What is Batch Processing? 2023’s Comprehensive Guide
Businesses collect data from several internal and external sources. To get a collective and deeper insight into the business performance, integrating this data is an essential part of the data analysis process. To upload collected data to your desired destination such as a Data Warehouse, you can choose a suitable data processing method based on your data volume and business strategies.
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Batch Processing is one such method to effectively handle massive amounts of data and send data in batches to the target system. It is a flexible technique that provides you with more control and assists you in efficiently transferring data with the already available computational resources. Though there are methods like Stream Processing for quickly transferring data in real-time, they are highly ineffective when it comes to dealing with huge volumes of data.
In this article, you will learn about the Batch Data Processing method in detail.
Table of Contents
- What is Batch Data Processing?
- What are the Essential Parameters for Batch Processing?
- What are the Benefits of Batch Data Processing?
- Key Use Cases of Batch Processing
- Understanding Batch Processing in the Cloud
- When should you consider Batch Processing?
- Batch Processing vs Stream Processing: What should you choose?
- What are the Challenges of Batch Data Processing?
What is Batch Data Processing?
Batch processing is an efficient way of running a large number of iterative data jobs. With the right amount of computing resources present, the batch method allows you to process data with little to no user interaction.
After you have collected and stored your data, the batch processing method allows you to process it during an event called a “batch window“. It provides an efficient workflow layout by prioritizing processing tasks and completing the given data jobs when it makes the most sense.
For the very first time, batch processing was brought into use in the 19th century by Herman Hollerith, an American inventor who built the first tabulating machine. This device pioneered the latest computers that can count and sort data organized in the form of punch cards. The card and the information it contains are collected in batches and processed together. This innovation has made it possible to process large amounts of data faster and more accurately than any of the traditional manual input methods.
What are the Essential Parameters for Batch Processing?
Batch processing plays an important role in assisting businesses and enterprises to manage huge volumes of data efficiently. This is especially effective for frequent and monotonous tasks such as accounting processes. The basics of batch processing remain the same for all industries and all jobs. The important parameters are:
- Who submits the job
- Which program runs
- Input and output location
- When the job runs
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What are the Benefits of Batch Data Processing?
Incorporating Batch processing in enterprise data management has provided organizations with the following benefits:
- Efficiency: Often not all the computing resources are available at a time for running a specific job. Batch processing allows businesses to process jobs by prioritizing projects that require immediate attention and planning batch procedures for jobs that aren’t as essential. Batch systems can also run offline to reduce the processor burden.
- Simplicity: Batch processing, in comparison to stream processing, is a less sophisticated system that does not require particular hardware or system support for data entry. A batch processing system requires less maintenance once it is set up than a stream processing system.
- Higher Data Quality: Batch processing automates most or all components of a processing job and minimizes any manual user intervention, thereby reducing the chance of errors or anomalies. This allows you to achieve a greater level of data quality, owing to the considerable improvement in precision and accuracy.
- Accelerated Business Intelligence: Batch processing enables businesses to swiftly process massive amounts of data. Batch processing reduces processing time and ensures that data is delivered promptly because multiple records can be effectively handled at once. Due to the efficient handling of multiple jobs at the same time, you can enjoy much faster business intelligence than ever before.
Key Use Cases of Batch Processing
You can easily find Batch Processing being used in a wide variety of industries and jobs all over the world. To truly understand the batch processing meaning and its application in organizations, you can check out the following common use cases:
- Mainframe-generated data is an excellent application of default batch mode processing. Extracting mainframe data and consolidating it into modern analytics environments can be time-consuming and in most cases, it is not possible to convert to streaming data.
- Batch processing is employed in scenarios where real-time analysis results are not needed, and processing large amounts of information is more important than getting fast analysis results.
- A financial institution sending out the information of all the transactions over a week is a good illustration of batch processing. Similarly, batch data processing can also be used in payrolls, supply chains, billings, orders from customers, line-item invoices, etc. Batch processing is an efficient way for businesses to minimize unnecessary operational costs spent on labor this doesn’t require specialized data input experts. Whether it is the end of the day, week, or the end of a pay period, batch mode gives you complete control over starting the processing.
- Manufacturing industries employ batch processing at a large scale as they need daily reports of the product line operations.
Understanding Batch Processing in the Cloud
Today, cloud computing has transformed the way all forms of processing are done by allowing data from a variety of programs to be effortlessly merged and integrated, as well as stored remotely. The most noticeable change in batch processing is the storage systems and their capabilities. Organizations have migrated their data from on-site storage systems to distributed systems, where data warehouses and data lakes can be kept in numerous places globally.
Despite the changes brought about by the advent of cloud-native technology and storage, batch processing is quite applicable today in most cases. The common ETL (extract, load, and transform) data movement and transformation procedure is a sort of batch processing. Other ways may have emerged, but batch processing will not be phased out very soon.
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When should you consider Batch Processing?
- Batch processing has always been a popularly chosen method by several organizations. This is especially preferred by businesses that still use the older technologies with fewer resources that are not capable of real-time processing and essentially wanted to save network bandwidth as well. You may observe its use is declining in some sectors, though organizations such as Amazon still employ some form of batch-based processing to transport their data.
- You will commonly find a proper use of Batch-based processing by companies with large orders. For instance, if a firm deals with 2,000 orders per day, your system will find it very difficult to process each order in real-time. This can a serious problem especially if your system lacks the right amount of computational resources to support the order quantity. By using a batch-based system, you can set your orders to be processed in a queue by your system instead of processing them all at once.
- If you are dealing with a large number of SKUs, it is recommended that you run them as a batch to prevent system throttling. This allows your system to allocate the required resources to these batch runs, thereby preventing any unnecessary delays or bottlenecks. In case your SKUs need any modifications, the batch mode processing allows you to execute the updates in the backend. In a border sense, you can ensure a smooth, efficient, and clog-free workflow with the batch processing employed in your system.
Batch Processing vs Stream Processing: What should you choose?
In the batch processing model records are collected, merged, and then supplied to the system i.e. data is turned into information batches and sent for processing. For streaming processing-based systems, there is almost an instant flow of data from one device to another i.e. processing usually takes place in real-time.
Choosing the right data processing method comes down to your business strategies, data transactional volume, and customer needs.
- For instance, stream processing is a good option if you need to keep a tab on an event that needs to be detected right away along with a quick response. Especially for fraud detection and cybersecurity, with real-time transactional data, you can quickly identify any fraudulent activities and take appropriate action timely.
- On the other hand, if you need to transport huge volumes of data collected over time, batch processing is the most efficient method that requires very little to almost no user interaction.
What are the Challenges of Batch Data Processing?
Apart from all the advantages of Batch processing, there are a few obstacles you might face while employing it for your data transfer:
- One of the biggest problems companies face is that debugging these systems turns out to be difficult. Without a dedicated IT team or skilled staff, trying to fix the system when a problem occurs can be a disadvantage and may require the assistance of an external consultant.
- Though batch processing typically is implemented to save costs by using the available resources efficiently, you also have to initially invest in the software and the training required to run it. The training may include learning how to schedule batches, setting triggers and what a specific notification alert means.
In this article, you have learned about the Batch Processing method. Businesses often collect large amounts of data over time and need to transfer it to the desired destination. The best possible solution to efficiently transfer your data in limited available resources is batch processing. Stream processing is also another data transfer method, however, it is more useful for real-time data processing with relatively lower volumes of data.
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