It’s pretty rare to find a dataset that’s actually useful in its raw form. And in a world where virtually every business seems to be drowning in a sea of raw data, it’s not surprising that we’re constantly looking for the best way to process it. Today, we’ll tackle a major duality in the world of data processing: batch vs real-time processing. 

While understanding the difference between batch and real-time data processing won’t identify the perfect architecture for your use case, it’s a critical first step. This knowledge will round out your understanding of the options available to you. 

Because as you’ll soon see, every data processing solution you encounter falls into one bucket or the other. And each has implications for performance, price, and business outcomes. 

In this article, we’ll discuss the basics of batch and real-time data processing. We’ll dig into their pros and cons, and when to use each. Along the way, you’ll find a handy table that compares their major traits side by side.

What is Data Processing?

Let’s start by setting a baseline: what exactly is data processing? 

Data processing is the act of converting raw data into useful information. It’s a deceptively simple concept with huge real-world implications.

Raw data is rarely useful to help make decisions or develop business insight. To turn it into information, you need to process it in some way. Your approach to data processing will depend on several factors, which include:

• Raw data state: Where is the data collected from? What format is it in? How much of it is there?
   
• Goals: What information do you need to gain from this data? This represents the end state of data processing.
   
• Business domain: From finance to marketing, different business domains use data differently. This influences how it needs to be processed.

You can break data processing down into these high-level steps:

Step 1: Data collection. Also known as data ingestion or data capture, in this step, the data enters the processing pipeline. Data can be collected from applications, IoT sensors, SaaS APIs, external databases, or other storage systems. 

Step 2: Data cleaning or transformation. Next, the processing pipeline needs to get the data into a usable state. The definition of “usable” will vary, but at minimum, the data will need to be in a consistent, known format that can be described by a schema. 

The pipeline might:

• Validate incoming data against an established schema and reject data that doesn’t conform. 
   
• Apply basic transformations so that data fits a schema. For example, it might add a required field or replace disallowed characters. 
   
• Apply complex transformation to unlock more sophisticated workflows. For example, it could join several datasets together to arrive at an important business metric. 

Step 3: Operationalization

At this step, it’s time to use the data to power business outcomes. For example: 

• Use the data to power a live dashboard or BI platform tracking KPIs, progress toward a goal, or a team’s workload. 
   
• Configure alerts when an action is needed — anything from deploying emergency personnel to replacing a piece of equipment on a factory floor. 
   
• Create tailored customer experiences in e-commerce apps or streaming platforms. 

Step 4: Data storage

It’s a waste to only use data once and let it disappear! In addition to operationalization, you’ll also want to store your data. This allows you to continue to analyze your data for years to come.

With this in mind, it’s time to choose a data processing framework. This is where things get tricky: you have many options. But a great place to start is by figuring out whether you need batch or real-time processing. 

Batch Data Processing

In batch data processing, the data pipeline collects data over an interval of time and processes it all at once. This window of time is called the “batch interval,” and it repeats over and over. 

In other words, the data collection step is not ongoing. The pipeline stays idle for a while as new data builds up in the source. Then, when the batch interval ends, the pipeline process begins. Usually, data is collected by querying the source for changes. 

Once collected, all the new data is processed at once, operationalized at once, and stored at once. 

Advantages of batch data processing

• Batch processing is easier to build and maintain from scratch. It’s more attainable for small teams who prefer control over their entire data pipeline infrastructure (rather than purchasing a solution). 
   
• Batch processing can eliminate complexity. If you don’t need real-time data, the added complexity of real-time processing may not be worthwhile. 
   
• Batch processing can be efficient in some scenarios. For example, if your total data volume is small, or your pipeline shares resources with other technology, batch might be more resource efficient. 

Disadvantages of batch data processing

• Batch processing adds latency. The longer the batch interval, the longer you have to wait to get new data into downstream systems.
   
• Data collection can get expensive. Because the mechanism for batch data collection is querying, each batch run must read the entire source dataset. And repeated, large read operations can get expensive.

Batch processing is the legacy data processing method. It came about in the days before cloud computing and Big Data as we know it today. 

For a company limited to the capacity of its own data center, processing data in batches was the only option that made sense. You had to share compute resources with every other process in the company, so you could be much more efficient by scheduling batch jobs at night or during downtime. And the complex infrastructure required for real-time processing wasn’t available yet. 

But today, all that has changed. This brings us to our second type of data processing. 

Real-time Data Processing

In real-time data processing, also known as stream data processing or just streaming, data is processed as soon as it appears at the source.

There’s no “batch interval” to introduce delay and no scheduling involved. 

Instead, a real-time processing pipeline constantly looks for change events in the source system. When it detects a change, it immediately moves that change through all steps of the pipeline. 

Advantages of real-time data processing

• Real-time processing creates instant insight. Raw data is useless until it’s processed. With real-time processing, your business operations are always informed by fresh, accurate data. 
   
• More efficient in modern use cases. Over time, real-time processing itself has become more efficient, and it scales excellently in the cloud. It also relieves the burden batch processing puts on data source systems; there’s no need to repeatedly query massive datasets just to see what’s new. 

Disadvantages of real-time data processing

• Hard to build. Real-time data infrastructure is extremely hard to build and maintain… even if you’re using an open-source framework as your baseline. Managed data pipeline platforms can help, but that might not be an option for all teams.
   
• Not suited for legacy use cases. If you use legacy data systems, are confined to relatively small on-prem infrastructure, or are generally dealing with a small amount of data, real-time processing could introduce unnecessary complexity.

Real-time processing pipelines are hard to engineer and maintain, and their technology took decades to evolve. Luckily, in recent years, they’ve become a viable option for companies of all sizes. Not only has data streaming technology improved, but more consultants and vendors offer solutions that put real-time data within reach for smaller businesses at a competitive price.

Real-time vs Batch Data Processing

No time? Here’s everything you really need to know.

Use Cases for Real-time and Batch Data Processing

So, which one is right for your business? Here’s a quick rule of thumb and a couple of real-world use cases for batch and real-time processing.

Batch processing is a good fit for use cases that either use only legacy architecture, or occur on a predictable basis and have no need for real-time insight. For example:

• A small security firm works as a government contractor. It performs weekly risk assessments for its client. Both the client and the firm use older, on-premise infrastructure. 
   
• An electric company keeps track of usage and bills customers monthly.
   

Real-time processing is a good fit for most modern organizations looking for scalable, affordable pipelines. If immediate insight is helpful for any use of a given dataset, real-time processing is a better fit. For example:

• An e-commerce company uses sales data to update the inventory in its online shop. It uses that same data, along with historical data, to create sales reports each week. 
   
• At a moment's notice, a streaming service needs to show a viewer TV recommendations informed by that user’s past experiences as well as the experience of hundreds of thousands of other users.

Wrapping up

If there’s one thing to take away from today’s article, it should be this:

Batch and real-time data processing are both very valid ways to turn raw data into insight, but that wasn’t always the case. 

Batch processing is the legacy method, and much of the infrastructure that makes our society run is built on it. It’s reliable, easy to engineer, and isn’t going anywhere anytime soon. 

And reliable real-time processing is no longer the pipe dream it once was. As more intelligent, efficient real-time processing architectures appear, it’s become an affordable, scalable option for businesses large and small. 

Estuary Flow is built on these ideas. It’s a real-time DataOps platform designed for ease of use. While it’s based on streaming technology, you can use Flow to connect to real-time and batch systems alike. 

You can try Flow for free — just register here. 

Keywords: batch, pipeline, processing, real-time