Batch vs. streaming data processing

Choosing the right data processing method can make or break your organization’s ability to power accurate, informed data-driven decision-making. On the one hand, there’s batch processing, which processes large sets of data at regular intervals. On the other, there’s streaming processing, which processes data continuously as it comes in. 

Both options have pros and cons and may be suitable for different use cases in your organization. Read on to learn more about the differences between batch and streaming data processing and how to choose the right one for your projects.

What's batch processing?

Batch processing involves collecting a substantial amount of data over a period of time, storing it temporarily, and then processing it all at once. Processing may occur at pre-scheduled intervals. For example, an accounting system might be set up so that every night, it automatically processes all invoices received over the course of the day.

Pros and cons of batch processing

First, the pros: Batch processing is the default method computers have been built to run on since the days of punch cards. That means it’s often easier to run directly on legacy IT infrastructure without significant alterations or updates. And because processing can be scheduled for off-peak times, such as the middle of the night, batch processing can also be highly resource-efficient.

The biggest downside of batch processing is a longer wait for insights. Once data is collected, it might be minutes, hours, or even days before a batch completes. That’s why batch processing is best suited to non-time-sensitive use cases, such as backups or end-of-day reporting. 

In addition, batch processing can be difficult to scale. As data volumes grow, processing times may start to exceed their scheduled intervals. For example, a processing job that was supposed to be completed overnight might extend into the next morning, delaying the delivery of important insights.

What's streaming processing?

Stream data processing involves processing data continuously as it is generated, rather than waiting for large datasets to accumulate. This method is ideal for use cases where high volumes of data are generated rapidly with no clear beginning or end. For example, streaming processing could be used to analyze temperature data from a network of IoT sensors or to continuously check server logs for suspicious activity that could indicate a cyberattack in progress.

Pros and cons of streaming processing

Faster time to insight is a major selling point for streaming processing. Because data is processed when it’s received, this method can yield results in real time or near real time. That’s especially important for use cases where quick decision-making and responsiveness is vital. For example, a stock trading algorithm needs to react to market fluctuations immediately or face potential losses on its investments.

On the flipside, some legacy IT infrastructure may struggle to deliver the low latency and high throughput required for real-time data processing. This method also requires specialized software architecture to collect streaming data, organize it into topics, and route it to analytics engines. All of this means some upfront investment in infrastructure updates is usually required for streaming processing to work.

What are the differences between batch processing and streaming processing?

Understanding the differences between batch processing and streaming processing is essential for selecting the right approach based on your organization's specific needs. Here’s a comparison of these two methods based on several key factors:

What’s the difference between batch ETL and streaming ETL?

The difference between batch ETL and streaming ETL is essentially the same as the difference between batch and streaming processing:

• Batch ETL collects data over a given period, performs transformations on the entire dataset, and loads it into a target system, such as a data warehouse, all at once. 
• Streaming ETL continuously ingests data from various sources, applies transformations like cleaning and aggregation on the fly, and loads the processed data into target systems immediately.

Batch vs. streaming processing: what to consider

When it comes to choosing between batch and streaming processing, there isn’t a clear winner overall. Instead, you’ll need to weigh which approach works best for each use case at your organization. Below, we explore the primary considerations: time to insight, cost, and complexity.

Time to insight

Time to insight is perhaps the most critical differentiator between batch and streaming processing. Streaming processing is designed for scenarios where immediate insights are crucial, such as cybersecurity threat detection or real-time ad optimization. By contrast, batch processing is suited for less time-sensitive applications, such as conducting periodic reporting or historical data analysis.

Cost

Batch data processing can be highly cost-efficient—if you’re able to leverage existing IT infrastructure and schedule processing jobs for off-peak times. At higher data volumes, costs can creep up as storage and compute needs increase and processing times extend.

Streaming processing generally involves higher upfront costs due to the need for specialized IT infrastructure. However, real-time data streaming architecture is built to scale horizontally, so ingesting additional streams takes relatively few resources as your data volumes grow. In addition, streaming processing can scale up and down automatically with data traffic, ensuring that you’re not paying for compute or storage capacity that goes unused. 

Complexity

Batch processing tends to be less complex to manage because it handles data in bulk at scheduled intervals, allowing for a more straightforward architecture and easier debugging and maintenance. It is well-suited for environments where data consistency and completeness are prioritized over immediacy.

In contrast, streaming processing introduces greater complexity. Dedicated infrastructure is necessary to manage continuous data flow and fluctuating data velocities. Additionally, managing state, dealing with out-of-order events, and ensuring fault tolerance in real-time applications require more advanced skills and expertise. 

Supercharge your streaming data with Redpanda

Streaming data processing is a powerful tool for generating instant insights from continuous data flows. If you’re ready to get started exploring streaming data use cases at your organization, Redpanda is a handy platform to have in your corner.

With streamlined workflows and an industry-leading UI, Redpanda is ridiculously simple to set up and manage. With full compatibility with the Apache Kafka® ecosystem and 260+ pre-built connectors, you can easily integrate data streams from diverse sources while keeping costs low. Want to see for yourself? Try Redpanda Cloud for free.