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Batch processing refers to the execution of batch jobs, where data is collected, stored, and processed at scheduled intervals. For decades, batch processing has given organizations an efficient and scalable way to process large volumes of data in predefined batches or groups.
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 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 in with real-time streaming solutions like the Confluent data streaming platform.
Over the last several decades, enterprise organizations came to rely on batch processing 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, as technology evolved and the demand for real-time insights grew, the limitations of batch processing became evident, giving rise to data streaming and stream processing as more agile alternatives.
Traditional thinking views batch processing as fundamentally different from stream processing, since it handles data in discrete chunks rather than real-time streams. 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 ETL (Extract, Transform and Load) processes, daily backups, and large-scale data transformations.
However, batch processing can also be thought of as a special case of stream processing. It can be argued that all data processing is stream processing and the reasons we started with batch processing are due to technical limitations. Since most if not all data can be reduced to streaming data, all data processing, even batch processing, can be viewed as stream processing.
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.
Batch processing and stream processing differ primarily in the following areas:
Since batch processing can be thought of as a special case of stream processing, it’s not quite accurate to compare the two. All things being equal, real-time processing is always better than batch processing, since it would not be necessary to divide the data into batches before processing it. Traditionally, though, real-time processing has been expensive and required a high level of computing resources that, lacking the low cost of storage and compute taken advantage of by stream processing. Therefore, stream processing was only seen as practical for high value applications that require immediate feedback or responses, such as fraud detection, anomaly detection, and real-time analytics.
| Feature | Batch Processing | Stream Processing |
|---|---|---|
| Data processing | Data is processed in batches | Data is processed as it is received |
| Data volume | Large amounts of data | Small amounts of data |
| Data latency | High latency | Low latency |
| Cost | Low cost | High cost |
| Use cases | Data consolidation, data analysis, data mining, data backup and recovery | Real-time analytics, fraud detection, anomaly detection |
Historically, batch processing is a good choice for applications that do not require immediate feedback or response, such as data consolidation, data analysis, data mining, and data backup and recovery. Batch processing has been less expensive than real-time processing and previously required fewer computing resources.
The utility of batch processing has always been limited by how much time you can afford to wait. Specifically, the time it takes to process a batch and the interval between schedule batch runs.
When low-latency responses are critical, it's time to choose data processing. Here are some key real-time and event-driven use cases that indicate the need for data streaming and stream processing:
Using the Confluent data streaming platform allows you to unify your batch processing, data streaming, and stream processing workloads. Process streams in real time as well as persist those streams with infinite storage with a complete suite of data integration, processing, and governance features. Ready to get started with a fully managed Kafka service? Sign up today and master the fundamentals with one of our comprehensive developer courses or hands-on webinars.