An Introduction to Batch Processing

Much of our data today arrives in a continuous stream, with new data points being generated at a rapid pace. However, there are still many situations where we need to process large amounts of data all at once. This is where batch processing comes into play.

Let's look at batch processing in depth, in this article.

What is Batch Processing?

Batch processing is a computational technique in which a collection of data is amassed and then processed in a single operation, often without the need for real-time interaction. This approach is particularly effective for handling large volumes of data, where tasks can be executed as a group during off-peak hours to optimize system resources and throughput.

Traditionally used for transaction processing in banking and billing systems, batch processing has evolved to serve diverse applications from ETL (extract, transform, load) operations to complex analytical computations.

How batch processing works

Batch processing operates on collected data groups, often scheduled, which are processed in one sequence without user intervention.

Data processed in batches minimizes system idle times, ensuring efficient use of computing resources, unlike the more computationally intensive stream processing approach. It also applies predefined operations to each batch — such as data transformation or analysis — executing tasks one-after-another or in parallel to enhance performance.

The process ends with outputs like reports, updates, or data storage, often during low-activity periods, to maximize system utilization and minimize disruption.

Basic principles

Here are some basic principles of the batch processing method:

• Data intake. Data is collected and aggregated into batches from various sources, such as databases, files, or APIs.
• Batch execution. Processing tasks are performed on the grouped data, usually without user intervention.
• Output production. The results of batch execution are generated in various forms, including reports, updates to databases or files, or storage.

Here is an example flow of batch processing:

1. Data collection: Raw data is gathered from various sources such as databases, files, sensors, or APIs. This data can be of various types including text, numerical, or multimedia.
2. Data preprocessing: Raw data often requires cleaning, normalization, and transformation to make it suitable for analysis. This step involves removing duplicates, handling missing values, scaling numerical data, and encoding categorical variables.
3. Batching data: Data is divided into batches based on a predefined criteria such as time intervals (e.g., daily, weekly), file sizes, or record counts. Each batch contains a subset of the overall data.
4. Processing: Each batch of data is processed using a specific algorithm or set of operations. This could involve computations, analyses, transformations, or model predictions depending on the task at hand. For example, in a batch image processing pipeline, this step might involve resizing, filtering, and feature extraction.
5. Aggregation: Results from each batch are aggregated or combined to derive meaningful insights or summaries. This could involve calculating statistics, generating reports, or visualizing trends across multiple batches.
6. Storage or output: The final results of the batch processing are typically stored in a database, data warehouse, or file system for future reference or further analysis. Alternatively, the results may be presented as reports, dashboards, or visualizations for consumption by stakeholders.
7. Monitoring and iteration: Batch processing systems are often monitored for performance, errors, or anomalies.

Batch processing vs. stream processing

The choice between batch and stream reflects a trade-off between timeliness and comprehensiveness.

• Batch processing handles data in large, discrete chunks, known as batches, within scheduled windows. Batch processing is best suited for scenarios where the completeness of data is essential, like end-of-day reporting or inventory management.
• Stream processing tackles data as it arrives in real-time, with no inherent delays. Stream processing excels when immediate insights are required, as seen in fraud detection systems or live dashboards.

Organizations often integrate batch and stream processing to leverage both strengths. While batch operations provide in-depth analysis of historical data, stream systems react to immediate data inputs and events.

Micro-batch processing

Micro-batch processing is a hybrid approach that combines the advantages of both batch and stream processing. In this method, data is processed in small batches at frequent intervals, allowing for faster insights while still maintaining the completeness of data found in batch processing.

This technique is commonly used in scenarios where real-time or near-real-time analysis is required, but the volume of data is too large for traditional stream processing methods to handle.

Components of Batch Systems

Batch systems are characterized by their methodical approach to handling large volumes of data. To enable batch processing, several components must be in place. Here are the key components to consider.

Job scheduling

Job scheduling is the process of specifying when and how often batches should be processed. A job scheduler is a tool or system used to automate the execution of tasks at predetermined intervals. Job scheduling ensures tasks are prioritized correctly, dictating which jobs execute when and on what resources.

Some common job scheduling tools include:

• Apache Spark
• Apache Airflow
• AWS Batch
• Google Cloud Dataflow

Algorithms can be used to determine the best sequence for executing tasks. These algorithms consider dependencies, resource availability (like CPU or memory), and expected completion time to optimize the best schedules. This minimizes downtime and accelerates overall processing time.

