What is Batch Processing?
Batch processing is a method of scheduling large-scale groups of jobs (batches) to be processed at the same time as determined by a member of the IT or business team. Traditionally, batch workloads have been processed during batch windows, which are periods of time when overall CPU usage is low (typically overnight). The reason for this is two-fold:
- Batch workloads can require high usage of the CPU, occupying resources that are needed for other business processes during the business day
- Batch workloads are typically used to process transactions and to produce reports. An example of this would be gathering all sales records that were created over the course of the business day
Today, batch processing is done through job schedulers, batch processing systems, workload automation solutions, and applications native to operating systems. The batch processing tool receives the input data, accounts for system requirements, and coordinates scheduling for high-volume processing. Batch processing requires non-continuous data and is not highly time sensitive. This is distinct from stream processing,also called streaming data processing, which requires a stream of continuous data and is time sensitive due to incoming, real-time data.
What is Workload Orchestration?
Workload orchestration is the process of bringing together all automated workloads into one centralized, scalable platform in order to better manage, monitor and optimize them. This allows IT teams to get the control and visibility into their processes that they’re missing when multiple native job schedulers and disparate automation tools are used across the organization.
Orchestration also saves time and money by streamlining workload creation and development and eliminates the need to maintain different schedulers and automation tools. It makes it easier to follow best practices and ensure compliance with all regulations when all processes are being built, launched and managed in one centralized, scalable platform. It’s used by both IT operations and DevOps teams for different use cases throughout the lifecycle as part of larger automation initiatives.
Workload orchestration is becoming increasingly important as organizations incorporate more and more automation into every part of their business and IT operations. With the greater visibility and control it brings, IT teams spend less time on manual intervention and can instead monitor performance metrics and proactively identify and remediate issues.
A History of Batch Processing
Batch processing is rooted in the early history of computers. As far back as 1890, the United States Census Bureau used an electromechanical tabulator to record information from the US census. Herman Hollerith, who invented the Tabulator, went on to found the company that would become IBM.
By the middle of the 20th century, batch jobs were run using data punched on cards. In the 1960s, with the development of multiprogramming, computer systems began to run multiple batch jobs at the same time to process data from magnetic tape instead of punch cards.
As mainframes evolved and became more powerful, more batch jobs were being run. To prevent delays, applications were developed to make sure that batch jobs only ran when there were sufficient resources. This helped give rise to modern batch processing systems.
Examples of Batch Processing
Banks, hospitals, accounting, and other environments that have complex data sources and handle large data sets all benefit from batch processing. Wherever a large data set needs processing, there is a batch processing use case.
For example, report generations run after the close of business, when all credit card transactions have been finalized. Utility companies collect data on customer usage and run batch processes to determine billing.
In another use case, a financial data management company runs overnight batch processes that provide financial reports directly to the banks and financial institutions they serve. It can also be used for newer container orchestration technologies such as Docker and Kubernetes as well as open source and cloud computing services like Microsoft Azure.
Advantages and Disadvantages of Batch Processing
Batch processing data sets is useful because it provides a method of processing large amounts of data without occupying key computing resources. If a healthcare provider needs to update billing records, it might be best to run an overnight batch when demands on resources will be low.
Similarly, batch processing helps reduce downtime by executing jobs offline and/or when computing resources are available.
Batch processing tools, however, are often limited in scope and capability. Custom scripts are often required to integrate the batch system with new sources of data, which can pose cybersecurity concerns where sensitive data is included. Traditional batch systems can also be ill-equipped to handle processes that require real-time data, for example, stream processing or transaction processing.
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Modern Batch Processing Systems
Modern batch processing systems provide a range of capabilities that make it easier for teams to manage large volumes of data. This can include event-based automation, constraints, and real-time monitoring. These modern capabilities help ensure that batches only execute when all necessary data is available, reducing delays and errors.
In order to further reduce delays, modern batch processing systems include load balancing algorithms to make sure batch jobs are not sent to servers with low memory or insufficient CPU capacity available.
Meanwhile, advanced date/time scheduling capabilities make it possible to schedule batches while accounting for custom holidays, fiscal calendars, multiple time zones, and much more.
However, because of the growing need for real-time data and the increasing complexity of modern data processing, many IT organizations are opting for workload automation and orchestration platforms that provide advanced tools for managing dependencies across disparate platforms.
Batch Processing Takes to the Cloud
The modern IT department is diverse, distributed, and dynamic. Instead of relying on homogeneous mainframes and on-premises data centers, batch processes are being run across hybrid environments. There’s a good reason for this.
Batch processes are frequently resource-intensive. Today, with the growth of big data and online transactions, batch workloads can require quite a lot of an organization’s resources. Leveraging cloud native infrastructure gives IT the ability to provision compute resources based on demand, instead of having to install physical servers that would, for a good chunk of the day, likely be idle.
The amount of data IT has to manage to meet business needs continues to grow, and batch processing and workload orchestration tools are evolving to meet these needs. For example, IT doesn’t have the resources needed to manually execute each ETL process, or to manually configure, provision, and deprovision VMs. Instead, batch workload tools are being used to automate and orchestrate these tasks into end-to-end processes.
For example, an automation and orchestration tool can be used to move data in and out of various components of a Hadoop cluster as part of an end-to-end process that includes provisioning VMs, running ETL jobs into a BI platform, and then delivering those reports via email.
As organizations become more dependent on cloud services and apps, the ability to orchestrate job scheduling and batch workloads across disparate platforms will become critical.
Batch Processing and Workload Orchestration
Automation and orchestration tools are increasingly extensible, with several workload automation solutions already providing universal connectors and low-code REST API adapters that make it possible to integrate virtually any tool or technology without scripting.
This is important because instead of having job schedulers, automation tools, and batch processes running in silos, IT can use a workload orchestration tool to centrally manage, monitor, and troubleshoot all batch jobs.
IT orchestration tools can, for example, automatically generate and store log files for each batch instance, enabling IT to quickly identify root causes when issues arise. