate feedback: It's suited for applications such as fraud detection or live monitoring systems.

The limitations of stream processing

With all the upsides of steam processing, there are certain limitations worth noting.

Risk of data corruption: Without proper oversight, stream processing might encounter issues like data corruption or receiving data out of sequence.
Complex implementation: Implementing this stream processing approach can be more complex compared to batch processing. It requires sophisticated infrastructure and expertise, just to ensure data consistency and handling asynchronous streams

By understanding the benefits and drawbacks of stream processing, organizations can make informed choices tailored to their specific needs. It's crucial to weigh the desire for real-time capabilities against the potential technical hurdles and costs involved in implementation.

Key differences between batch processing and stream processing

The key differences between batch processing and stream processing lie in four aspects: latency, throughput, infrastructure needs, and data consistency.

Aspect	Batch Processing	Stream Processing
Latency	High latency due to processing large volumes of data simultaneously.	Low latency due to managing smaller chunks of data instantly.
Throughput (the amount of data it can process)	Can handle extensive datasets but struggles with real-time demands.	Handles real-time data streams, but requires a robust infrastructure.
Infrastructure needs	Can work with lower-performing hardware as operations are less frequent and tolerate delays.	Demands high-performance resources and redundancy to ensure continuous data analysis without slowdowns.
Data consistency and fault tolerance	Delivers consistent outcomes by verifying entire datasets before proceeding.	Can face more challenges in delivering consistency but is equipped with strategies to recover from faults.

Batch processing vs. stream processing: Which one to choose for your project?

Choosing between batch processing and stream processing largely depends on your specific requirements.

When deciding on a method, consider how quickly you need insights and what your infrastructure can handle. It's important to evaluate the time-sensitivity of the information you require and contemplate the volume and speed of incoming data.

Batch processing is ideal for examining large datasets at specific times, like compiling end-of-day reports or aggregating information. This approach suits projects where the are large amounts of data, but where immediate decisions aren't necessary.

Stream processing provides real-time insights from continuous data flows. It is ideal for projects demanding immediate results, but only if you have the resources to build and maintain a demanding architecture.

Each approach has its strengths and ideal applications, and sometimes, a hybrid model employing both methods is the best solution. In the end the choice between batch and stream processing should be guided by the unique needs of your project.

How to manage data for your project? Comparing batch processing with stream processing.