What is batch processing?
Batch processing is when a computer processes a number of tasks that it has collected in a group. It is designed to be a completely automated process, without human intervention. It can also be called workload automation (WLA) and job scheduling.
Batch processing is an incredibly cost effective way to process huge amounts of data in a small amount of time. Once it has started, the computer will only stop when it discovers an error or abnormality, whereupon it notifies the appropriate staff member or manager.
When is batch processing used in business?
Batch processing has a range of benefits, but it is ideal in businesses where:
- There’s a process that doesn’t need to be addressed immediately and real-time information isn’t needed
- Large volumes of data need to be processed
- There’s a span of time where a computer or system is idle
- A process doesn’t need the input of humans and is repetitive
A good example of batch processing is how credit card companies do their billing. When customers get their credit card bills, it isn’t a separate bill for each transaction; rather, there is one bill for the entire month. That bill is created using batch processing. All the information is collected during the month, but it is processed on a certain date, all at once.
Historically, banks used batch processing at the end of each day so as not to take up compute resources during peak times. However, these days transactions are usually processed immediately.
An example of batch processing that people are familiar with is an email system. Most programs have the capability to store emails for a time period after you send them and then send them as a batch. This gives the user time to delete or edit an email before it is sent to avoid “email regret,” for instance, when you forget to include an attachment.
Why use batch processing?
Batch processing began early on in the origin of computers. Batches of punch cards, with computer programming instructions, would be processed at one time. The batch would run until it was completed, or an error occurred, whereupon it would stop and manual intervention would be required.
This method was used when computer resources were limited and lacked today’s enormous processing power. Running these batches at the end of the day meant valuable computer resources weren’t tied up and allowed the machine to process bulk data at top speed.
Batch processing has changed quite a bit over the years. Now, batch data isn’t just an “end-of-the-day” or overnight process. It doesn’t need an internet connection to process, and it can run asynchronously. Basically, these batches can run in the background at any time that’s suitable without interrupting vital processes.
But even so, with today’s massive computing power and cloud computing, there are still very good reasons that batch processing is used.
Benefits of batch processing
Speed and cost savings
Because batch processing is largely automated, it doesn’t require manual intervention. The automation reduces operational costs and increases the speed at which transactions and data can be processed. Organizations can prioritize the order in which data is processed, if required.
By eliminating people from the process, there are no human errors, saving time and money and resulting in more accurate data and happier end users.
Batch processing systems operate offline. When the day ends, this workhorse is still chugging along. Managers can control when a process starts to avoid overwhelming a system and disrupting daily activities.
Set it and forget it
Once the batch processing system is in place, it is automatic. There is no need to log in and check or adjust anything. If there is a problem, an exception notification is sent to the appropriate staff member. Otherwise, it is a completely hands-off solution that managers can trust.
Keeping it simple
There is no on-going system support, extra data input, or specialized software required. Once the system is up and running, there is no maintenance and it’s a low barrier-to-entry solution for data processing.
Accurate data for machine learning and artificial intelligence
One of the biggest challenges in artificial intelligence is poor quality data. Data scientists spend a lot of their time cleansing data and removing errors and inconsistencies. Batch processing, because of its automated nature, avoids data errors completely. When an abnormality is found, it is flagged immediately so it can be resolved quickly. The end result is highly accurate data that will create accurate predictions.
Better use of existing computer systems
Allowing data to be processed at a point where the system is in low demand allows for maximum use of the existing system. Because batch processing can be triggered or automated to run when the system hits a certain point in bandwidth, there is less need to buy new systems, and existing resources are used more intelligently.
Challenges of batch processing
While batch processing is a great answer, it is not the right answer for every company or scenario. There are limitations and challenges that may not make it the best solution for every organization.
Training and deployment
All new technology requires training. Managers and staff need to understand batch triggers, scheduling, and how to process exception notifications and errors.
Solution: The solution is thorough training, along with simple, easy-to-follow manuals. Once the system is set up, the need for changes may be rare, so it is important that exception training is carried out.
Debugging systems can be quite complex, so it is best to have an in-house employee that understands and specializes in these systems. Some organizations may find that hiring outside consultants is the best solution.
For large businesses and organizations that are processing bulky and continuous data, implementing batch processing will save time and money on labor. However, for a smaller organization that does not have data entry staff or enough hardware to sustain the system, the start-up costs may not be feasible.
