Batch Processing vs. Stream Processing: The Ultimate Showdown

When it comes to big data and data analytics, there is a lot of confusion about the difference between stream processing and batch processing. In this article, we will clear up that confusion and explain the differences between these two types of processing. We will also discuss when each type of processing is applicable. So, let’s get started!

Managing the Ever-Increasing Amounts of Data and Data Sources

Data is being generated at an unprecedented rate nowadays, and that rate is only continuing to grow. In fact, it is estimated that 2.5 quintillion bytes of data are created every single day, with 90% of the world’s data created in just the past two years.

This data comes from a variety of sources, including social media, sensors, eCommerce data, and more, with the growth in popularity of Internet of Things (IoT) devices, in particular, accelerating the process. With so much data generated, it is becoming increasingly difficult to manage and make sense of it all.

As innovation rapidly advances, developers are tasked with exploring increasing amounts for data analysis – terabytes or even petabytes – in very short time frames. Moreover, a large amount of data only leads to the creation of yet more data, according to the phenomenon known as data gravity.

There are, of course, numerous advantages of having access to this data, but it can be difficult to know how to use them best when you need to make decisions quickly. As more companies move toward a digital-first model, they are increasingly concerned with finding the best way to accelerate their data analysis processes.

Enter batch processing and stream processing. These are two of the possible methods that can be used to manage the ever-increasing amounts of data. But which one is right for you? Maybe you need them both? Let’s take a closer look at each type to find out.

Batch Processing

How Does Batch Processing Work, and What Are Its Key Features?

Batch Processing is a method to process large volumes of data that have been collected and stored over a period of time at once and passed over to an analytics system. It requires some kind of storage (database of a file system) for loading and processing data that is finite in size (though it can be significant in amounts - e.g., big data). This technique involves grouping together transactions or data records and handling them as one rather than individually.

Prioritizing data-intensive jobs like this, when it best suits the user rather than the other way around, improves productivity. That’s because users can process all data at once during a designated “batch window” instead of doing so immediately if they collect and store it first.

In the past, batch processing was the only method of handling large amounts of data because computers weren’t powerful enough to process them in real-time. The basis of modern computing is the first tabulating machine, which organized punch cards and the data on them to be processed in batches quicker and more accurately compared to manual entry.

Nowadays, batch processing is still used for some tasks, but it has largely been replaced by stream processing for most applications that require real-time data analysis.

Use Cases for Batch Processing

Batch processing is mainly used for tasks that do not require real-time data analysis or decision-making, such as:

• data backup and archiving (e.g., overnight backups)
• ETL (extract, transform, load) processes (i.e., data migration between systems)
• report generation (e.g., monthly financials, payroll, and billing systems)
• analytics tools for gaining insights from data (e.g., customer segmentation)
• machine learning or data mining (e.g., training a neural network)

Stream Processing

How Does Stream Processing Work, and What Are Its Key Features?

Stream processing means processing data when required for a particular usage or as they are created. This means that data are collected and then processed immediately or very soon after it is collected, allowing for real-time streaming data analysis and decision-making that are essential for many applications.

The stream processing technology is used to process a constant feed of data in (near) real-time to be utilized further, create reports or trigger automatic responses without needing to be downloaded first and with the minimum possible latency in situations when any delays could result in negative outcomes. Real-time processing means that data will be acted on almost immediately, within milliseconds. Streaming data architecture gives you the ability to ingest, process, store, enrich, structure, and analyze data in motion.

A stream processor will continually read and process data streams from input sources according to some rules or logic and write the results to output streams. The processor can use one or more threads to enable parallelism and improve performance.

