Every organizational activity or interaction today generates data. This quickly creates large amounts of data at organizational and departmental levels, but data generation is only the beginning. No matter how much raw data you have at your disposal, you can only leverage it fully if you know how to process it correctly for your requirements.

You can process data flows using one of two approaches: batch processing or batch processing. Over the last few years, there’s been a considerable shift toward stream processing. But the right approach ultimately depends on your data types, volumes, applications, and data processing objectives. 

Here’s an in-depth batch processing vs. stream processing comparison to help you make an informed decision.

What is Batch Processing? 

The batch processing technique collects, processes, and stores data in preconfigured batches or chunks. Data collection is a distinguishing factor here since batch processing doesn’t occur continuously. Instead, it happens when all the data is collected at predefined intervals or according to preset data quantities. This characteristic makes batch processing ideal whenever processing data in real-time isn’t a priority.

Batch processing is optimized for efficiently handling large data volumes, making it suitable for big data applications. Batch processes are generally scheduled during off-peak hours or outside of standard work hours to avoid straining system resources and to minimize disruptions to daily operations. 

Micro-batch processing is a variant of batch processing that processes very small batches of data much more frequently — for instance, every hour or every few minutes. 

How it Works 

Batch processing comprises the following stages: 

1. Data Collection

The first part of the process is data collection, which can take considerable time as data is collected over time from various internal and external sources.

These sources vary by business model. For example, an influencer marketing agency will focus more on its social media activity to identify areas of improvement, and a manufacturing company will collect sensor data to assess machine performance during a period. 

In the interim between collection and processing, the collected data is temporarily stored in a data warehouse or another staging area. If necessary, it’ll undergo pre-processing or cleaning to ensure that it’s in the appropriate format and error-free. 

2. Job Scheduling

Configuring batch jobs allows data processing tools to process the collected data according to conditions you specify. You can set up these batch jobs to run at a particular time of the day. Alternatively, you can schedule batch jobs at predetermined intervals — nightly, weekly, monthly, or even further apart.

You can schedule jobs to run in parallel or sequentially. For example, it’d be logical for payroll processing to begin once the timesheet data aggregation is completed since the former won’t be accurate without the latter. Such a combination would require sequential execution.

3. Data Processing

Once executed, the batch job processes the collected data in bulk or its entirety. Data processing includes data manipulation by running predefined queries, programs, or scripts. Operations such as data transformations, validation, and sorting are also part of the process.

Because this approach processes a large volume of data, it needs to operationalize high-performance computing resources. Batch processing leverages multiple processors or servers to handle the workload when there’s a more significant data set. 

4. Output Generation

Data processing results are generated based on your requirements. For example, you can create detailed reports for review, update a centralized repository with the processed data to create a Single Source of Truth (SSoT), or generate files to perform further analysis.

You can also share the output results with various stakeholders. Upper management, for instance, will be interested in reviewing financial reports to understand the business’s financial position. 

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What is Stream Processing? 

Stream processing, also known as real-time processing, continuously processes data as it’s received or generated. Unlike batch processing, there’s no concept of storing data before it’s processed, which makes this technique ideal for obtaining real-time results or processing time-sensitive data streams.

Its low latency and continuous operation characterize stream processing. It’s commonly used in applications that require data to be processed in real-time for immediate analysis, such as financial trading platforms. 

Real-time processing is also necessary for applications that must assess and respond to events as they happen, such as fraud detection systems, network security monitoring, or Internet of Things (IoT) devices and systems. 

How it Works 

Stream processing comprises the following stages: 

1. Data Ingestion

In the first stage, data is ingested from different sources, such as sensors, APIs, databases, and logs. This data is collected continuously and in real-time. It often needs immediate cleaning or pre-processing to remove errors and fix its formatting before it enters the processing pipeline.

2. Stream Processing Engine

Following ingestion and cleaning, dedicated processing engines or frameworks process data streams. At this stage, the engine also performs different operations on the data streams, including filtering, transforming, aggregating, and enriching.

