What are the benefits and challenges of using micro-batching for streaming data integration?

1 What is micro-batching?

Micro-batching is a technique that divides the incoming data stream into small batches based on time or size, and processes each batch as a mini-batch job. Unlike pure stream processing, which processes each record individually and continuously, micro-batching allows for some latency and buffering between data ingestion and processing. However, unlike pure batch processing, which processes large batches of data periodically, micro-batching enables near-real-time processing and delivery of results.

2 How does micro-batching work?

Micro-batching relies on a distributed computing framework that can handle both batch and stream processing, such as Apache Spark, Apache Flink, or Apache Beam. These frameworks provide APIs and libraries that abstract the complexity of micro-batching and allow the user to write code that looks like batch processing, but runs as stream processing under the hood. The framework handles the details of partitioning, distributing, and scheduling the micro-batches across a cluster of nodes, as well as managing fault tolerance, state management, and checkpointing.

3 What are the benefits of micro-batching?

Micro-batching provides several advantages for streaming data integration, such as scalability, performance, and flexibility. By parallelizing and distributing the processing across multiple nodes and adjusting the batch size and frequency according to the load, micro-batching can handle large volumes of data. It can also reduce the overhead of network communication, disk I/O, and garbage collection that occur in pure stream processing. Furthermore, micro-batching can support various types of data sources, sinks, and transformations, as well as different levels of latency and consistency requirements. It can also integrate with other systems and tools that are designed for batch processing, such as data warehouses, ETL tools, and BI tools.

4 What are the challenges of micro-batching?

Micro-batching is not without its limitations and trade-offs for streaming data integration. Most notably, it introduces latency between data arrival and processing, which may not be suitable for use cases that require real-time or near-real-time responses. Furthermore, micro-batching adds complexity to the data integration pipeline, necessitating tuning of the batch size and frequency, managing state and checkpoints, as well as handling errors and failures that may occur in the micro-batches. Additionally, it requires choosing and configuring an appropriate framework and platform that can support micro-batching, which may incur higher costs than pure stream or batch processing due to the increased resources and infrastructure needed to run the micro-batches and store intermediate data.

5 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?