Batch Processing vs Stream Processing: Key Differences

Large amounts of data are created every second, and you need to find efficient ways to manage it. The two primary approaches are batch processing and stream processing. While both have unique benefits of handling data, they differ in how and when data is processed.

In this article, you will learn the key differences between batch processing vs stream processing, helping you determine the best method for your data needs.

What Is Batch Processing?

Batch processing allows you to collect data over a specific period, typically hours, days, or weeks, and process it in bulk. Instead of handling each transaction separately as it arrives, this approach allows you to group large datasets into batches and manage them at scheduled intervals.

Using batch processing, you can streamline tasks requiring complex computation and in-depth analysis. By automating these repetitive tasks, it assists you in reducing manual effort and system load. While the batch approach lacks real-time responsiveness, it facilitates managing vast data volumes with high accuracy, making it ideal for structured data processing.

How Does It Work?

1. Data Collection: Gather large datasets from diverse sources such as databases, logs, sensors, or transactions and store them in a staging system.
2. ‍Grouping: Determine how and when to process the data by grouping related tasks or jobs into batches.
3. ‍Scheduling: You can schedule these batches to run at defined times, such as overnight or during low-traffic hours, to save resources.
4. ‍Processing: Depending on the system’s capacity, batches are processed one by one or in parallel.
5. ‍Results: Once processed, results are stored in databases or a data warehouse. You can also integrate them with dashboards or business intelligence (BI) tools for further analysis and reporting.

Pros

• Batch processing allows you to improve data quality by validating data once it is in the staging area. Here, you can apply validation rules to remove duplicates or missing values. These basic data cleaning steps help you ensure high-quality data before batch execution.
• In batch processing, you can schedule tasks to run in the background or during off-peak hours. This offline feature enables you to prevent disruptions to real-time activities.

Cons

• Batch processing is not efficient for small data changes. If only a few records need modifications, the whole batch must still be handled. This leads to unnecessary resource usage.
• Once a batch job starts, applying changes or stopping it due to an error is difficult. During execution, it is not possible to modify processing rules, access partial results, or manage unexpected issues. This lack of flexibility makes batch processing less adaptable to quick updates or errors.

What Is Stream Processing?

Stream processing refers to the processing of data in real-time as it is created. Compared to batch processing, stream processing aids you in managing data continuously. This ensures quick insights and faster decision-making.

You can utilize the streaming approach for applications that need instant data updates, such as real-time analytics, stock marketing, and live recommendation systems. Since stream processing works on a constant flow of data, it depends on low-latency systems to handle high-speed data streams. However, the real-time aspect can range from milliseconds to minutes, based on the application’s requirements.

How Does It Work?

1. Input Stream: Data is continuously produced from multiple sources, such as sensors, logs, and social media feeds. These data streams flow directly into a stream processing engine.‍
2. Stream Processing: As soon as the data arrives, it is processed in real-time for immediate insights and actions.
3. ‍Output Stream: Once processed, the data is sent to a destination such as a database, data warehouse, or analytical system.

Pros

• Stream processing applications need to maintain context by tracking user activity or monitoring trends over time. In such cases, the state store in the stream processing framework helps save a temporary record of past data. This stateful processing gives you better real-time decision-making.
• With the event-driven architecture of the stream processing system, events are processed as they occur. Rather than waiting for scheduled jobs, the streaming system continuously processes the data and ensures low-latency responses.

Cons

• Adopting a stream processing approach needs expertise in real-time distributed systems and event-driven frameworks. Without a strong understanding, your team may struggle to manage data consistency, scale workloads, and optimize low-latency processing.
• Since the streaming system allows you to handle data in real-time, it demands continuous computational resources. This leads to higher CPU and memory use than batch processing.

Batch Processing vs Stream Processing: Key Differences

Apart from these basic batch vs stream processing differences, here are some of the crucial factors to consider:

Data Ingestion Architecture

Batch processing follows a scheduled data ingestion approach to help you gather and handle high volumes of data at specific intervals. This approach is commonly supported by solutions like Airbyte or AWS Batch, which help automate batch ingestion and storage.

