Batch Processing vs Event Streaming

As data becomes increasingly important to modern business, organizations are tasked with finding the most efficient and effective way to process their data.?

Two of the most popular methods for processing data are batch processing and event streaming. This article will aim to compare these methods, discuss when each is appropriate and describe the technologies used in each.?

What is Batch Processing?

Batch processing is a traditional approach to data management where data is collected, stored and processed in fixed intervals or “batches.” These are high-volume, repetitive data jobs that can involve tasks such as backups, filtering or sorting which can be compute-intensive and inefficient to run on individual data transactions. Instead, data systems process such tasks in batches, typically at off-peak hours when compute resources are more commonly available.?

An example of batch processing would be the monthly electricity bill you receive from your provider. This data isn’t required daily, so it is sufficient for your energy provider to run a monthly batch job to aggregate usage data and process once when bills are due each month.?

Existing tools for batch processing are Apache Hive, Apache Spark, Apache Hadoop.?

What is Event Streaming?

Event streaming is a modern data processing method where data is processed in real-time, as it becomes available. Event streams in that same manner are continuous flows of data that can be collected and processed instantly. With event streaming, data is rarely static, and the ability to “stream” data in real-time has become crucial to the innovation we see in the world today.?

Data processing has evolved from legacy batch processing of data to real-time streaming in which data can be utilized in a flow as it becomes available. Consider how consumers stream Netflix or Apple Music, without having to wait for their entire show or album to download. The ability to stream data in real time enables application functionality that would be impossible without the “streaming” of data. Event-Streaming systems are often based on the Apache Kafka project. Many vendors have built on top of this project for enterprise-grade solutions. Examples include IBM Event Streams and Red Hat OpenShift Stream for Apache Kafka.

Benefits of Streaming Data

The most obvious benefit of Event Streaming is that it allows systems to react in Real-Time. An example would be multi-player video games, video conferencing, or live-sports streaming. The ability to process data in real-time enables a better user experience, while more accurately reflecting ongoing events.

Some others include:?

• Enables better business decisions: Stream processing enables organizations to act on what’s happening now.?
• Better application performance: Systems built with event streams are better enabled to react to changes in the environment. If there is an increase in load, these systems can react immediately rather than waiting for a pre-defined interval.?
• Increase system resilience: Because batch systems build up large amounts of data to be processed all at once, a single piece of data can collapse the whole process. This makes for expensive mistakes. With event streaming, we deal with events as they happen. If an event fails, we can put processes in place to handle that failure without disrupting other events.?

Batch Processing vs Event Streaming

The nature of your data and business needs determines whether batch or stream processing should be utilized. Batch processing is well-suited for applications that do not require real-time insights, as it allows efficient processing of large data volumes at regular intervals. Event Streaming enables organizations to gain real-time insights and actions on incoming data, making it a vital piece of mission critical applications.?

As organizations modernize, many are adopting a real-time data architecture for the benefits mentioned in this article. Any organization interested in adopting an Event-Driven architecture should have a partner that can scale with them. IBM is an experienced vendor with Batch-Processing, Event Streaming, and Message-Queueing capabilities that can scale with your data, wherever it resides.?

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