How do you choose between batch and streaming data processing?

1 Batch data processing

Batch data processing is the traditional way of processing data in batches or chunks, usually on a fixed schedule or interval. This means that the data is collected, stored, and processed in bulk, and the results are delivered after some delay. Batch data processing is often used for historical analysis, reporting, data warehousing, and offline tasks that do not require real-time or near-real-time feedback. It can handle large and complex data sets efficiently and reliably and leverage existing infrastructure and tools such as Hadoop, Spark, and SQL. It can also perform complex transformations and aggregations on the data as well as ensure its quality and consistency by applying validation and cleansing rules. However, batch data processing can incur high latency and low responsiveness due to the delayed processing of the data, miss out on timely insights or opportunities since it is not available for analysis or action until after processing, struggle to handle streaming or unbounded data sources such as sensors, logs or events, and be costly and wasteful due to redundant storage and processing.

2 Streaming data processing

Streaming data processing is a newer way of processing data in streams or flows, usually as soon as it arrives or in near-real-time. This means that the data is ingested, processed, and delivered continuously and incrementally, with minimal delay. It is often used for real-time analysis, monitoring, alerting, and online tasks that require immediate or near-immediate feedback. The benefits of streaming data processing include reduced latency and increased responsiveness, timely insights and opportunities, handling streaming or unbounded data sources, and being efficient and scalable. However, it can be challenging to handle large and complex data sets due to the small and fast batches or windows of processing. Additionally, it may require new infrastructure and tools such as Kafka, Flink, and Beam. Furthermore, it may perform limited transformations and aggregations on the data which can compromise data quality and consistency without validation and cleansing rules.

3 Choosing between batch and streaming

Choosing between batch and streaming data processing depends on several factors, such as the nature and source of the data, the purpose and goal of the data processing, the latency and throughput of the data processing, and the complexity and quality of the data processing. There is no definitive answer to these questions; different use cases and scenarios may have different trade-offs and preferences. However, some general guidelines are to use batch data processing when the data is bounded, structured, static, and large; when its purpose is for historical analysis, reporting, data warehousing, or offline tasks; when latency and throughput are not critical; and when complexity and quality are high. Conversely, streaming data processing should be used when the data is unbounded, unstructured, dynamic, and fast; when its purpose is real-time analysis, monitoring, alerting or online tasks; when latency and throughput are critical; and when complexity and quality are low.

4 Combining batch and streaming

In some cases, it may be beneficial to combine batch and streaming data processing, to leverage the strengths and mitigate the weaknesses of each method. For example, you can use a hybrid or lambda architecture, where you have two parallel data pipelines: one for batch processing and one for streaming processing. The batch pipeline handles the heavy and complex data processing, while the streaming pipeline handles the light and fast data processing. The results from both pipelines are then merged or synchronized in a serving layer, where they can be queried or consumed by users or applications.

Alternatively, you can use a unified or kappa architecture, where you have a single data pipeline that can handle both batch and streaming data processing. The pipeline uses a streaming platform, such as Kafka, as the source and sink of the data, and a streaming framework, such as Flink or Beam, as the processor of the data. The framework can apply different windowing and triggering strategies to process the data in batches or streams, depending on the needs and preferences of the use case.

5 Examples of batch and streaming

To illustrate the differences and similarities between batch and streaming data processing, here are some examples of use cases and scenarios where one might be more suitable than the other. A retail company may use batch data processing to analyze sales and inventory data across different regions and time periods, while a gaming company may use streaming data processing to monitor user activity and behavior data. Combining batch and streaming data processing can be used by a financial company to detect and prevent fraud and money laundering activities on its transactions. Batch processing involves tools such as Spark and SQL to perform complex calculations on the data, while streaming processing involves tools such as Flink or Beam to perform simple calculations. The results are delivered through BI tools or web/mobile apps, depending on the use case. Both pipelines can be merged or synchronized in a serving layer for users or applications to query or consume.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?