In contrast, the stream processing data ingestion architecture includes real-time event queues and message brokers. The former are buffers that enable you to temporarily store incoming data or events before processing. The latter are the systems that facilitate communication between producers and consumers. Both components ensure a smooth data flow with low latency. It relies on real-time event-driven architectures using tools such as Apache Flink or Spark Streaming.

Fault Tolerance

Batch processing achieves fault tolerance through retries and re-running failed batches. If an error occurs, you can restart the batch job from the last saved state without data loss.

Conversely, stream processing needs more advanced fault tolerance approaches, as failures must be handled instantly. However, attaining a high fault tolerance while maintaining low latency is difficult. In turn, you should adopt sophisticated techniques such as distributed checkpointing and event reprocessing. This ensures smooth recovery without affecting real-time system performance.

Throughput Characteristics

Batch processing offers high throughput since data is managed in large volumes at once. This approach lets you optimize resource usage but introduces some processing lags.

In contrast, real-time data processing needs handling data in smaller chunks. This makes it more complex to maintain high throughput while ensuring reduced latency. Efficient resource management and parallelism are crucial to sustain performance.

Cost

In batch processing, you can control system resource use and reduce costs by handling large amounts of data in bulk. This also helps you run batch jobs outside business hours to save expenses.

In contrast, running a real-time processing system needs continuous computing and network infrastructure. Consequently, maintaining a streaming system is more expensive due to ongoing resource consumption.

When to Use Batch Data Processing?

• Payroll and Invoicing: Batch processing is ideal for handling monthly payroll, including tax deductions, and generating bulk invoices at the end of each pay cycle.
• ‍System Backups: You can schedule routine backups for databases, applications, and system logs at predefined intervals. This reduces data loss and ensures data integrity for disaster recovery.

When to Use Stream Data Processing?

• Fraud Detection: Banks and payment systems use stream processing to check transactions instantly. This helps detect unauthorized card use or strange banking activity, allowing immediate action to prevent financial loss.
• ‍Online Gaming: Multiplayer games rely on stream processing to sync player actions and update game progress in real-time. This ensures a smooth, responsive gaming experience where all players see consistent game states without delay.

Stream Processing Tools

Let’s check out some of the powerful streaming tools:

Apache Kafka

Apache Kafka is a distributed event streaming tool. It was originally developed by LinkedIn and later open-sourced under Apache Software Foundation. Using Kafka, you can build high-performance, real-time pipelines for data integration and analytics tasks.

Apache Kafka follows a publisher (producer) and subscriber (consumer) model. In this model, producers publish data on topics, and consumers subscribe to these topics to use the data. You can then split the topics into partitions to enhance fault tolerance. These partitions allow you to spread data between diverse servers (brokers) in a Kafka cluster. If a broker fails, one of the partitions takes over as the primary server to ensure data availability.

Google Cloud Dataflow

Google Cloud Dataflow is a fully managed data streaming service offered by Google Cloud. It helps you build scalable data ingestion and transformation pipelines. DataFlow is built on Apache Beam, an open-source tool that allows you to create pipelines using Java, Python, or Go SDKs.

Without rewriting your code, you can run your workflows on other streaming platforms, such as Spark or Flink. As a result, you can ensure portability between different platforms.

Amazon Kinesis

Amazon Kinesis is a data stream processing tool offered by AWS. It allows you to continuously ingest and analyze streaming data at scale. This capability makes it ideal for real-time analytics, application monitoring, and event-driven applications.

With Kinesis, you can collect data from sources like IoT devices, application logs, and social media feeds and process it with low latency. Once processed, you can integrate it with other AWS services like Lambda, S3, CloudWatch, and Redshift. This integration can help you automate tasks such as real-time event processing, storage, monitoring, and analytics, streamlining end-to-end data pipelines.

Batch Processing Tools

To handle large datasets in batches, consider using the following tools:

Airbyte

Airbyte is a data movement tool that follows a batch processing approach to move data from one source to a target system. With over 550 pre-built connectors, you can simplify the data integration across different platforms.

