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Real-Time vs. Batch Data Integration: Which is Better for Which Use Cases?

When it comes to big data, there are two main ways to process information. The first—and more traditional—approach is batch-based data integration. The second is real-time integration.

Understanding which data integration strategy is the right fit for which situation is an important step for ensuring that you are processing big data in the fastest and most cost-effective way. Toward that end, let’s take a look at the differences between batch-based and real-time data integration, and explain when you might choose to use one or the other.

What Is Batch-Based Data Processing?

As the term implies, batch-based data processing involves collecting a series of data, storing it until a given quantity of data has been collected, then processing all of that data as a group—in other words, as a batch. It’s different from processing each piece of data as it is collected.

Traditionally, batch processing has been the go-to approach for data integration. With older technologies, it was typically more efficient to process information in batches rather than working with each small piece of data individually. Doing so reduces the number of discrete I/O events that need to take place. It can also help to save network bandwidth by compressing data within batches.

The New Rules for Your Data Landscape

Batch-based data integration is ideal for situations where you can afford to wait a bit to receive data analytics results. For example, you might use batch-based processing to maintain an index of all of the documents that your company stores on its infrastructure. In that case, it’s probably OK if the index is not updated every single time a document is added, removed or modified. It would be acceptable to rebuild the index every hour, or maybe even just once a day, by collecting data about document changes and processing it in batches. Similarly, batch processing works well for data that is being archived and will be accessed periodically for historical purposes, rather than used to make instantaneous decisions.

Hadoop is probably the best-known big data framework today that was designed first and foremost for batch processing—although there are ways to do other kinds of processing in Hadoop (for more on that, keep reading).

What Is Real-Time Data Processing

You can probably guess what real-time data processing means from its name: It refers to processing data in, well, real time. In other words, each piece of data is processed as soon as it is collected, with results available virtually instantaneously.

It is worth keeping in mind that defining real time can be harder than it might seem. In practice, real-time data integration is not usually truly instantaneous because migrating, transforming and processing data takes time; delays of fractions of a second are typical. But the idea behind real-time processing is that you process data as quickly as you possibly can after it is collected.

In a world where getting results as quickly as possible is increasingly important for business operations, real-time data processing is a critical resource. If you want to, say, detect fraudulent credit card payments, being able to collect data associated with a payment and determine whether it matches fraud signatures in real time—or at least within a few seconds—is important. If it takes you minutes or hours to process the data, the thief will likely have made off with the goods by the time you identify the fraud, and it will be much harder to resolve the problem than it would be if you could simply cancel the transaction before it was completed.

Streaming analytics frameworks like Spark make it easy to do data processing in real time, provided you also have the tools (like those offered by Syncsort) to offload and transform your data from its source into a modern analytics environment.

Batch vs. Real-Time: What’s Right for You?

Because real-time processing leads to faster results, it’s generally preferable to process data in real time whenever possible. Even if real-time integration is not strictly necessary for a given workload, having the ability to process in real time can never hurt—and it may prove handy if your needs change in the future and real-time insight becomes a must.

The major reason you may choose not to do real-time processing is that it can be more costly in terms of resource expenditure, for the reasons explained above. Again, however, modern tools make it easy to do real-time data integration without overloading your infrastructure.

The bottom line: Whenever you can integrate data in real time, do it. And if you’re still heavily reliant on batch processing, exploring options for real-time integration may be wise, because you never know what your future needs will be.

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