Rapid growth in the volume, velocity and variety of data has made data processing a critical component of modern business operations. Batch processing and stream processing are two major and widely used methods to perform data processing. In this blog, we will explain batch processing and stream processing and will go over their differences. Moreover, we will explore the pros and cons of each, and discuss the importance of choosing the right approach for your use cases. If you are looking for Streaming ETL vs. Batch ETL, we have a blog on that too.
What is Batch Processing?
Batch processing is a data processing method on large volumes of fully stored data. Depending on the use case specifics, a batch processing pipeline typically consists of multiple steps including data ingestion, data preprocessing, data analysis and data storage. Data goes through various transformations for cleaning, normalization, and enrichment, and different tools and frameworks could be used for analysis and value extraction. The final processed data gets stored in a new location with a new format such as a database, a data warehouse, a file, or a report.
Batch processing is used in a wide range of applications where large volumes of data need to be processed efficiently in a cost-effective manner with high throughput. Examples are: ETL processes for data aggregation, log processing, and training predictive ML models.Pros and cons of batch processing
Batch processing has its advantages and disadvantages. Here are some of its pros and cons.
- Batch processing is efficient and cost-effective for applications with large volumes of already stored data.
- The process is predictable, reliable and repeatable which makes it easier to schedule, maintain, and recover from failures.
- Batch processing is scalable and can handle workloads with complex transformations on large volumes of data.
- Batch processing has a high latency, and processing time can be long depending on the volume of data and complexity of the workload.
- Batch processing is generally not interactive, while the processing is running. Users need to wait until the whole process is complete before they can access the results. This means batch processing does not provide real time insights into data which can be a disadvantage in applications where real-time access to (partial) results is necessary.
What is Stream Processing?
Stream processing is a data processing method where processing is done in real time as data is being generated and ingested. It involves analyzing and processing continuous streams of data in order to extract insights and information from them. A stream processing pipeline typically consists of several phases including data ingestion, data processing, data storage, and reporting. While the steps in a stream processing pipeline may look similar to those in batch processing, these two methods are significantly different from each other.
Stream processing pipelines are designed to have low latency and process the data in a continuous mode. When it comes to the complexity of transformations, batch processing normally involves more complex and resource intensive transformations which run over large, discrete batches of data. In contrast, stream processing pipelines run simpler transformations on smaller chunks of data, as soon as the data arrives. Given that stream processing is optimized for continuous processing with low latency, it is suitable for interactive use cases where users need to receive immediate feedback. Examples include fraud detection and online advertising.
Pros and cons of stream processing
Here is a list of pros and cons for stream processing.
- Stream processing allows for real time processing of data with low latency, which means the results can be gained quickly to serve use cases and applications with real-time processing demands.
- Stream processing pipelines are flexible and can be quickly adapted to changes in data or processing needs.
- Stream processing pipelines tend to be more expensive due to their requirements to handle long-running jobs and data in real time, which means they need more powerful hardware and faster processing capabilities. However, with serverless platforms such as DeltaStream, stream processing can be simplified significantly.
- Stream processing pipelines require more maintenance and tuning due to the fact that any change or error in data, as it is being processed in real time, needs to be addressed immediately. They also need more frequent updates and modifications to adapt to changes in the workload. Serverless stream processing platforms like DeltaStream simplify this aspect of stream processing as well.
Choosing between Stream Processing and Batch Processing
There are several factors to consider when deciding whether to use stream processing or batch processing for your use case. First, consider the nature and specifics of the application and its data. If the application is highly time-sensitive and requires real-time analysis and immediate response (low latency), then stream processing is the option to choose. On the other hand, if the application can rely on offline and periodic processing of large amounts of data, then batch processing is more appropriate.
Second factor is the volume and velocity of the data. Stream processing is suitable for processing continuous streams of data arriving in high velocity. However, if the data volume is too high to be processed in real time and it needs extensive and complex processing, batch processing is most likely a better choice though it comes with the cost of sacrificing low latency. Scaling up stream processing to thousands of workers to handle petabytes of data is very expensive and complex. Finally, you should consider the cost and resource constraints of the application. Stream processing pipelines are typically more expensive to set up, maintain, and tune. Batch processing pipelines tend to be more cost-effective and scalable, as long as no major changes appear in the workload or nature of the data.
Batch processing and stream processing are two widely used data processing methods for data-intensive applications. Choosing the right approach among them for a use case depends on characteristics of the data being processed, complexity of the workload, frequency of data and workload changes, and requirements of the use case in terms of latency and cost. It is important to evaluate your data processing requirements carefully to determine which approach is the best fit. Moreover, keep an eye on emerging technologies and solutions in the data processing space, as they may provide new options and capabilities for your applications. While batch processing technologies have been around for several decades; Stream processing technologies have seen significant growth and innovation in recent years. Beside several open-source stream processing frameworks such as Apache Flink which gained widespread adoption, cloud-based stream processing services are emerging as well which aim at providing easy-to-use and scalable data processing capabilities.
DeltaStream is a unified serverless stream processing platform to manage, secure and process all your event streams. DeltaStream provides a comprehensive stream processing platform that is easy to use, easy to operate, and scales automatically. You can get more in-depth information about DeltaStream features and use cases by checking our blogs series. If you are ready to try a modern stream processing solution, you can reach out to our team to schedule a demo and start using the system.