Moreover, a job scheduling system must be resilient to faults, capable of handling unexpected failures by rerouting tasks or restarting jobs to guarantee completion.

Resource allocation

Resource allocation in batch processing involves the management of computational assets to ensure tasks are handled efficiently. It requires planning, oversight, and a comprehensive understanding of system capacities and limitations to allocate resources effectively.

This process stretches beyond mere CPU or memory assignments. It includes managing:

• Disk space
• Network bandwidth
• Data access rights

Careful resource allocation is pivotal to preventing bottlenecks in the data processing pipeline. It balances load across all system components, ensuring a smoother workflow and avoiding overutilization of any single resource.

Job execution

Job execution in batch processing is a highly orchestrated sequence of events. It typically entails a series of steps, from initialization to cleanup. This workflow is often automated and operates without human intervention, with the exception of some tasks that require manual input or decision-making.

The execution process also includes monitoring for errors or system failures and handling them appropriately. Here are the steps:

1. Initialization: The system sets up the necessary environments and parameters for the job.
2. Execution: The actual processing of data according to predefined workflows and algorithms commences.
3. Monitoring: Continuous observation to track progress and detect abnormalities in the execution phase.
4. Completion: After processing, the job yields results and releases resources for subsequent tasks.
5. Cleanup: Final housekeeping tasks ensure a clean state for the system, removing temporary files and data.

Each job follows a detailed execution plan to ensure data integrity and process accuracy.

It is crucial that jobs are executed in a controlled and predictable manner to guarantee the reliability of batch processing systems.

Batch processing: Use cases & applications

Batch processing finds its place within a variety of verticals, notably where large volumes of data must be processed during off-peak hours.

Here are some common examples of batch processing applications.

Financial transactions

Financial institutions like banks and credit card companies handle millions of transactions each day, requiring large-scale data processing. Batch systems enable them to process these transactions in bulk when transaction volumes are lower, either at the end of each day or during weekends.

(See how Splunk makes financial services more resilient.)

Customer billing

Businesses use batch systems to generate invoices or billing statements for customers. These can include utilities, telecommunications, or subscription-based services.

(Related reading: capital expenses vs. operating expenses.)

Inventory management

Retailers rely on batch processing to manage inventory levels. Using data from sales transactions and inventory databases, batch systems can reconcile stock levels and generate reorder requests automatically.

Report generation

Batch processing is commonly used for generating reports in various industries, such as healthcare, government agencies, and marketing firms. These reports can include financial statements, sales reports, or operational metrics that require data from multiple sources.

ETL jobs

Extract, Transform, and Load (ETL) is a process used to transfer data from different sources into a single location for analysis. Batch processing systems are often used to perform ETL jobs to load data into their respective data warehouses.

Advantages & challenges

To fully consider the feasibility of batch processing, we have to look at the advantages and challenges it comes with, especially when comparing to other methods like stream processing.

Here are some advantages of batch processing:

• Cost-effective: Batch processing is often more cost-effective than real-time processing, as it utilizes resources during off-peak hours when they are not in demand.
• Data completeness: Since all data is processed at once, batch systems ensure that all data is included and processed in each job run.

However, there are also some challenges to consider with batch processing:

• Delay in processing: Batch systems often have a lag time for data to be processed, making it unsuitable for use cases that require real-time or near-real-time data insights.
• Scalability concerns: As data volumes continue to grow, batch processing systems may struggle to keep up with the increasing workload. This can lead to longer processing times or system overloads.
• Managing dependencies: Batch processing systems must handle complex dependencies between tasks, such as data dependencies or interdependent workflows. This can be challenging to manage and may require a more sophisticated job scheduling system.

Despite these challenges, batch processing remains an essential tool for many industries that require large-scale data processing without the need for real-time insights.

Wrapping up

Batch processing is a fundamental concept in data processing and feeding data. It continues to play a crucial role in handling large volumes of data and automating complex workflows.

With batch processing evolving into other new methods such as micro-batch processing and lambda architectures, this technique will continue to be a vital component in the data processing pipeline. Organizations should consider the balance between the need for real-time analysis and cost-effectiveness and work that into their data strategy and architecture.