Batch Processing vs Stream Processing: 7 Key Differences

Updated August 11th, 2023
Batch Processing vs Stream Processing

Share this article

Batch and stream processing are two common data processing paradigms. While batch processing deals with the processing of data in large, predefined chunks, stream processing involves handling data in real-time, as it comes in.

In this article, we will explore the core differences between batch processing vs stream processing, their pros and cons, and practical use cases where they can be used.

Let’s get started!

Table of contents

  1. Differences between batch processing and stream processing
  2. Batch processing vs stream processing: A tabular comparison
  3. Understanding batch processing vs stream processing in big data
  4. The pros and cons of batch processing and stream processing
  5. Practical use cases/examples
  6. Rounding it all up
  7. Related reads

7 Differences between batch processing and stream processing you need to know

While batch processing is about processing large volumes of data at scheduled intervals, stream processing is all about handling data on-the-fly, in real time, or near-real-time. The best choice depends on the specific needs of a project or business requirement.

So, let’s understand the main differences between these concepts, which are:

  1. Definition and nature of data processing
  2. Latency and processing time
  3. Use cases and applications
  4. Fault tolerance and reliability
  5. Scalability and performance
  6. Complexity and setup
  7. Examples of tools and platforms

Let us now look into each of the above differences in detail:

1. Definition and nature of data processing

  • Batch processing: This involves processing data in large chunks, or batches after it has been collected over a certain period. The data is stored, and once there’s enough, or after a certain time has passed, it’s processed all at once.
  • Stream processing: On the other hand, stream processing is designed to process data in real-time or near-real-time. As soon as the data arrives, it’s processed, which means there’s no waiting for a batch of data to accumulate.

2. Latency and processing time

  • Batch processing Typically has higher latency since data is not processed immediately. It waits for a batch to be complete or a specific schedule to trigger the processing.
  • Stream processing: Offers lower latency because data is processed immediately as it flows into the system, which makes it more suitable for real-time analytics or tasks requiring instantaneous insights.

3. Use cases and applications

  • Batch processing: Common in scenarios where immediate data processing is not essential. Examples include monthly payroll processing, end-of-day report generation, and large-scale data analytics.
  • Stream processing: Used in situations requiring immediate action based on incoming data, such as fraud detection in banking, real-time recommendations in e-commerce, or live dashboard updates.

4. Fault tolerance and reliability

  • Batch processing: Typically, if a batch processing job fails, it can be restarted from where it left off, or the entire batch can be reprocessed.
  • Stream processing: Requires more sophisticated fault tolerance mechanisms. If a data stream is interrupted, the system needs ways to handle the interruption and ensure data isn’t lost.

5. Scalability and performance

  • Batch processing: Systems are often optimized for throughput since large volumes of data are processed at once. They might be scaled vertically (more powerful machines) or horizontally (more machines) depending on the use case.
  • Stream processing: Systems need to be designed for both high throughput and low latency. They’re usually scaled horizontally to handle varying data velocities.

6. Complexity and setup

  • Batch processing: Might have a simpler setup and design since it doesn’t always need to account for real-time processing complexities.
  • Stream processing: Often requires a more complex setup, especially when ensuring fault tolerance, managing state, and dealing with out-of-order data events.

7. Examples of tools and platforms

  • Batch processing: Hadoop MapReduce, Apache Hive, and Apache Spark’s batch processing capabilities are popular examples.
  • Stream processing: Examples include Apache Kafka Streams, Apache Flink, and Apache Storm.

Batch processing vs stream processing: A tabular comparison

Now, let us quickly look at a tabular comparison between batch processing and stream processing for better context:

CriteriaBatch ProcessingStream Processing
Nature of DataProcessed in chunks or batches.Processed continuously, one event at a time.
LatencyHigh latency: insights are obtained after the entire batch is processed.Low latency: insights are available almost immediately or in near-real-time.
Processing TimeScheduled (e.g., daily, weekly).Continuous.
Infrastructure NeedsSignificant resources might be required but can be provisioned less frequently.Requires systems to be always on and resilient.
ThroughputHigh: can handle vast amounts of data at once.Varies: optimized for real-time but might handle less data volume at a given time.
ComplexityRelatively simpler as it deals with finite data chunks.More complex due to continuous data flow and potential order or consistency issues.
Ideal Use CasesData backups, ETL jobs, monthly reports.Real-time analytics, fraud detection, live dashboards.
Error HandlingDetected after processing the batch; might need to re-process data.Needs immediate error-handling mechanisms; might also involve later corrections.
Consistency & CompletenessData is typically complete and consistent when processed.Potential for out-of-order data or missing data points.
Tools & TechnologiesHadoop, Apache Hive, batch-oriented Apache Spark.Apache Kafka, Apache Flink, Apache Storm.

