Daniel Greenberg
AUG 1, 2023
9 min read
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In today’s Big Data landscape, there are two primary ways of processing data: batch processing and stream processing.

Both methods offer unique advantages and disadvantages, depending on your use case. Here’s a quick overview of both, including the pros and cons of each method:

Batch Processing vs. Stream Processing – Differences

Batch processing is a method of running repetitive, high-volume data jobs in a group where no user interaction is needed, while stream processing is a technique of collecting, analyzing, and delivering data that is in motion.

With batch processing, data is collected, stored, and then processed. It is a tool that can be used for big amounts of data and end-of-cycle processing like the end-of-day or month generation of reports, settling overnight trades, or payrolls. Batch processing can also be done in small batches, typically known as micro-batch processing; a form of processing that Rivery offers up to once every five minutes.

With stream processing, a data stream can constitute any type of data, like factory production or other process data, financial transactions, web traffic, stock market data, and more. It entails a diverse set of tasks performed in parallel or series (sometimes in both) and is usually used for real-time analytics or streaming media.

If we closely examine batch vs. stream processing, this is what we can conclude:
Batch processing collects data over time and sends it for processing once collected. It is generally meant for large data quantities that are not time sensitive.

Stream processing continuously collects data and processes it fast, piece by piece, and is typically meant for data needed immediately.


Batch Processing

Stream Processing

The Nature of the Data

Processed gradually in batches.

Processed continuously in a stream.

Processing Time

On a set schedule.

Constant processing.


Simple, as it deals with finite and predetermined data chunks.

Complex, as the data flow is constant and may lead to consistency anomalies.

Hardware Requirements

Varies but can be performed by lower-end systems as well as high-end systems.

Demanding while also requiring that the system be operational at all times.


High. Batch processing is intended for large amounts of data, and as such, it is optimized with that goal in mind.

Varies depending on the task at hand.


Email campaigns, billing, Invoicing, scientific research, image processing, video processing, etc.

Social media monitoring, fraud detection, healthcare monitoring, network monitoring, etc.

Consistency & Completeness of Data

Data consistency and completeness are usually uncompromised upon processing.

Higher potential for corrupted data or out-of-order data.

Error Recognition & Resolution

Errors can only be recognized and resolved after the processing is finished.

Errors can be recognized and resolved in real-time.

Input Requirements

In batch processing, inputs are static and preset.

In stream processing, inputs are dynamic.

Available Tools

Apache Hive, Apache Spark, Apache Hadoop.

Apache Kafka, Apache Storm, Apache Fink.


High latency, as insight becomes available only after the processing of the batch finishes.

Low Latency, with insights being available instantaneously.

Stream Processing Use Cases

Stream processing is a vital segment of data management. The versatility of the approach allows for numerous applications across many industries and business operations. Besides being a robust approach to real-time analytics, stream processing also facilitates big data processing, handling IoT data, and deploying data anomaly detection.

There are several aspects where stream processing can be successfully used, such as the following:

  • Fraud detection
  • Social media monitoring
  • Real-time stock trades
  • Real-time recommendations and personalization
  • Healthcare monitoring
  • Supply chain tracking
  • Network monitoring
  • Predictive maintenance
  • Intrusion detection (in cybersecurity), etc.

Stream processing is commonly used in the sales and marketing sectors. Data teams can use stream processing to go through massive volumes of customer data and extract relevant value. That can, in turn, yield valuable insight into customer behavior, purchasing habits of the customers, and so on.

Batch Processing Use Cases

When it comes to batch processing, data teams use the approach to process heaps of data in batches. For instance, data operators can cleanse, aggregate, and transform data by batch processing.

In general, batch processing can increase processing efficacy, minimize processing costs, and enable businesses to handle massive data volumes and tasks systematically. Batch processing is especially important (and valuable) when processing data in real-time.

Some of the most notable use cases of batch processing involve the following:

  • Billing and invoicing
  • Inventory management
  • Scientific research
  • Image and video processing
  • QA and data cleansing
  • Financial transactions
  • Email campaigns
  • Risk assessment
  • Credit scoring, etc.

In the retail business, supply chain management systems and retailers rely on batch processing to analyze sales data and automate purchasing orders to replenish the inventory. Also, credit bureaus and financial institutions turn to batch processing to estimate credit scores and assess credit risks.

Batch Processing Vs. Stream Processing: Performance

Batch processing and stream processing are two different approaches catering to the same goal: processing large volumes of data. Each approach comes with its own set of strengths and weaknesses, with performance being the most important one.

In terms of performance, businesses resort to batch processing as an easily manageable and optimizable method. On the other hand, stream processing is the best choice for processing volumes of data in real-time.

The performance of each method is influenced by complexity. Batch processing is generally less complex than stream processing, mainly because data comes in batches and is processed offline. Comparably, stream processing is rather more complex because it processes data in real time, which is a challenge on its own.

Another aspect that influences the performance of each data processing method is the processing speed. Batch processing is somewhat slower because it involves processing data in batches and takes some time. On the other hand, stream processing processes data in real-time and with low latency, which makes it a suitable option for tasks that require immediate actions.

Batch Processing: Large, Complex Data Analysis

With batch processing, data is collected in batches and then fed into an analytics system. A “batch” is a group of data points collected within a given time period.

Unlike stream processing, batch processing does not immediately feed data into an analytics system, so results are not available in real-time. With batch processing, some type of storage is required to load the data, such as a database or a file system.

