Streaming vs Batch Data processing

Batch and Streaming: two vital data processing approaches in today's digitized world. Our article delves into the intricate differences, comparing the mass data management of batching to real-time processing in streaming. Discover their unique pros, cons, and optimal usage scenarios, enabling strategic decision-making in your technology endeavours.


Jatin Solanki

Updated on

January 10, 2024


Data processing is like sorting through a big pile of information to find what we need. It helps us make sense of all the data that is generated every single day. Did you know that more data is created in just one minute today than what was created in an entire year a long time ago? Yes, It is that huge now!

Some interesting statistics about data processing:

  • As per reports, 2.5 quintillion bytes of data are created every day! 
  • A study by Forbes found that businesses that use data analytics are 5 times more likely to make faster and better decisions.
  • By 2025 the global data sphere will reach 175 zettabytes as per predictions. That's a mind-boggling amount of data!

So, in this blog, we will explore two methods of data processing: batch processing and stream processing. Just like sorting Legos before building something amazing, batch processing helps us handle large amounts of data at once. On the other hand, stream processing allows us to work with data as it arrives, in real-time. Both approaches have their benefits and uses.

Let's get started!

Batch Processing Explained:

Batch processing is a way of handling a large amount of data simultaneously, just like when you have a huge pile of Legos to sort through. Let's explore batch processing with some examples.

  1. Payroll Processing: When a company needs to calculate salaries for its employees, it often processes payroll in batches. Instead of calculating each employee's salary individually, the company collects all the necessary data, such as hours worked and deductions, and runs a batch process to calculate the salaries for all employees at once. This allows for efficient and consistent payroll processing.
  2. Inventory Replenishment: When it comes to replenishing stock, batch processing is often used. Instead of monitoring and restocking items individually, businesses gather data on product sales and inventory levels over a specific period. Based on this data, they run a batch process to determine which items need to be restocked and in what quantities.
  3. Data Analytics and Reporting: Analyzing large volumes of data for insights and generating reports is a common use case for batch processing. Organizations collect data from various sources, such as customer interactions, sales, or website activity, and process it in batches to extract meaningful information. This allows them to identify trends, make informed decisions, and generate reports for management or stakeholders.
  4. Data Backup and Synchronization: Regular data backup and synchronization are essential to ensure data integrity and disaster recovery. Many backup systems use batch processing to back up data at specified intervals. Rather than continuously backing up data in real time, the system collects changes and updates made within a defined timeframe and performs the backup process as a batch operation.

In these examples, batch processing allows us to handle a large volume of tasks or data systematically and efficiently. It's like doing things in groups, which can be much faster and more organized than doing them one by one.

Batch processing is beneficial for tasks that don't require immediate results or real-time decision-making. It's commonly used in situations such as analyzing large sets of historical data, generating reports, running scheduled backups, or processing transactions in banking systems. By processing data in batches, we can save time, optimize resources, and perform complex calculations on large datasets.

Stream Processing Explained:

Stream processing is like building with Legos as they come in, one at a time. Instead of waiting for all the Legos to be sorted before starting to build, you start building right away. Let's dive into stream processing with some examples.

  1. Monitoring Traffic Flow: Think about a city with multiple traffic signals. Instead of controlling all the traffic signals based on a fixed schedule, the traffic management system uses real-time data from sensors and cameras to adjust the signal timings dynamically. It processes the traffic flow in a streaming fashion, constantly analyzing the incoming data to optimize the signal timings and respond to changes in traffic patterns.
  2. Fraud Detection: In the banking and financial industry, stream processing is crucial for real-time fraud detection. By analyzing incoming transactions in real time, banks can detect suspicious patterns, identify fraudulent activities, and take immediate actions to prevent potential losses. Stream processing enables the quick analysis and response needed to protect customers and minimize financial risks.
  3. Social Media Sentiment Analysis: With the vast amount of data generated on social media platforms, stream processing is used to analyze and understand the public sentiment in real-time. By processing incoming tweets, posts, and comments as they are posted, organizations can gauge public opinion, track trends, and respond promptly to customer feedback or emerging issues. This allows businesses to adapt their strategies and engage with their audience effectively.

