Batch processing provides efficient and scalable ways to process large volumes of data in predefined batches or groups. At its core, batch processing refers to the execution of batch jobs, where data is collected, stored, and processed in batches, often at scheduled intervals. This approach offers numerous use cases across various industries, such as financial transactions, data analytics, and report generation.
Combining fault-tolerant, scalable batch processing and streaming in one, Confluent can process data streams in real time with infinite storage. Get started with a complete suite of stream processing tools on any cloud.
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.
Batch processing can also be thought of as a natural stepping stone to 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 such as Hadoop dramatically increasing the size of a batch and shortening the time it takes to process a batch. This latency decrease blurred the line between batch processing and stream processing.
Batch processing introduces an arbitrary difference from stream processing, as it requires the data be bounded in discrete chunks rather than in real-time streams. To implement batch processing effectively, organizations rely on dedicated software and systems that streamline data ingestion, processing, and output generation.
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 time. Specifically,
Both of these challenges have technical solutions, such as using a linearly scalable distributed shared nothing architecture and adding more nodes, but as the processing time and interval between scheduled batch runs both decrease, batch processing becomes more like stream processing.
Event streaming with Confluent unifies batch processing and stream processing, allowing you to accomplish both with the same platform. Confluent allows you to process streams in real time as well as persist those streams with infinite storage, as well as a complete suite of stream processing technologies including Kafka Streams, KSQL, and our fully managed Apache Flink service in Confluent Cloud.