These engines can scale horizontally and engage multiple nodes for effective data stream processing. 

3. Real-Time Analysis

The processed data is analyzed instantaneously to derive immediate insights. This minimizes the gap between data generation and leveraging it for decision-making.

You can configure the data analytics system to trigger specific actions in response to these insights. It can generate alerts, start an automated workflow, or update a dashboard.

4. Output and Storage

Suppose real-time or near-real-time data analysis isn’t needed. In that case, you can store the processed data in a database, data lake, or another repository for further analysis or future reference and review.

You can integrate the processed data with business intelligence tools like Microsoft Power BI for more comprehensive real-time analytics and reporting. 

Batch Processing vs. Stream Processing: Key Differences 

Here’s a closer look at batch processing vs. stream processing across different areas: 

1. Data Ingestion 

Batch processing collects data and processes it in large chunks.  Whereas in stream processing, data is processed in real time as received.

2. Processing Time 

Batch processing typically requires longer processing times since it handles large data volumes. Stream processing emphasizes real-time operations and doesn’t let data accumulate, leading to faster processing.

3. Latency 

Accounting for delays is part of batch processing since data is only processed according to the intervals you define. In contrast, no intervals are needed for stream processing, so it delivers results quickly with low latency.

4. Speed 

Batch processing operations deprioritize speed in favor of efficiently handling high-throughput operations, whereas stream processing emphasizes speed in ingesting data, processing it, and delivering results constantly.

5. Complexity 

Batch processing systems are relatively easier to set up and manage. You won’t need to change the processing intervals and other operational conditions you set up too often. On the other hand, stream processing can be more complicated since it involves continuous operations and real-time analytics.

6. Use Cases 

Batch processing works well whenever results or insights aren’t urgently needed or if you’re working with legacy systems that can’t deliver data streams. In contrast, stream processing is appropriate for use cases needing real-time actions and insights, such as social media feeds, stock trades, and ride-sharing apps.

Batch Processing vs. Stream Processing in the Context of Big Data 

Both batch processing and stream processing have their uses in the context of big data, as discussed below: 

Batch Processing in Big Data 

Batch processing is the primary method to carry out big data ETL (extract, transform, load) processes. Since batch processing processes and analyzes large quantities of data accumulated over time, it aids in comprehensive reporting and data warehousing tasks. 

Stream Processing in Big Data 

Stream processing offers real-time insights into data, making it useful for big data applications that require real-time analytics, monitoring, and responses to live events. For example, stream processing can analyze social media activity or data from IoT device sensors to find trends and anomalies.

How to Turn Batch Data into Streaming Data 

You can turn batch data into streaming data by changing how you process and analyze data, using the following steps:

1. Data Transformation 

You can use dedicated tools or frameworks that convert batch processes into their streaming counterparts. Note that this can require rearchitecting your data pipelines to ensure they can handle real-time data streams.

2. Event-Driven Architecture 

You can implement an event-driven architecture that allows data changes to trigger real-time processing events through messaging systems or event-streaming platforms.

3. Integration with Batch Systems 

You can implement a hybrid approach by integrating streaming data with existing batch-processing systems. This approach allows you to use batch processing for historical data while using streaming for real-time analysis.

Batch Processing vs. Stream Processing: Which One is Better? 

When it comes to batch processing vs. stream processing, there’s no objectively better option. Both are viable and highly useful approaches — each with its strengths and weaknesses — and ‘better’ is more a question of which is more appropriate for your data processing requirements.  An in-depth understanding of both techniques can help you decide whether batch or stream processing is suitable for you.

Astera allows you to create fully automated pipelines effortlessly, integrate data from diverse sources, verify its quality and clean it as needed, and use built-in connectors to move it to various on-prem and cloud destinations. 

Using Astera, you can efficiently work with batch, micro-batch, or near-real-time processing. Start your free 14-day trial, or contact us for more information.