With the constant evolution of technology, batch processing has become essential for efficiently handling large volumes of data. This type of processing plays a crucial role in large enterprises.
This is because, with batch processing, it is possible to perform tasks in a pre-scheduled, systematic manner, without the need for constant human interaction, resulting in various benefits for businesses.
So, if you want to learn more about what batch processing is, its steps, advantages, and use cases, just continue reading the article. Check it out now!
What is Batch Processing?
Batch processing is a method of data processing that groups similar tasks into batches and executes them sequentially. It is particularly suitable for executing tasks that are repetitive, time-consuming, or require significant computational resources.
Also known as batch processing, it empowers and streamlines the tasks and workflows of your business. Traditional batch processing requires IT developers to manually write scripts for each step, such as reading and processing batch files.
This process can be time-consuming and occupy valuable time that could be better spent on higher-value tasks. Automation facilitates the creation of the necessary code to execute batch processes.
Batch processing is one of the two main data processing methods, with the other being streaming processing. The key difference is that streaming processing processes data in real-time as it arrives.
Learn more: What is process mining and how to combine it with RPA?
Benefits of Batch Processing
Batch processing offers several benefits for IT teams and automation. Below are the main ones:
- Efficiency: Optimizes the efficiency of business processes by automating repetitive tasks. This can free up human resources to focus on more strategic tasks and complex automations.
- Cost Reduction: Contributes to reducing infrastructure costs, as it allows processing during both low and high-demand periods with parallel processing.
- Improved Accuracy: Supports the optimization of the accuracy of business processes by reducing the likelihood of human errors. This can lead to cost reductions, improved customer satisfaction, and a reduction in compliance risk.
- Decision-Making Support: Helps companies make better decisions based on historical data. For example, companies can use it to analyze sales data to identify trends and growth opportunities.
Learn more: Intelligent Automation – Why Combine AI and RPA?
Batch Processing Stages
Batch processing, or batch processing, involves several key stages. Learn more about each of them:
1. Data Collection
The first stage of batch processing is data collection. Data can be gathered from various sources, including:
- Databases: Data may come from relational databases, NoSQL databases, or object databases.
- Files: It can also be collected from text files, CSV files, or images.
- Sensors: Another data source is sensors, such as temperature, humidity, or motion sensors.
The way data is collected depends on its source. For example, data from a database can be collected using an SQL query. Data from a file can be gathered using a file reading function. Sensor data can be gathered from a sensor programming library.
2. Data Preparation
After collection, data needs to be prepared for processing, which may involve the following tasks:
- Data Cleaning: Involves removing invalid or irrelevant data. The former may include missing, duplicate, or erroneous data. The latter is unnecessary for processing.
- Data Validation: Linked to checking the accuracy and integrity of the data. It involves verifying valid values, consistent data, and an acceptable data range.
- Data Transformation: Involves converting data into a format that can be processed by the batch processing system.
Data preparation is an important stage of batch processing as it ensures that data is processed correctly.
3. Execution
Once preparation is complete, the data is ready to be processed. The execution of batch processing is linked to a series of sequential tasks. These may include calculations, analyses, or report generation.
Sequential execution means each task is performed and completed before the start of the next task. This ensures that the results of one task are used by the next.
4. Monitoring
Finally, monitoring is conducted to ensure that tasks are executed as planned. To perform monitoring, a variety of tools and techniques can be used, such as logs, alerts, and reports.
Monitoring is crucial to ensure that batch processing runs successfully and that the results are accurate.
Learn more: What is automation software and what are its benefits?
Use Cases of Batch Processing
Batch processing is often employed for tasks that do not require real-time responses. Below, we outline some examples of this usage. Take a look:
- Financial Data Processing: It can be used to process financial transactions, generate financial reports, and reconcile accounts. This can help businesses save time and money.
- Inventory Management: It is useful for tracking inventory, conducting inventories, and generating purchase orders, ensuring that companies have the necessary stock to meet customer demands while avoiding waste.
- Data Analysis: It can be employed to analyze historical data, generate insights, and identify trends, supporting companies in making better decisions regarding marketing, sales, and operations.
- Report Generation: Batch processing can be used to generate periodic reports, such as financial, sales, or performance reports.
- Calculations: It can be employed to perform complex calculations, such as tax, statistical, or financial calculations.
- Data Transformation: Batch processing can help convert data from one file format to another or from one database format to another.
How to Perform Batch Processing with Data Pools?
Data pools are crucial elements in batch processing, functioning as dynamic queues. They are designed to manage and optimize the handling of data from various sources, such as databases and spreadsheets.
This management includes overseeing the complete life cycle of processed items, from initiation to completion, and efficiently handling processing attempts and queue management during error occurrences or when systems are offline.
Some RPA tools, for example, already have data pool functionalities, allowing both manual and automated data entry. This includes task prioritization and importing datasets from external sources, such as CSV files.
As new data is input into the pool, corresponding tasks are automatically generated and queued for processing, showcasing the system’s ability for efficient task management and automation.
However, establishing a data pool requires some considerations and requirements, including:
- Definition of consumption policies that dictate how data is processed and when it should be reprocessed in case of execution failures.
- Timeout strategy for each task, helping identify and address processes prone to delays or likely to encounter issues.
- Implementation of schemas consisting of labels and data types for each piece of information added to the pool, ensuring data integrity and facilitating processing.
- Real-time monitoring and task management, including tracking the number of items in progress, those remaining, and the success rates of processed items.
Learn more: RPA Software: Types and Features
Differences between Batch Processing and Stream Processing
The main difference between batch processing and stream processing is that batch processing handles data in batches, while stream processing processes data as it arrives.
Other differences between the two methods include:
- Response Time: Batch processing generally has a slower response time than stream processing, as data needs to be collected and grouped before being processed.
- Computational Resources: Batch processing often requires fewer computational resources than stream processing, as data can be processed in batches.
- Data Requirements: Batch processing may require data to be structured and consistent, while stream processing can handle unstructured or inconsistent data.
The choice between batch processing and stream processing depends on the specific needs of the application. Batch processing is a good choice for tasks that do not require real-time responses and can be executed in batches.
On the other hand, stream processing is more suitable for tasks that require real-time responses and need to be processed as data arrives.
Combining Batch Processing and RPA with Data Pools
We hope this article helps you, IT manager, better understand your company’s needs and find the best path to meet them.
So, if you’re looking for a solution for your company, take the opportunity to get to know BotCity. The robotic process automation (RPA) software is transforming batch processing into RPA by enabling parallel processing of tasks.
With the BotCity Maestro orchestrator, you can choose the processing consumption policy, the number of retries in case of errors, triggers for launching new tasks, maximum processing time per item, among many other settings.
Our orchestrator is ideal for scaling batch processing operations through remote runners and managing distributed tasks. If your company deals with large volumes of data and complex batch processing tasks, sign up for free on the BitCity website or talk to our experts!
