Optimizing Data Pipelines: Understanding Batch Processing vs. Stream Processing

Batch processing and stream processing are two different methods of handling data. Batch processing is processing vast volumes of data at once and at scheduled intervals, while stream processing is constantly processing data in real-time as it arrives.

Understanding the functions and differences between these methods can help you choose the best approach for your data processing needs.

What Is Batch Processing

Batch processing is when computers handle high-volume and repetitive tasks by grouping data into batches and processing. This method doesn’t require immediate results, saves time, and reduces computing power.

For example, in a payroll system, you gather all employee data over a pay period and calculate salaries for everyone at once through batch processing. Similarly, when you run system backups, you consolidate and store data in bulk at specific intervals.

Batch processing generates detailed reports when analyzing large datasets, and it’s ideal for achieving consistency and resource efficiency.

However, it is not always suitable for tasks requiring instant feedback or action.

What Is Stream Processing

Stream processing continuously ingests and analyzes data. Instead of waiting for the data to accumulate, you can process it instantly. Therefore, you can respond immediately to changes, which is critical for tasks depending on fast decisions.

For example, stream processing powers fraud detection systems. As transactions occur, you can spot and block fraudulent activity. Similarly, if you’re running an e-commerce site, you can personalize customer experiences straightaway by analyzing their behavior.

You’ll also find stream processing in action with IoT devices, like smart thermostats or fitness trackers, which react instantly to changes in their environment.

Stream processing provides the speed for modern applications, but it can be complex to implement and requires advanced infrastructure.

Batch Processing vs. Stream Processing – Key Differences

Batch processing and stream processing are different ways of processing data.

Batch and stream processing are suited to different data operations. You can typically use batch processing for large-scale, infrequent data jobs that don’t require immediate results. It processes data in enormous chunks, making it perfect for tasks where a delay is acceptable.

Whereas, you can use stream processing for continuous, real-time data processing where immediate insights and actions are vital. It handles data as it flows in by ensuring minimal latency and real-time data analysis and response.

Advantages and Disadvantages of Batch Processing

The Pros of Batch Processing

• Efficiency for large datasets: Batch processing excels at handling large volumes of data in one go, optimizing computational resources, and minimizing processing overhead.
• Cost-effective resource usage: Batch jobs are often scheduled during off-peak times, reducing costs associated with high-demand periods.
• Streamlined workflows: The sequential nature of batch processing simplifies system design and is ideal for operations that don’t require immediate results.
• Error handling: Errors can be identified and addressed before processing the next batch to preserve data accuracy and integrity.
• Scalability: Batch systems can handle growing data volumes effectively, making them suitable for businesses planning for long-term growth.

The Cons of Batch Processing

• Delayed outcomes: Results are not available until the entire batch is processed, which is a drawback for time-sensitive tasks.
• Resource spikes: Running batch jobs can temporarily strain system resources, potentially causing slowdowns during processing.
• Inflexibility: Adjusting batch workflows often requires significant changes, making them less adaptable to real-time needs.
• Error propagation: If errors occur during batch processing, they may affect the entire batch, requiring reprocessing.
• Higher upfront costs: Initially, designing and setting up batch processing systems can be resource-intensive

Advantages and Disadvantages of Stream Processing

The Pros of Stream Processing

• Real-time insights: Stream processing enables instant data analysis, which is critical for applications like fraud detection, live monitoring, and dynamic decision-making.
• Continuous data handling: Data is processed as it arrives, allowing uninterrupted workflows and timely responses.
• Enhanced agility: Your company can react quickly to real-time events by providing a competitive edge in fast-paced industries.
• Event-driven operations: It’s ideal for systems relying on triggers, such as IoT devices, online transactions, and sensor networks.
• Dynamic scalability: Stream processing systems can adapt to fluctuating data loads by ensuring consistent performance.

The Cons of Stream Processing

• Increased complexity: Implementing stream processing requires sophisticated architecture, specialized tools, and expertise.
• Higher operational costs: Real-time systems often demand significant computing resources, which boosts overall expenses.
• Data accuracy challenges: Ensuring consistency and handling out-of-order events can be complicated.
• Monitoring and maintenance: Stream processing systems need continuous monitoring to address issues immediately.
• Limited historical context: Stream processing focuses on current data. As a result, it’s less suitable for applications requiring detailed historical analysis.