Use Cases for Stream Processing

Stream processing is mainly used for tasks that require real-time data analysis and decision making, such as:

• sensor data processing (e.g., real-time traffic monitoring)
• log data analysis (e.g., to detect anomalies or intrusions)
• recommendation engines (e.g., real-time product suggestions)
• IoT applications (e.g., detecting anomalies in sensor data)
• fraud detection (e.g., stop fraudulent transactions, credit card fraud)
• clickstream analysis (e.g., real-time analytics detecting user behavior patterns, customer service systems)
• financial trading and risk management (e.g., identifying arbitrage opportunities)
• other machine learning and AI applications (e.g., predictive analytics, especially in solutions that need to compare and analyze historical and real-time data sources)

What Are Some of the Challenges Associated With Real-Time Streaming?

Scalability of The Infrastructure

The stream data processing infrastructure must be able to scale up or down quickly and easily to meet changing demands, which could be due to a sudden increase in the data rate (e.g., during a marketing campaign) or the need for data stream processing from a new data source (e.g., adding a new sensor to an IoT application). As applications scale, adding more capacity, resources, and servers should happen immediately to keep up with the exponential increase in data generation.

Data Ordering and Managing Delays

Data from different sources might not always arrive in the sequential order in which each generated data packet was created. To function properly, applications (and developers) must provide mechanisms that allow sorting incoming events if necessary.

There can also be delays or interruptions to continuous data streams due to network congestion or other factors.

Fault Tolerance & Reliability

The data streaming infrastructure must be able to withstand errors and have a high uptime in order to prevent disruptions to service, even if failures of individual components do occur (fault tolerance when it comes to a single point of failure). The use of redundancies and/or replicas might be required to achieve this.

Data Consistency

When a set of data is being constantly updated, all or part of the infrastructure for processing data often needs to have an up-to-date copy (e.g., if multiple stream processors are being used for redundancy). There are various ways to achieve data consistency, such as using a quorum or master-slave replication.

High Requirements for Storage and Processing Resources

Real-time processing (or near) tends to be a resource-intensive task with high computational requirements, especially if the data rate is high and/or the sources are distributed (e.g., sensors in an IoT device). This often requires the use of powerful processors and/or GPUs, as well as fast storage devices for stream processing.

Batch vs. Stream Processing – Comparison of Key Features

• Hardware – a lot of resources are needed to store and process data in large batches, vs. streaming data packets require less storage, but more resources are necessary for meeting real-time latency, consistency, and fault tolerance guarantees.
• Performance – for batch processing, latency can vary from a few minutes or hours up to even several days vs. milliseconds of latency required in order to ensure a smooth user experience in data streaming.
• Data set – feed data packets processed in large batches vs. continuous data streams.
• Analysis – complex process over an extended period of time for the generated data vs. straightforward computation and reporting as the data is streamed.

What Are the Benefits of Stream Processing Over Batch Data Processing?

Processing Speed

Since data can be processed as soon as it arrives without having to wait for a batch to be completed, stream processing technologies can be much faster than batch data processing.

Flexibility

Stream process transaction data is generally more flexible than batch, as a wider variety of end applications, data types, and formats can easily be handled. It can also accommodate changes to the data sources (e.g., adding a new sensor to an IoT application).

Lower Cost

The costs of stream processing are often lower than those of batch data processing because of the lack of a need to store data before processing it. Stream processing can also be more efficient in terms of resource utilization (e.g., CPU, memory, storage).

Tools for Real-Time Data Processing

• Apache Kafka Streams
• Apache Spark
• Apache Flink
• Apache Samza
• Apache Hive
• Apache Storm
• Apache Apex
• Apache Flume

How Can You Get Started With Stream Processing in Your Own Organization or Business?

First, you must have a clear understanding of your data and its sources to determine which stream processing tool would be best suited for your needs. Second, you need the necessary infrastructure in place to support stream processing, including a fast and reliable data storage system and a cluster of machines with the required processing power.

Finally, you need the right team to design, build, and operate your stream processing system. This team should have expertise in data engineering, distributed systems, and big data processing.

Perhaps that team is nexocode? Contact us and get expert support in big data engineering.