Beginner’s Guide to Batch Processing

What is batch processing?

Batch processing is a method of running high-volume, repetitive data jobs. The batch method allows users to process data when computing resources are available, and with little or no user interaction.

With batch processing, users collect and store data, and then process the data during an event known as a “batch window.” Batch processing improves efficiency by setting processing priorities and completing data jobs at a time that makes the most sense.

The batch processing method was first used in the 19th century by Herman Hollerith, an American inventor who created the first tabulating machine. This device became the precursor to the modern computer, capable of counting and sorting data organized in the form of punched cards. The cards and the information they contained could then be collected and processed together in batches. This innovation allowed large amounts of data to be processed more quickly and accurately than by manual entry methods.

Basics of batch processing

Batch processing plays a critical role in helping companies and organizations manage large amounts of data efficiently. It is especially suited for handling frequent, repetitive tasks such as accounting processes. In every industry and for every job, the basics of batch processing remain the same. The essential parameters include:

  1.  who is submitting the job
  2.  which program will run
  3.  the location of the input and outputs
  4.  when the job should be run. 

In other words, the who, what, where, and why.

Example — processing financial data in batches 

Many companies use batch processing to automate their billing processes.

Think of a credit card transaction that did not show up in your bank account history until several days after you spent your money. This transaction may have been processed in a batch sometime after you made your purchase.

In another scenario, a wholesale company may only bill its customers once per month and pay its employees every two weeks. Both the monthly billing cycle and bi-weekly payroll cycles are examples of batch processing. \


Batch processing has become common because it provides a number advantages to enterprise data management. Organizations can realize quite a few benefits of batch processing:


Batch processing allows a company to process jobs when computing or other resources are readily available. Companies can prioritize time-sensitive jobs and schedule batch processes for those which are not as urgent. In addition, batch systems can run offline to minimize stress on processors.


Compared to stream processing, batch processing is a less complex system that doesn’t require special hardware or system support for inputting data. Once established, a batch processing system requires less maintenance than stream processing.

Improved data quality

Because batch processing automates most or all components of a processing job, and minimizes user interaction, opportunities for errors are reduced. Precision and accuracy are improved to produce a higher degree of data quality.

Faster business intelligence

Batch processing allows companies to process large volumes of data quickly. Since many records can be processed at once, batch processing speeds up processing time and delivers data so that companies can take timely action. And since several jobs can be handled simultaneously, business intelligence becomes available more quickly than ever before.

Use cases

Affinity Water — millions of customers, billions of liters

Affinity Water, the largest water-only supplier in the UK, uses an automated system to read meters for 3.6 million customers, who use over 900 million litres of water each day. The complexity of managing a water delivery infrastructure, the company’s massive customer base, and the scope of its services means that Affinity must find the most efficient and effective strategies for handling vast amounts of data.

Batch processing allows Affinity to prioritize its computing processes so that actions such as meter reading and billing take place quickly and accurately, without unnecessarily diverting critical resources from other data processing jobs.

Almerys — batch processing in healthcare

When it comes to handling the vast amounts of data generated by healthcare billing, Almerys knows a thing or two about batch processing. The company uses a customized strategy which incorporates batch processing for some jobs, and stream processing for others. As a result, Almerys is able to manage over 1 million paperless, third-party healthcare transactions each day.

Data dilemma: batch or stream processing 

When it comes to deciding which method of data processing is optimal, there is no single right answer. It’s all about finding a solution that works best for the company, the data, and the situation. In some cases, batch processing offers the most cost-effective approach to managing jobs. In other instances, access to streaming data is essential. Many companies use both methods.

Batch processing handles large amounts of non-continuous data. It can process data quickly, minimize or eliminate the need for user interaction, and improve the efficiency of job processing. It can be ideal for managing database updates, transaction processing, and converting files from one format to another.

Stream processing is appropriate for continuous data and makes sense for systems or processes which depend on having access to data in real-time. If timeliness is critical to a process, stream processing is likely the best option. For example, companies who deal with cybersecurity, as well as those working with connected devices such as medical equipment, rely on stream processing to deliver real-time data.

In some cases, the same company may employ both processes, relying on stream processing for data tasks which are time-sensitive, and batch processing for others. For example, a healthcare company that distributes wearable medical devices may use stream processing to collect and monitor data from the device. But batch processing may be more cost effective for managing its customer billing cycles.

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Batch processing and the cloud

Batch processing continues to evolve. Cloud technology has revolutionized the way all types of processing work by allowing data from many kinds of programs to be merged and integrated seamlessly and stored remotely. For batch processing, the most significant change is the migration of data from on-site locations to distributed systems in which data warehouses and data lakes may be stored in multiple locations around the world.

Even with the changes brought about by the rise of cloud-native technologies and storage, batch processing remains as useful today as ever. In fact, the familiar ETL (extract, load, and transform) process of moving and transforming data is in itself a kind of batch processing. Other methods may have arrived, but batch processing isn’t going anywhere anytime soon.

Preparing for the future of batch processing

Businesses are facing more diverse and complex data sets now than ever before. That means that companies can no longer rely solely on batch processing to manage their data. Most companies today use a variety of processing methods to remain competitive. 

Talend Data Management Platform delivers a diverse set of data processing tools and capabilities to make sure businesses always have access to the best tool for their data processing jobs. Talend helps companies navigate the increasingly complex demands of data integration, big data processing, and data analytics.

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