Stream Processing Use Cases

Stream processing is particularly beneficial in several key areas. Here are 4 prime examples:

1. Fraud detection: Stream processing allows financial institutions to monitor transactions in real time. This helps identify and flag suspicious activities immediately, which helps in preventing fraud effectively.
2. Network Monitoring: In network management, stream processing enables you to constantly monitor your network traffic. This real-time analysis helps in quickly detecting and addressing any anomalies or issues, ensuring smooth network operations.
3. Predictive Maintenance: Industries use stream processing to monitor equipment health in real time. As a result, potential issues can be detected and addressed before they lead to equipment failure, which saves costs and improves efficiency.
4. Intrusion Detection: In cybersecurity, stream processing helps in real-time detection of unauthorized access or activities within a network. The detection allows for swift action to mitigate potential security threats.

Batch Processing Use Cases

You should use batch processing in scenarios where data processing must be scheduled and does not require immediate results. The 3 best examples include:

1. End-of-day reporting: Financial institutions often use batch processing for end-of-day reports. Transactions and activities are accumulated throughout the day and processed in one go, generating comprehensive reports for analysis.
2. Data warehousing: Organizations use batch processing to update data warehouses periodically. Large volumes of data are collected and processed in batches, ensuring that the data warehouse is up-to-date with the latest information for analytical purposes.
3. Payroll processing: Companies process payroll data in batches, typically on a bi-weekly or monthly basis. This involves collecting timekeeping data, calculating salaries, and generating paychecks, all done in bulk to streamline operations.

Batch Processing Vs. Stream Processing: Performance

Batch processing and stream processing are two different approaches catering to the same goal: processing large volumes of data. Each approach comes with its own set of strengths and weaknesses, with performance being the most important one.

In terms of performance, businesses resort to batch processing as an easily manageable and optimizable method. On the other hand, stream processing is the best choice for processing volumes of data in real-time.

The performance of each method is influenced by complexity. Batch processing is generally less complex than stream processing, mainly because data comes in batches and is processed offline. Comparably, stream processing is rather more complex because it processes data in real time, which is a challenge on its own.

Another aspect that influences the performance of each data processing method is the processing speed. Batch processing is somewhat slower because it involves processing data in batches and takes some time. On the other hand, stream processing processes data in real-time and with low latency, which makes it a suitable option for tasks that require immediate actions.

Batch Processing: Large, Complex Data Analysis

With batch processing, data is collected in batches and then fed into an analytics system. A “batch” is a group of data points collected within a given time period.

Unlike stream processing, batch processing does not immediately feed data into an analytics system, so results are not available in real-time. With batch processing, some type of storage is required to load the data, such as a database or a file system.

Batch processing is ideal for very large data sets and projects that involve deeper data analysis. The method is not as desirable for projects that involve speed or real-time results. Additionally, many legacy systems only support batch processing.

This often forces teams to use batch processing during a cloud data migration involving older mainframes and servers. In terms of performance, batch processing is also optimal when the data has already been collected.

Batch Processing Example: Each day, a retailer keeps track of overall revenue across all stores. Instead of processing every purchase in real-time, the retailer processes the batches of each store’s daily revenue totals at the end of the day.

Stream Processing: Speed and Real-Time Analytics

With stream processing, data is fed into an analytics system piece-by-piece as soon as it is generated. Instead of processing a batch of data over time, stream processing feeds each data point or “micro-batch” directly into an analytics platform. This allows teams to produce key insights in near real-time.

Stream processing is ideal for projects that require speed and nimbleness. The method is less relevant for projects with high data volumes or deep data analysis.

When coupled with platforms such as Apache Kafka, Apache Flink, Apache Storm, or Apache Samza, stream processing quickly generates key insights, so teams can make decisions quickly and efficiently. Stream processing is also primed for non-stop data sources, along with fraud detection, and other features that require near-instant reactions.

Stream Processing Example: A soda company wants to amplify brand interest after airing a commercial during a sporting event. The company feeds social media data directly into an analytics system to measure audience response and decide how to boost brand messaging in real-time.

Simple Solutions for Complex Data Pipelines

Boomi Enterprise Platform provides a unified solution for data pipelines, workflow orchestration, and data operations.

Some of Boomi’s features and capabilities:

• Completely Automated SaaS Platform: Start connecting data in the Boomi platform in just a few minutes with little to no maintenance required.
• 200+ Native Connectors: Instantly connect to applications, databases, file storage options, and data warehouses with our fully-managed and always up-to-date connectors, including BigQuery, Redshift, Shopify, Snowflake, Amazon S3, Firebolt, Databricks, Salesforce, MySQL, PostgreSQL, and Rest API to name just a few.
• Python Support: Have a data source that requires custom code? With Boomi’s native Python support, you can pull data from any system, no matter how complex the need.
• 1-Click Data Apps: With Kits, deploy complete, production-level workflow templates in minutes with data models, pipelines, transformations, table schemas, and orchestration logic already defined for you based on best practices.
• Data Development Lifecycle Support: Separate walled-off environments for each stage of your development, from dev and staging to production, making it easier to move fast without breaking things. Get version control, API, & CLI included.
• Solution-Led Support: Rated highly by G2. Receive engineering-led assistance from Boomi to facilitate all your data needs.