If a suitable connector is not available for your needs, you can create custom ones through Airbyte’s Connector Builder, low-code CDKs, or language-specific CDKs. The Connector Builder features an AI Assistant that auto-fills the required configuration fields during connector development. It also provides intelligent suggestions for fine-tuning the configuration process.

While configuring connection settings, Airbyte offers various sync modes to read and write data between source and destination. These are Incremental Append, Incremental Append + Deduped, Full Refresh Append/Overwrite, Full Refresh Overwrite + Deduped, and Resumable Full Refresh.

Further, Airbyte provides flexible synchronization scheduling options for each connection—Scheduled, Cron, and Manual. Scheduled syncs enable you to set intervals ranging from every 1 hour to every 24 hours to replicate data in batches. Cron syncs offer precise control using custom expressions for specific timing. Manual Syncs require you to start them through the UI or API.

AWS Batch

AWS(Amazon Web Services) Batch, a fully managed batch processing solution, enables you to run workloads of any scale efficiently. It simplifies batch computing by dynamically provisioning the computing resources based on the size and requirements of the submitted batch jobs. This eliminates the need to install and manage batch processing system infrastructure. Thus, you can focus more on analyzing results rather than managing infrastructure.

Further, AWS Batch facilitates integration with a broad ecosystem of other AWS services. For example, you can integrate Batch with AWS Lambda for serverless computing and Amazon CloudWatch for monitoring and logging. This helps streamline the creation of complex workflows for batch-processing tasks.

Azure Batch

Azure Batch is a cloud-based platform that empowers you to run large-scale, high-performance computing (HPC) batch jobs. It lets you automate the creation and management of a pool of compute nodes (virtual machines) and schedules jobs to run on these nodes. This eliminates the need for you to manage clusters, significantly simplifying the process.

The platform is built on a high-scale job scheduling engine that’s available to you as a managed service. You don’t need to create your work queue, dispatcher, or monitor. Batch provides this as a service. Further, Azure Batch processes jobs on demand rather than on a predefined schedule, making it ideal for scenarios where data processing needs vary.

Hybrid Processing Approaches

By combining batch and stream processing, you can handle large datasets while ensuring real-time responsiveness. Utilizing the strengths of both methods, you can develop batch and streaming workflows within a single system.

For instance, an e-commerce platform can use stream processing to provide instant order updates, personalize recommendations, and detect fraud in real time. On the other hand, with batch processing, the platform can handle tasks like inventory reconciliation and customer analytics.

How Does Airbyte Help with Data Processing?

Here are a few Airbyte features that help you with data processing: 

• Streamline GenAI Workflows: Airbyte supports RAG-specific operations like automated chunking, embedding, and indexing. You can transform raw data and store the embeddings in vector databases like Pinecone and Chroma. This facilitates context-based retrieval and enhances the relevance of the outputs generated by LLM.‍
• Schema Management: You can specify how Airbyte should handle any change of schema in the source. Once configured, Airbyte performs schema checks for Cloud users every 15 minutes and every 24 hours for Self-managed users. These regular checks ensure that your data remains accurate and up-to-date.‍
• PyAirbyte: Airbyte offers an open-source Python library, PyAirbyte, to help you build data pipelines directly within your Python environment. With PyAirbyte, you can extract data from diverse sources using Airbyte connectors and load it into SQL caches like Snowflake and DuckDB. You can then apply custom transformations on cached data with SQL tools or Pandas libraries and move the data to your target destination.‍
• Custom Transformations: Airbyte and dbt Cloud integration allows you to create and run custom transformations immediately after the initial sync. This enables you to turn your data into a format that is suitable for further processing.‍
• Data Orchestration: You can integrate Airbyte with data orchestration tools, such as Prefect, Dagster, and Apache Airflow. This greatly helps in automating your complex data workflows.

Conclusion

In this guide, you have learned the differences between batch processing vs stream processing in simpler terms. Batch processing is well-suited for handling large volumes of data at scheduled intervals, while stream processing is useful for achieving timely insights. By understanding these approaches and how they vary, you can choose the right data processing method based on your organizational needs.