Understanding batch processing vs stream processing in big data

When we specifically reference ”batch processing vs. stream processing in big data,” we’re emphasizing the techniques’ relevance to vast amounts of data — their collection, analysis, and processing.

In the realm of big data, choosing between batch and stream processing depends on the nature of the insights required, the characteristics of the data, and the specific business or technical objectives.

Let us understand the difference between these two concepts:

  1. Nature of big data processing
  2. Data continuity and flow
  3. Infrastructure and resource demands
  4. Data consistency and completeness
  5. Real-time analytics vs. deep analytics
  6. Big data tools and ecosystems
  7. Integration with other big data technologies

Now, let us understand each of the above points in detail:

1. Nature of big data processing

  • Batch Processing: In the context of big data, batch processing means accumulating huge volumes of data over a period and processing them all at once. This method is particularly effective when the overall dataset is massive and requires significant computation.
  • Stream Processing: Within big data, stream processing is all about ingesting, processing, and analyzing data in real-time or near-real-time, even as the dataset grows at an immense scale.

2. Data continuity and flow

  • Batch Processing: Data is segmented into specific blocks or chunks, and each batch is processed sequentially. There’s often a start and end to each batch.
  • Stream Processing: Data is continuous and unbounded. Processing involves handling infinite data streams, with no predefined start or end.

3. Infrastructure and resource demands

  • Batch Processing: Due to the bulk nature of data processing, substantial resources might be required, but these can be provisioned less frequently.
  • Stream Processing: Resources are spread out over time, but systems must be designed for constant availability and resilience to ensure real-time processing.

4. Data consistency and completeness

  • Batch Processing: Since data is processed in chunks after collection, it’s often complete and consistent, reducing the chances of missing data.
  • Stream Processing: As data is processed in real-time, there’s a chance for out-of-order data or potential gaps, requiring mechanisms to handle such inconsistencies.

5. Real-time analytics vs deep analytics

  • Batch Processing: Better suited for deep analytics, complex algorithms, and heavy computations where insights don’t need to be immediate.
  • Stream Processing: Geared towards real-time analytics, where quick decisions or immediate insights are crucial, albeit potentially at the cost of depth or complexity.

6. Big data tools and ecosystems

  • Batch Processing: Tools like Hadoop MapReduce, Apache Hive, and batch-oriented Apache Spark have been foundational in big data batch processing.
  • Stream Processing: Modern big data ecosystems include tools like Apache Kafka, Apache Flink, and Apache Storm, designed specifically for real-time data streaming and processing.

7. Integration with other big data technologies

  • Batch Processing: Often integrated with data lakes, HDFS (Hadoop Distributed File System), and other big data storage solutions.
  • Stream Processing: Typically works in tandem with message brokers (like Apache Kafka) and can feed processed data into real-time dashboards, alerting systems, or even other big data storage solutions.

In the next section, we will analyze the advantages and disadvantages of both batch processing and stream processing.

The pros and cons of batch processing and stream processing

Batch and stream processing are two distinct paradigms for data processing. Each comes with its unique strengths and challenges, making them suitable for different scenarios. In this section, we will understand the pros and cons of each concept so you can decide and adapt based on your specific requirements.

Batch processing pros

  1. Simplified data processing: Since data is processed in chunks, there’s typically a clearer start and end point, making the flow easier to manage and understand.
  2. High throughput: Batch processing can handle vast amounts of data at once, ensuring high throughput rates.
  3. Optimal for deep analysis: It’s ideal for deep and complex data analytics, where immediate insights are not necessary.
  4. Resource efficiency: By aggregating tasks, resources like CPU and memory can be efficiently utilized during processing intervals.
  5. Mature technology and tools: Many well-established tools, like Hadoop and Apache Hive, support batch processing, offering mature features and extensive documentation.

Batch processing cons

  1. Delayed insights: Due to its non-immediate nature, insights are only available after the entire batch has been processed.
  2. Potentially resource-intensive: Large datasets can demand significant computational resources, leading to potential bottlenecks.
  3. Inflexible once started: Modifying or stopping a batch process midway can be challenging, making it less adaptable to changing conditions.
  4. Complex error handling: Errors may only be discovered after processing a large batch, necessitating re-processing.
  5. Scalability challenges: Scaling vertically (adding more power to existing machines) can become expensive and have limits.