In these examples, stream processing allows us to work with data as it arrives, in real-time. It enables us to make quick decisions and take immediate actions based on the incoming information. Stream processing is valuable when we need to respond promptly to changing conditions or detect patterns in fast-moving data streams.

Stream processing finds applications in various domains, such as detecting anomalies in financial transactions, monitoring social media feeds for trending topics, analyzing sensor data in industrial settings, or processing real-time stock market data. By leveraging stream processing, we can gain insights, detect patterns, and make timely decisions based on the continuously flowing data.

Key Differences Between Batch and Stream Processing:

Unlocking the power of data processing requires understanding the key differences between batch and stream processing. Let us discover how batch processing unleashes insights from accumulated data, while stream processing empowers real-time analysis for immediate decision-making.

Batch Processing:

  1. Handling Data: Batch processing involves processing a large amount of data all at once, in chunks or batches. 
  2. Time Sensitivity: Batch processing is not time-sensitive and typically operates on data that is not time-critical. It focuses on analyzing historical or accumulated data.
  3. Processing Flow: In batch processing, data is collected, stored, and processed offline or in scheduled intervals. The processing occurs in a sequential and orderly manner.
  4. Resource Allocation: Batch processing allows for efficient utilization of computing resources as the tasks can be scheduled during periods of low system activity.
  5. Complex Calculations: Batch processing is suitable for performing complex calculations, generating reports, or running analytical algorithms on large datasets.

Stream Processing:

  1. Handling Data: Stream processing involves handling data as it arrives in real-time, processing each data item individually or in small groups, often referred to as data streams.
  2. Time Sensitivity: Stream processing is time-sensitive and focuses on the immediate processing and analysis of data as it flows. It aims to provide real-time insights and make decisions based on up-to-date information.
  3. Processing Flow: In stream processing, data is processed continuously and incrementally as it becomes available. It enables real-time monitoring, analysis, and response to changing data.
  4. Resource Allocation: Stream processing requires dedicated computing resources capable of handling high-speed data streams in real time. It often involves parallel processing and distributed systems to ensure timely data processing.
  5. Rapid Response: Stream processing enables quick response and action based on incoming data, allowing for immediate detection of patterns, anomalies, or events as they occur.

Choosing the Right Processing Approach:

Selecting the appropriate processing approach depends on data characteristics, time sensitivity, scalability requirements, and specific use cases. Evaluating these factors enables organizations to determine whether batch processing, stream processing, or a hybrid approach is best suited for their data processing needs. So let's start!

  1. Data Characteristics: Consider the nature and characteristics of your data. If you are dealing with large volumes of accumulated or historical data that don't require immediate analysis, batch processing may be suitable. On the other hand, if you have time-sensitive data streams that require real-time monitoring and rapid response, stream processing is a better choice.
  2. Use Case and Objectives: Identify the specific use case and objectives of your data processing. Batch processing is often used for tasks like generating reports, performing complex calculations, or analyzing historical data trends. Stream processing is beneficial for real-time analytics, fraud detection, monitoring systems, or responding to dynamic data events.
  3. Time Sensitivity: Assess the time-sensitivity of your data and processing requirements. If you need immediate insights or real-time decision-making, stream processing provides the advantage of processing data as it arrives. However, if time is not a critical factor and you can afford a delay in processing, batch processing may be sufficient.
  4. Scalability and Resource Considerations: Evaluate the scalability and resource requirements of your processing approach. Stream processing demands robust computing resources capable of handling high-speed data streams. Batch processing, on the other hand, can be scheduled during off-peak hours or when system resources are available.
  5. Hybrid Approaches: In some cases, a hybrid approach that combines both batch and stream processing may be appropriate. This allows you to leverage the benefits of both systems based on specific processing needs. For example, you can perform real-time monitoring and analysis using stream processing, while periodically running batch processes for in-depth analysis of historical data.

Ultimately, choosing the right processing approach depends on the characteristics of your data, the time-sensitivity of your analysis, and the specific objectives of your data processing task. Assessing these factors will help you determine whether batch processing, stream processing, or a combination of both is the most suitable approach for your needs.

Table of Contents

Read other blog articles

Grow with our latest insights

Sneak peek from the data world.

Thank you! Your submission has been received!
Talk to a designer

All in one place

Comprehensive and centralized solution for data governance, and observability.

decube all in one image