Real-time data processing versus micro-batch processing

Do you know the strengths and weaknesses of real-time processing and micro-batch processing?

And, crucially, do you know which you should be using in your systems?

With so much data moving through your data pipelines, your teams have a series of options to choose from when managing the frequency of their data processing.

It's also key for organizations to adopt the right strategy. Otherwise, you can end up building a data infrastructure that either doesn’t do the job it needs to, or that's more complex and costly than it needs to be.

So, in this article, we’re going to explore (and clarify) what separates these two kinds of processing so you can choose which is best for your projects.

Defining ‘real-time data processing’ and ‘micro-batch processing’

Before we start comparing the two processes, let’s first define our terms.

Here's a definition of real-time data processing:

Real-time data processing applies to data processing that is near-instantaneous. Typically, this is a sub-second timeframe. This means the experience for the end-user is ‘instant’ and is best used when data input requests need to be handled rapidly.

Real-time data processing is appropriate when your organization needs real-time insight, decision making, or input into systems. When the latency needs to be below a second, and data is coming continuously, real-time data processing is the right kind of data processing to choose.

The following use-cases make good use of real-time data processing:

Day trading where high-frequency trades and quick investments are made.
Fraud detection where anomalous activity can be recognized and actioned immediately.
Monitoring IT systems where security issues can be quickly picked up.

Micro-batch data processing, on the other hand, executes data processes slower and is used for situations where latency of over a second (and up to a few minutes) is acceptable.

Here’s a definition of micro-batch processing:

Micro-batch data processing refers to data processing performed in small ‘batches’. In this way, data is allowed to ‘pile up’ before it’s moved on to the next stage. This is done in smaller batches than traditional ‘batch processing''. Micro-batch processing delivers data more slowly than real-time data processing but faster than typical batch processing.

Some of the use cases for micro-batch data processing include:

Processing orders from customers on ecommerce websites.
Billing customers and clients and sending invoices on billing systems.
Updating operations systems such as an organization's HR software.

Now, while both real-time and micro-batch processing have their place, many organizations fall into thinking they need one when they need the other.

The hard truth (do you really need real-time data processing?)

Although many organizations feel like they need real-time data processing, the reality is that this is often overkill for what they’re trying to achieve.

It’s perhaps understandable – after all, why wouldn’t you want data all the time at real-time speeds if that’s possible? And if you could, why wouldn’t you build systems that update all the time?

Well, for most organizations and most purposes, micro-batching is sufficient, and setting up real-time data processing is a needless (and costly) initiative that won’t yield any further business benefits. Real-time processing can also amplify data quality challenges as the data moves so rapidly.

Yes, if you’re working as a day-trader, and making high-frequency investments throughout the day, it matters whether you get constant data or data that’s only updated every few minutes.

But most common examples of data processing needs don’t need this. For example, if your organization uses a CRM (customer relationship management) software, there’s no added value if it updates immediately instead of every two minutes.

So, when making the decision between real-time data processing and micro-batch processing, ask yourself whether any value is really added by executing on data constantly. The majority of the time, there won’t be, and micro-batch processing will suffice.

Once you’ve made the decision on which processing approach you’ll use, it’s then time to find and deploy the right tools to build your brilliant data pipelines.

Data processing with a platform built for automation

Both real-time data processing and micro-batch processing have their own advantages, and knowing the difference is the first step for analysts and IT teams looking to optimize their data processing.

As we’ve addressed, organizations and scenarios that need real-time insights tend to use real-time processing, but most pipelines are best-suited for micro-batch processing as it still gets the job done whilst being easier and more cost-effective to implement.

And, with the majority of real-world scenarios best suited for micro-batch processing, a tool like the CloverDX platform is worth considering. It empowers your team to build data pipelines that boost productivity and make it easy for technical and non-technical teams to collaborate on building solutions for their data challenges.

For those who are looking for data-streaming and in need of real-time data processing, CloverDX also integrates with Kafka – here's our webinar on Apache Kafka and Microbatching in CloverDX that explains how. By combining the streaming nature of Kafka with the capabilities of CloverDX, you can build pipelines with real-time data processing that are both comprehensive and auditable.

If you’d like to see how CloverDX can help you build data pipelines more quickly, you can get a 45 day free trial and try it for yourself.