Now, let us learn the pros and cons of stream processing.

Stream processing pros

  1. Real-time insights: Provides immediate feedback and insights, allowing for quicker decision-making.
  2. Flexible and adaptable: Easier to modify, stop, or scale up and down based on changing data inflow or requirements.
  3. Continuous data flow: Ideal for applications requiring continuous monitoring and alerting.
  4. Suits modern data-driven applications: Great for use cases like fraud detection, live dashboards, and real-time recommendations.
  5. Horizontal scalability: Can be scaled out by simply adding more machines, making it suitable for growing datasets.

Stream processing cons

  1. Complex infrastructure: Setting up a real-time stream processing solution might require intricate infrastructure planning and management.
  2. Potential consistency challenges: Handling out-of-order data or missed data points can introduce consistency issues.
  3. Requires sophisticated fault-tolerance: Systems must be designed to handle interruptions and ensure data isn’t lost.
  4. Can be resource-intensive over time: Since it runs continuously, resource demands can accumulate, potentially leading to higher costs.
  5. Potential data order issues: Handling data in the correct order becomes crucial, especially in scenarios where sequence matters.

So, while batch processing is optimized for structured, high-throughput tasks on stable datasets, stream processing thrives in dynamic, real-time scenarios. The choice between them should be based on the specific needs and constraints of a given application or system.

Practical use cases/examples for batch processing and stream processing: Where do you use them?

In this section, let us look at a few practical examples of where batch processing and stream processing are applicable”

Batch processing use cases

Use case 1: Financial statement generation

Many companies generate monthly or quarterly financial statements, summarizing transactions, expenses, and revenues. Due to the vast amount of data involved, these statements are not generated in real time but instead are produced using batch processing. At the end of the month or quarter, all the financial data accumulated during the period is processed in a single batch to generate these reports.

Use case 2: Daily backup of data

A common practice in IT is to back up data at regular intervals, like daily or weekly. Given the potentially massive size of the data, backing up in real-time might be inefficient. Instead, a batch process runs during off-peak hours, collecting and saving changes made during the day.

Use case 3: ETL processes in data warehouses

Extract, Transform, Load (ETL) processes are used to take data from source systems, transform it into a consistent format, and load it into a data warehouse. Given the volume of data and the potential complexity of transformations, this process is typically run in batches, often nightly or weekly.

Stream processing use cases

Use case 1: Real-time fraud detection

Financial institutions and credit card companies use stream processing to detect fraudulent activities. As transactions happen in real-time, systems instantly analyze patterns, behaviors, and known fraud markers. If a transaction seems suspicious (like a sudden high-value purchase in a foreign country), the system can flag it immediately, potentially stopping the transaction or alerting the cardholder.

Use case 2: Social media sentiment analysis

Brands monitor social media platforms to understand public sentiment about their products or services. Using stream processing, they can analyze tweets, status updates, or comments in real time, picking up on trends, feedback, or potential PR crises. For instance, if a new product launch is met with negative feedback, brands can pick up on this immediately and react accordingly.

Use Case 3: Real-time Analytics Dashboards

In industries where real-time data is vital, such as stock trading platforms or e-commerce sites during big sales, analytics dashboards update in real-time using stream processing. These dashboards show data like active users, current sales, stock prices, or any other metric that needs immediate updates. This allows decision-makers to act quickly, making decisions based on the latest data.

In summary, batch processing is utilized in scenarios where data accumulates over a period and doesn’t require immediate action, while stream processing shines in contexts demanding instant insights and actions based on live data streams. Both processing paradigms are essential, with their significance determined by the specific needs of the task at hand.

Rounding it all up

Batch processing and stream processing are two distinct paradigms in data management. Batch processing involves handling data in predetermined chunks, providing high throughput, and being ideal for tasks like ETL jobs and data backups. It usually has higher latency, and insights come post-processing.

On the other hand, stream processing manages data continuously and in real time, making it perfect for live analytics and monitoring. It requires resilient systems due to its always-on nature.

Both methods have unique challenges: batch can be resource-heavy and less flexible, while stream can face consistency issues. The choice between them hinges on the specific needs of the data task in question.

Share this article

[Website env: production]