Batch processing is ideal for very large data sets and projects that involve deeper data analysis. The method is not as desirable for projects that involve speed or real-time results. Additionally, many legacy systems only support batch processing.

This often forces teams to use batch processing during a cloud data migration involving older mainframes and servers. In terms of performance, batch processing is also optimal when the data has already been collected.

Batch Processing Example: Each day, a retailer keeps track of overall revenue across all stores. Instead of processing every purchase in real-time, the retailer processes the batches of each store’s daily revenue totals at the end of the day.

Stream Processing: Speed and Real-Time Analytics

With stream processing, data is fed into an analytics system piece-by-piece as soon as it is generated. Instead of processing a batch of data over time, stream processing feeds each data point or “micro-batch” directly into an analytics platform. This allows teams to produce key insights in near real-time.

Stream processing is ideal for projects that require speed and nimbleness. The method is less relevant for projects with high data volumes or deep data analysis.

When coupled with platforms such as Apache Kafka, Apache Flink, Apache Storm, or Apache Samza, stream processing quickly generates key insights, so teams can make decisions quickly and efficiently. Stream processing is also primed for non-stop data sources, along with fraud detection, and other features that require near-instant reactions.

Stream Processing Example: A soda company wants to amplify brand interest after airing a commercial during a sporting event. The company feeds social media data directly into an analytics system to measure audience response and decide how to boost brand messaging in real-time.

Simple Solutions for Complex Data Pipelines

Rivery's SaaS ELT platform provides a unified solution for data pipelines, workflow orchestration, and data operations. Some of Rivery's features and capabilities:
  • Completely Automated SaaS Platform: Get setup and start connecting data in the Rivery platform in just a few minutes with little to no maintenance required.
  • 200+ Native Connectors: Instantly connect to applications, databases, file storage options, and data warehouses with our fully-managed and always up-to-date connectors, including BigQuery, Redshift, Shopify, Snowflake, Amazon S3, Firebolt, Databricks, Salesforce, MySQL, PostgreSQL, and Rest API to name just a few.
  • Python Support: Have a data source that requires custom code? With Rivery’s native Python support, you can pull data from any system, no matter how complex the need.
  • 1-Click Data Apps: With Rivery Kits, deploy complete, production-level workflow templates in minutes with data models, pipelines, transformations, table schemas, and orchestration logic already defined for you based on best practices.
  • Data Development Lifecycle Support: Separate walled-off environments for each stage of your development, from dev and staging to production, making it easier to move fast without breaking things. Get version control, API, & CLI included.
  • Solution-Led Support: Consistently rated the best support by G2, receive engineering-led assistance from Rivery to facilitate all your data needs.

How Data Streaming Works

As we mentioned, data streaming means data continuously flows from the source to the destination, where it is processed and analyzed. What was once reserved for several selected businesses today is embraced by almost every company.

Data streaming allows for real-time data processing and provides monitoring of every aspect of the business. It is becoming a very useful tool that companies can use daily.
So how does the process work? Below we break down several data streaming features.

The Data Streaming Process

Every company possesses a lot of data that needs to be analyzed and processed. This data is piped to different locations through data stream processing techniques consisting of tiny data packets. It is then processed in real or near real-time, commonly used by streaming media and real-time analytics.

Unlike other processing techniques that don’t allow quick reactions and address crisis events, data streams do just that. These differ from traditional data thanks to several crucial features.

Namely, they carry a timestamp and are time-sensitive, meaning that after a while, they become insignificant. Happening in real-time, they are continuous and, at the same time, heterogeneous. Data streams can have multiple formats because of the variety of sources from which the data originates.

Note that there is a big possibility that a stream may have damaged or missing data because of the different transmission methods and numerous sources, meaning that a data stream may arrive out of order.

The Data Streaming Hardware

When learning how data streaming works, it’s important to note some differences in the hardware. In other words, comparing batch processing vs. stream processing, we can notice that batch processing requires a standard computer specification. In contrast, stream processing demands high-end hardware and sophisticated computer architecture.

Batch processing uses most of the processing and storage resources to process large data packets. On the other hand, streaming processing reduces computational requirements and uses less storage to process a current set of data packets.

Today, data is generated from an infinite number of sources, so it’s impossible to regulate the data structure, frequency, and volume. Data stream processing applications have to process one data packet in sequential order. The generated data packet includes the timestamp and source, enabling applications to work with the data stream.

Difference Between Real-time Data Processing, Streaming Data, and Batch Processing

To fully understand how data streaming works, here is a simple distinction between these 3 methods.
Batch processing is done on a large data batch, and the latency can be in minutes, days, or hours. It requires the most storage and processing resources to process big data batches.

The latency of real-time data processing is in milliseconds and seconds, and it processes the current data packet or several of them. It requires less storage for processing recent or current data pocket sets and has fewer computational requirements.

Streaming data analyzes continuous data streams, and the latency is guaranteed in milliseconds. It requires current data packet processing; hence the processing resources must be alert to meet guarantees of real-time processing.

Batch vs. Stream Processing: What to Use?

With data processing, there is no universally superior method. Batch and stream processing each have strengths and weaknesses, depending on your project. In an effort to stay agile, companies continue to gravitate toward stream processing.

But batch processing is still widely used and will be so long as legacy systems remain an integral component of the data ecosystem.

When it comes to data processing, flexibility is the most important factor for data teams. Different projects call for different approaches. Teams must have the wherewithal to find optimal solutions for each use case.

There is no clear winner in a comparison between batch and stream processing. The winners are the teams that can work with both.

Minimize the firefighting.
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