How do you process data in real-time vs. batches?

1 Real-time data processing

Real-time data processing, also known as streaming or event-driven processing, is the technique of processing data as soon as it arrives, without waiting for a fixed interval or a large volume of data. Real-time data processing enables you to react to data changes in near real-time, perform complex analytics on the fly, and deliver timely insights to your users or applications. Some examples of real-time data processing are fraud detection, sentiment analysis, and personalized recommendations.

2 Batch data processing

Batch data processing, also known as offline or periodic processing, is the technique of processing data in large chunks or batches, usually at regular intervals or after a certain trigger. Batch data processing is suitable for scenarios where you don't need immediate results, where you have a lot of historical data to process, or where you need to perform heavy computations that require more resources and time. Some examples of batch data processing are data warehousing, reporting, and machine learning.

3 Advantages of real-time data processing

Real-time data processing has several advantages over batch data processing, such as reducing latency and improving performance, enabling real-time decision making and action, supporting dynamic and interactive applications, and allowing you to handle complex and unpredictable data patterns and volumes. This can result in faster and more efficient delivery of results and enable you to take action based on the latest data.

4 Challenges of real-time data processing

Real-time data processing comes with some challenges, such as increased infrastructure and maintenance costs to handle high-throughput and low-latency data streams. It also poses more technical and operational complexity and risks, such as data loss, duplication, or inconsistency. Moreover, rigorous testing and monitoring is required to ensure data quality and reliability. Additionally, some types of analytics or transformations may not be supported due to the incomplete view of the data.

5 Advantages of batch data processing

Batch data processing offers several advantages over real-time data processing, such as reducing complexity and overhead by simplifying the data pipeline and processing logic. It also enables more comprehensive and accurate analytics and transformations by using a complete and consistent data set, and supports more scalable and cost-effective solutions by leveraging cheaper and more abundant resources. Furthermore, batch data processing allows you to handle large and historical data sets that are not time-sensitive or volatile.

6 Challenges of batch data processing

Batch data processing has some drawbacks that can adversely affect its performance, such as increasing latency and reducing responsiveness. It also limits real-time visibility and action based on the current data state, restricting dynamic and interactive applications that require continuous updates and feedback. Additionally, it may not support some types of analytics or transformations that require a granular or incremental view of the data.

7 Tools and frameworks for data processing

Depending on your data processing needs and preferences, you can choose from a variety of tools and frameworks to implement real-time or batch data processing. Apache Kafka is a distributed streaming platform that enables you to publish, subscribe, process, and store data streams in real-time or near real-time. Apache Spark is a unified analytics engine that supports both batch and streaming data processing, as well as SQL, machine learning, graph, and structured and unstructured data. Apache Flink is a stateful stream processing framework that enables you to process data streams in real-time or near real-time, with low latency, high throughput, and fault tolerance. Apache Airflow is a workflow management platform that enables you to orchestrate and schedule batch data processing tasks, dependencies, and pipelines. Lastly, Apache Hadoop is a distributed data processing platform that enables you to store and process large and diverse data sets in batches using the MapReduce programming model and the Hadoop Distributed File System (HDFS).

8 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?