Batch Processing vs. Stream Processing

With an increase in the data flowing through organizations, a business is only as good as their ability to process it. There’s never been more diverse sources of data, and with that, there are multiple options for processing that data. Two of the most popular types right now are stream processing and batch processing 

Streaming vs. batching is an important choice to make when looking for the right data processing solution for your organization. Learn the definition of each type, discover the pros and cons, use cases, and better understand which type can provide your organization the biggest benefits. 

What is Stream Processing? 

Stream processing is a newer method of data processing that analyzes data as it arrives in real time. Using stream processing, a system can process and transform data as it is generated in a continuous data flow—otherwise known as a stream—and automatically output it to a database or another application or system. 

Stream Processing Pros and Cons 

When your organization needs to keep data flowing, stream processing is a great tool to have. Here’s why: 

  • Provides real-time insights as data is analyzed, giving you insights into events as they happen. 
  • Increased processing speed is great for time-sensitive processes that rely on getting the most accurate data as soon as it’s available. 
  • Processing data in real time can be a more efficient use of resources as the data doesn’t require storage before processing. 

But there are times when a constant flow of data may not be ideal for an organization. Here are some of the downsides of stream processing:  

  • Systems must be optimized for low latency to ensure minimal delay to the applications and processes requiring real-time data to function. 
  • As the amount of data in a stream is constantly changing, it can be harder to scale as applications might find it difficult to keep up with capacity and other resources needed to meet a larger volume of incoming data. 
  • An increased need for fault tolerance and reliability is necessary to ensure minimal disruptions to the flow of data and even data loss. 
  • Data inconsistency can cause problems if there are different sources of data coming through your stream at the same time, causing things to arrive out of a sequential order. 

Use Cases: When to Choose Stream Processing 

Whenever a process needs real-time data, choose stream processing. These stream processing examples include: 

  • Fraud Detection
    Banks and financial institutions can use stream processing to analyze real-time transactions on credit cards, bank accounts, and more to help identify any suspicious or fraudulent activity. 
  • Network Security
    Stream processing can be used to monitor activity on your network to detect threats and attacks for real-time alerting and mitigation. 
  • Log Monitoring
    Monitoring logs with a data stream can help spot any issues with servers, applications, or other systems in real time to help troubleshoot and avoid downtime. 
  • User Behavior Analysis
    With the addition of an AI and machine learning tool, stream processing can be used to make real-time suggestions to customers based on their inputs into your website or application. 

What is Batch Processing? 

Batch processing is when data is processed together in larger volumes, known as batches. These batch jobs can include scripts, tasks, file transfers, loads, and even multi-step workloads, processed at a set interval or time. 

Batch Processing Pros and Cons 

Batch processing is best when you have larger volumes of data that isn’t as time sensitive. Here are a few of the benefits of batch processing: 

  • More efficient way to process large volumes of data with limited user interactions. 
  • Increased reliability as batches are run in a more controlled environment and can be more easily backed up in case of failure. 
  • Easier to scale—as well as handle more complex data—as batch processing can give users more control over processing using triggers, dependencies, and other parameters. 

However, batch processing might be seen as more old-school, and more and more, real-time processing is required. Here are some of the downsides to batch processing:  

  • Not great for time-sensitive tasks or applications when real-time data is needed, especially for quick analytics. 
  • Can be more complex to orchestrate without the proper batch scheduler. 
  • Increases data latency as batches are run at intervals far greater than seconds and minutes, and one batch must be processed before the next. 

Use Cases: When to Choose Batch Processing 

Choose batch processing when your tasks are less time-sensitive and involve larger amounts of data. These batch processing examples can include: 

  • ETL Processes
    Batch processing can be used to orchestrate every part of the ETL process from import to export across your entire IT environment. 
  • Order Fulfillment
    Automate daily transactions and processes like online orders, file arrivals, invoices and billing, and other processing, to run on any necessary prerequisites. 
  • File Transfers
    Move large amounts of files with greater efficiency by automating file transfers to run based on events, like a file entering a folder, or based on a specified time. 
  • Report Generation
    Bring together critical business systems and applications to automatically create month-end reports based on parameters and dependencies that reduce the burden on system resources. 

Key Differences Between Batch Processing and Stream Processing 

The big key difference between streaming vs. batch processing is when the data is processed. With stream processing, data is processed in real time instead of in batches.  

To get the best of both worlds, an enterprise batch scheduler with workload automation capabilities can create an effective solution for batch and streaming data ingestion that gives you the reliability and efficiency of batch scheduling, with closer to real-time processing as streaming. 

Best Practices for Implementing Batch Processing 

Realizing that your tasks are currently better suited for batch processing than stream processing? Or are you currently using batch processing and want to add more streaming capabilities? A workload automation solution can deliver the best of both worlds. Here are some important requirements to look for in a WLA tool to implement batch processing best practices that take you closer to stream processing: 

  • Day of Week Scheduling
    A good batch scheduling tool should allow users to configure jobs to be carried out on specific days during the week, specific days of the month, every day, workdays, and on intervals (e.g. Every Other Tuesday). 
  • Event-Driven Scheduling
    To get as close to real-time processing as possible, look for a batch processing tool with triggers. Using dynamic, event-driven scheduling, triggers fire off jobs and tasks that are contingent upon another task or the presence of files—for when Event A leads to Event B. 
  • Dependency Requirements
    In many cases, it may be necessary to run a batch process only if other conditions are satisfied. Those conditions could include having enough resources to run a process, or that a file is available for editing. A batch processing solution with dependencies allow processes to enter a “ready” state, so they will begin running as soon as the dependency is satisfied. 
  • Recurrences and Retries
    To create a more fault tolerant and reliable environment, a batch processer should have the ability to recover from issues they may encounter, and ensure that batch processes that need to run multiple times each day can do so without manual intervention.


Want to See What Batch Scheduling Could Do for Your Organization? 

Get a personalized look at the batch scheduling capabilities of JAMS, Fortra’s comprehensive workload automation and job scheduling solution.