Solution: Thorough cost analysis and return-on-investment feasibility studies must be carried out before implementing such systems.
The alternatives to batch processing
There are two alternative ways to process data. Both are recent developments in the timeline of computing, only accessible due to connectivity and the greater availability of compute power.
This is when data is processed directly as it is received or produced. Most data is a continuous stream; think of activity on a website, financial trades, traffic information, or credit card transactions. These systems do not require large amounts of data to be stored but instead have a constant, instant flow.
Stream processing is useful when there are a number of actions happening frequently and the event needs to be acted upon quickly. Examples include share prices or identifying fraudulent credit card transactions.
Real time operating systems
These systems process data as it comes in, with no delays or buffers. Processing times are within microseconds; these systems are reactionary and used when timing is vitally important. Think of air traffic control or multimedia systems. Processing data within tenths of a second is critical to the finished product: a plane landing flawlessly or multimedia systems being synced up.
These two alternative systems are suitable in some environments and use cases, but not others. When implementing systems, organizations should look at their data and the outcomes they want before making a decision.
When should batch processing be used?
As outlined above, there are specific circumstances when batch processing is the ideal choice. There is no right or wrong answer, and the correct choice may even be a hybrid system. A medical system is a good example of choosing a hybrid option: wearable medical devices such as diabetes blood sugar levels need to be stream processed, but billing can be completed in a batch process.
Things that do not need real-time processing and are ideal candidates for batch processing could include:
- Payroll and timesheet processing
- Line-item invoices for any company or organization that accrues data and produces one main output at a certain time
- Bank statements
- Research and reporting
- Supply chain and fulfillment: unlike tracking stock levels, which must be immediate, ordering a replacement product may be a weekly or monthly task
- Billing systems that may prefer to bill once a week or month
- Managing database updates
- Files being converted from one file to another, for example, month-end invoices changing from one format to PDF
When considering batch processing for an organization, the following questions need to be asked:
- Are there a high number of manual tasks to complete? How are these tasks guaranteed to be correct? Is there a system in place to ensure their accuracy, and that they are submitted and processed in the correct order?
- Are there jobs in the system that wait on other jobs to finish? Do you know when each job is completed or when the next one will start?
- Does the organization check for new files manually? Is there a script loop that is frequent enough to efficiently check for files?
- Does the current system have job-level retries at the server? Does it slow things down or reprioritize other tasks? Could your server be better utilized?
The future of batch processing
With huge compute power and cloud computing, is there a future for batch processing? And as data becomes more complex and diverse, and batch processing is no longer the sole solution for data management, is it even relevant anymore?
Batch processing still has a place today and moving into the future. The speed at which batches can be processed uninterrupted, without relying on the internet or human intervention, is incredible. Machine speed means that if you took all the data and entered it in real time, it would take hours, days, or weeks longer. Not having to wait for slow humans or devices makes batch processing a valuable use of computer time.
While batch processing used to be a static process, it is now much more agile. While manual and hard coded approaches aimed for consistency, results were hit and miss. Over its evolution, batch processing is now innovative, with rules-based workflows and processes that create a more efficient, reliable, consistent, and agile approach.
These days, companies also face stringent regulations and mandates that they may not have in the past. This change may dictate a need for policy-driven workflows, with dynamic triggers for batch processes as certain scenarios arise. For example, if a breach occurs, it may trigger a rollback of data and an update to the relevant systems—which can all be automated and run in a batch process.
It is preferable to use batch processing in a number of processes, such as ordering stock. Rather than ordering and shipping items one at a time, it’s far better to order everything when it hits a certain threshold or at the end of a sales period.
Or, another scenario is recording information from the Internet of Things (IoT). Information from a smart meter, for instance, is not needed on a minute-by-minute basis. If a failure or fault occurs, there should be immediate action, but normal use of electricity, water or internet does not need to be supplied every few seconds. Sending data in that way can be a drain on resources.
While the speed and availability of data connectivity enables instant and ongoing processing, there are still benefits for some scenarios to simply wait for batch processing. While it may be tempting to drive all systems in a live manner, it could end up creating a lot of extra work, as well as tying up a system needlessly. Although some may look at batch processing as an artifact of legacy systems, there is still a place for batch processing today, and into the future.
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