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Batch processing

Process management11/27/2025Basic Level

Batch processing involves executing a series of non-interactive jobs or tasks on large volumes of data without manual intervention. It is used for efficient, automated data management and updates.

What is Batch processing? (Definition)

Batch processing refers to the execution of a series of programs or jobs on a computer system without manual intervention. These jobs are typically processed in batches, meaning a collection of data or tasks is processed all at once, rather than individually or interactively. This method is particularly effective for large volumes of data where immediate user interaction is not required and efficiency is paramount.

Why Batch processing is Important for E-commerce

In e-commerce, batch processing is fundamental for managing extensive product catalogs and operational tasks efficiently. Online retailers often deal with thousands or even millions of SKUs, each with numerous attributes, images, and prices that require frequent updates. Manually updating this data is impractical and error-prone.

Examples of Batch processing

  • 1Updating prices for 10,000 products across an entire catalog based on a new pricing strategy.
  • 2Importing a daily CSV file containing inventory updates for all products from a supplier's ERP system.
  • 3Exporting product data feeds to multiple marketplaces (e.g., Bol.com, Amazon) and comparison shopping engines (e.g., Google Shopping) simultaneously.
  • 4Applying a new product category or attribute to hundreds of products within a specific collection.
  • 5Resizing and watermarking thousands of product images overnight for a new channel.

How WISEPIM Helps

  • Efficient Data Updates: WISEPIM enables users to update thousands of product attributes, prices, or inventory levels simultaneously, ensuring data consistency and reducing manual effort.
  • Automated Content Syndication: Product data can be batched and automatically pushed to various e-commerce channels, marketplaces, and social media platforms, saving time and ensuring timely updates.
  • Streamlined Data Imports & Exports: WISEPIM facilitates the import of large datasets from ERPs or supplier systems and the export of product feeds to external platforms, all managed as efficient batch processes.
  • Improved Data Quality Control: Batch operations allow for mass validation and enrichment, ensuring that bulk changes adhere to predefined data quality rules before publication.

Common Mistakes with Batch processing

  • Not validating data before batch processing, leading to errors propagating throughout the system and requiring extensive manual correction.
  • Running intensive batch jobs during peak operational hours, which degrades system performance and negatively impacts user experience.
  • Lacking comprehensive error handling and logging mechanisms, making it difficult to identify, troubleshoot, and resolve issues efficiently.
  • Infrequent or inconsistent batch processing for critical data updates, resulting in stale product information and misaligned inventory levels.
  • Over-reliance on manual triggers for batch jobs, increasing the risk of human error, missed schedules, and operational bottlenecks.

Tips for Batch processing

  • Implement robust data validation rules at the ingestion stage to prevent erroneous data from entering the batch process.
  • Schedule batch jobs strategically during off-peak hours to minimize impact on real-time systems and user experience.
  • Design and implement comprehensive error handling, logging, and alerting mechanisms for all batch processes to facilitate quick issue resolution.
  • Automate the scheduling, execution, and monitoring of batch jobs using dedicated orchestration tools to reduce manual effort and human error.
  • Segment batch processes by data criticality and volume, allowing more frequent updates for essential product information and optimizing resource allocation.

Trends Surrounding Batch processing

  • AI-driven optimization: Leveraging AI and machine learning to predict optimal batch schedules, identify data quality issues pre-processing, and enhance data transformation logic.
  • Increased automation and orchestration: Advanced tools for automating complex batch workflows, integrating them with broader enterprise automation and CI/CD platforms.
  • Cloud-native and serverless architectures: Migrating batch jobs to cloud platforms (e.g., AWS Batch, Google Cloud Dataflow) for enhanced scalability, cost efficiency, and reduced operational overhead.
  • Real-time data synchronization support for headless commerce: While batch processing is not real-time, it remains crucial for efficiently synchronizing large product datasets to various headless frontends and channels.
  • Focus on data observability: Implementing robust monitoring and alerting for batch jobs to ensure data integrity, performance, and timely completion.

Tools for Batch processing

  • WISEPIM: Essential for managing large-scale product data, supporting batch imports, exports, and updates across various e-commerce channels.
  • Akeneo: A leading PIM system that heavily relies on batch processing for efficient product data ingestion, enrichment, and distribution to diverse touchpoints.
  • Salsify: A PIM and Product Experience Management (PXM) platform that leverages batch processing for comprehensive product content syndication and data management.
  • Magento: An e-commerce platform offering robust built-in capabilities for batch importing and exporting product data, managing inventory, and processing orders.
  • Apache NiFi: An open-source tool designed for automating data flow between systems, often used for building complex and scalable batch data pipelines.

Related Terms

data-syndicationproduct-feeddata-qualityautomationpim-systemsku

Also Known As

Bulk processingBatch jobNon-interactive processing