How Data Streaming Works

Data streaming means data continuously flows from the source to the destination, where it is processed and analyzed.

Data streaming allows for real-time data processing and provides monitoring of every aspect of the business.
Below we break down several data streaming features.

The Data Streaming Process

Every company possesses data that needs to be analyzed and processed. This data is piped to different locations through data stream processing techniques consisting of tiny data packets. It is then processed in real or near real-time, commonly used by streaming media and real-time analytics.

Unlike other processing techniques that don’t allow quick reactions and address crisis events, data streams do. These differ from traditional data thanks to several crucial features.

Namely, they carry a timestamp and are time-sensitive, meaning that after a while, they become insignificant. Happening in real-time, they are continuous and, at the same time, heterogeneous. Data streams can have multiple formats because of the variety of sources from which the data originates.

Note that there is the possibility that a stream may have damaged or missing data because of the different transmission methods and numerous sources, meaning that a data stream may arrive out of order.

The Data Streaming Hardware

When learning how data streaming works, it’s important to note some differences in the hardware. In other words, comparing batch processing vs. stream processing, we can notice that batch processing requires a standard computer specification. In contrast, stream processing demands high-end hardware and sophisticated computer architecture.

Batch processing uses most of the processing and storage resources to process large data packets. On the other hand, streaming processing reduces computational requirements and uses less storage to process a current set of data packets.

Today, data is generated from an infinite number of sources, so it’s impossible to regulate the data structure, frequency, and volume. Data stream processing applications have to process one data packet in sequential order. The generated data packet includes the timestamp and source, enabling applications to work with the data stream.

Difference Between Real-time Data Processing, Streaming Data, and Batch Processing

To fully understand how data streaming works, here is a simple distinction between these 3 methods.
Batch processing is done on a large data batch, and the latency can be in minutes, days, or hours. It requires the most storage and processing resources to process big data batches.

The latency of real-time data processing is in milliseconds and seconds, and it processes the current data packet or several of them. It requires less storage for processing recent or current data pocket sets and has fewer computational requirements.

Streaming data analyzes continuous data streams, and the latency is guaranteed in milliseconds. It requires current data packet processing; hence the processing resources must be alert to meet guarantees of real-time processing.

How to Choose Between Batch and Stream Processing

Evaluate Business Needs and Use Cases

When deciding between batch and stream processing, the first step is to assess your specific business requirements and use cases. The nature of your data plays a significant role.

If your data requires real-time insights, i.e., fraud detection or live monitoring, stream processing is the better choice. On the other hand, tasks like payroll processing or report generation can be efficiently handled through batch processing.

The urgency of insights is another crucial factor. Stream processing excels in scenarios where immediate responses are vital. In contrast, batch processing is better suited for less time-sensitive operations.

Additionally, the volume and frequency of your data should be taken into account. High-frequency data streams, like IoT sensor outputs, align well with stream processing, but large datasets collected periodically are better suited for batch processing.

Finally, consider industry-specific needs. For instance, financial services often prioritize real-time analytics, whereas manufacturing industries may benefit more from batch reporting.

Consider Budget and Infrastructure

Your budget and existing infrastructure are pivotal in determining whether to choose batch or stream processing.

Stream processing systems require more computational power and ongoing maintenance, leading to higher operational costs. Conversely, batch processing systems are generally simpler and less expensive to set up. That makes them an attractive option for organizations with limited budgets.

Scalability is another critical consideration. If your data volume is expected to grow rapidly, ensure your infrastructure can support the scalability demands of your chosen approach.

The compatibility of your existing tools and platforms is also important. Some tools may be better suited for batch processing, while others are optimized for stream processing. Moreover, leveraging cloud-based solutions can offer scalable and cost-effective processing options for both methods—which reduces the burden of managing physical infrastructure.

Hybrid Processing: Combining Batch and Stream

For many organizations, a hybrid approach integrating batch and stream processing may be ideal.

This approach optimizes workflows by using stream processing for tasks that require real-time insights, such as monitoring or anomaly detection—while relying on batch processing for periodic reporting or archival purposes.

A hybrid system uses the complementary strengths of both methods, so you can address diverse use cases within a single infrastructure. For example, your e-commerce platform might use stream processing to provide real-time order tracking and batch processing for inventory reconciliation.

Batch processing and stream processing offer unique benefits, and choosing the right one depends on your goals and resources. Whether you prioritize consistency or speed or decide to use both, you’re setting yourself up for success.