What Is Batch Processing?

Historically, this approach to handling data has enabled numerous operational and analytics use cases across various industries. Today, however, batch-based business functions like financial transactions, data analytics, and report generation frequently require much faster insights from the underlying data. The increasing demand for near real-time and real-time data processing, has led to the rise of data processing technologies like Apache Kafka® and Apache Flink®.

Let's dive into how batch processing works so you can understand the best times to use it and when to combine it with real-time streaming solutions like the Confluent data streaming platform.

An Overview – Batch Processing Challenges & How We Got Here

Batch processing has long been the default approach for moving and transforming data in enterprise systems due to its efficiency in handling large volumes of data. Processing data in batches allowed large businesses to manage and analyze data without overloading their systems, offering a predictable, cost-effective solution for tasks like payroll, inventory management, or financial reporting. 

However, today’s digital businesses operate in real time—and can’t afford to wait hours for fresh insights. As technology evolves and the demand for real-time insights grows, the limitations of batch processing have become evident, giving rise to data streaming and stream processing as more agile alternatives.

How Batch Processing Works

While batch processing applications can vary based on the task at hand, here’s an overview of how it works:

• Data Collection: Data is collected over time from various sources and stored until ready for processing

• Batch Execution: A batch job is triggered based on a schedule (e.g., nightly) or a defined data threshold

• Output Generation: The results after the batch execution are generated in various forms, including  reports and database updates.

To implement batch processing effectively, organizations rely on dedicated software and systems that streamline data ingestion, processing, and output generation. Examples of batch processing include extract-transform-load (ETL) processes, daily backups, and large-scale data transformations.

See how batch processing was holding back Globe Group back from driving personalized marketing campaigns.

 

From Batch Processing to Micro-Batches to Data Streaming

From early days of computing, data has always been stored and processed in batches even when it was generated in a stream. This is largely due to technical limitations in data collection, storage, and processing. Over a period of decades, those technical limitations lessened and the cost of storage, compute and networking came down by orders of magnitude. This allowed for the rise of low-cost distributed systems like Apache Hadoop®, which was an early leader in handling large-scale batch processing but often struggled with speed and complexity.

Later, Apache Spark™ emerged as a faster, more flexible alternative, offering in-memory processing that dramatically reduced job execution times, making it suitable for both batch and real-time workloads. However, as the demand for real-time data streams increased, Apache Kafka® was developed at LinkedIn and then open-sourced. Its distributed architecture made it ideal for realizing the high throughput, low latency, and fault tolerance needed for real-time use cases.

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Key Differences Between Batch and Stream Processing

Batch processing and stream processing differ primarily in the following areas:

• Data Handling: Batch processing involves collecting and storing data over a period of time, then processing it in large batches at scheduled intervals. On the other hand, using stream processing pipelines built with technologies like Kafka and Flink transform and enrich your data as it's generated, allowing for immediate insights and actions. 

• Latency: Because batch processing involves processing data in bulk, using it incurs higher latency between data generation and processing a delay which may range from hours to days. Stream processing offers low latency, providing near-instantaneous processing and analysis of data as it arrives. See how L'Oréal powered digital transformation after transitioning away from batch processing. 

• Use Cases: Batch processing is ideal for tasks like end-of-day reporting or data warehousing, where immediate processing isn’t critical. Stream processing is suited for event-driven applications and real-time use cases that require real-time analytics, such as fraud detection, recommendation systems, or monitoring of sensor data.

• Complexity: Batch processing is generally simpler to implement and more cost-effective for large-scale data tasks, while stream processing often involves more complex architectures to manage the continuous flow of data and maintain real-time performance.

Benefits of Real-Time vs Batch Processing

Batch processing is widely used across industries—especially in scenarios where large volumes of data need to be processed during off-peak hours. Here are a few use cases:

• End-of-day reporting: Financial institutions rely on batch processing to compile and process transactions accumulated throughout the day. This enables the generation of comprehensive reports used for compliance, auditing, and performance analysis.

• Payroll processing: Organizations handle payroll in batches—by collecting employee hours, calculating compensation, and issuing payments in one streamlined process.

• Data warehouse updates: Batch processing is commonly used to refresh data warehouses at scheduled intervals. 

But as real-time data becomes more important than ever, businesses across industries are modernizing their data infrastructure to react faster, reduce complexity, and deliver better outcomes. In fact, according to the 2025 Data Streaming Report, 86% of IT leaders cite investments in data streaming as a key priority. Additionally, 44% of IT leaders also cite 5x or more ROI with real-time data streaming while driving significant benefits in the areas of customer experience (95%), security and risk management (92%), product/service innovation (90%), and reduced time to market (86%).

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With data streaming, instead of waiting until the end of the day, financial institutions can process transactions as they happen, enabling real-time visibility into account activity, risk, and compliance metrics. Benefits include immediate detection of anomalies and fraud, and faster decision-making and customer response. When it comes to data warehousing updates, data streaming allows for data to be continuously ingested, transformed, and delivered to analytics platforms in real time, removing the need for scheduled ETL jobs. 

A data streaming platform like Confluent takes it further by helping move data processing, governance, and quality checks closer to the source of data creation—a process known as shifting left—rather than waiting until the data reaches the data warehouse. This reduces data quality issues by up to 60%, decreases compute costs by 30%, and improves engineering productivity.