Batch processing

What is batch processing?

Batch processing is a data movement term that denotes the collection, storage, and processing of data in batches over a time period. It’s the execution of a series of tasks in batches at scheduled times without manual intervention. 

Batch processing is great for systems with high-volume data processing requirements from time to time. Some examples of systems with batch processing are payroll processing, healthcare data systems, finance portals, data warehouses, data marts, etc.

Differences between batch vs. stream processing is that batch processing processes huge chunks of data together at scheduled intervals. Whereas with stream or real-time processing, data is processed whenever it's available for real-time analytics and time-sensitive use cases.

How does batch processing work?

Batch processing happens in cycles and one batch processing cycle involves five tasks, data collection, task scheduling, execution, output, and job ending.

Data collection: Batch processing cycle begins with collecting data from sources gathered over a period (after the last cycle). 

Task scheduling: Batch processing can occur hourly, weekly, monthly, or at custom timings. Scheduling denotes setting the time frequency for the job to begin.

Job execution: This is where the destined data processing happens. It could be data cleansing, transformation, or aggregation. 

Output: The data processing is over and results are being copied or written to the target system.

End of the task: one batch processing cycle is over. System resets and restarts, preparing for the next batch data collection.

How to set up batch processing?

Five-step processes required to set up a batch processing strategy for your company.

1. Identify the data sources required for processing. It could be logs, transactional data, data from streaming apps like sensors, etc.

2. Select a job scheduling tool like Apache Spark or AWS Batch and specify the batch intervals at which the job must be performed. You could also specify which jobs to be prioritized and what resources must be used. 

3. The scheduled jobs are out for execution using processing engines like Apache Hadoop or Spark. 

4. Once the job runs, the output gets stored in a repository, it could be a database, reporting system, or data warehouse. 

5. Monitoring workflows must be enabled, so that you can track if the job runs successfully and there are no failures or processing errors. 

Tools for batch processing

• Apache Hadoop - Could handle both single and distributed batch processing in the case of large datasets with MapReduce.  

• Talend - Comes with sets of tools and processes required for batch processing, specifically suitable for ETL environments.

• AWS Batch - Comes with batch processing capabilities for managing batch jobs. Similarly, there is Microsoft Azure Batch, which comes with high performance, parallel-running batch capabilities.

Advantages of batch processing are the reduction of human labor and cost effectiveness, since it handles CPU and memory more efficiently. Besides, batch processing can scale and accommodate your growing needs without affecting performance. Its flexibility and simplicity make it suitable for all industries, even the heavily regulated industries. 

Batch processing vs. stream processing. Which is better?

While batch processing handles data aggregated from the past, stream processing handles real-time or near-real-time data. If you ask whether batch processing or stream processing is better, it depends on the data workloads, company requirements, current infrastructure, and many other factors. Also, both processes have their own advantages and weaknesses. 

Comparatively, stream processing is slightly more complicated than batch processing, requiring sophisticated tools and setup. 

Hence, batch processing is sufficient in most cases where the end outcome is analytics and periodic reports. 

Stream processing will be required if you have got real-time data processing requirements, like fraud detection and prevention, stock trading predictions,  real-time inventory management, or similar time-bound tasks.

Differences between batch and stream processing

Batch processing is evolving fast and, more recently, micro batch processing is gaining popularity, which divides batch jobs into smaller parts for easier processing. 

This means more effective data pipelines and accurate data processing, helping your organization with timely reporting. Based on your resources, expectations, and data size, you could choose an apt batch processing strategy for your organization.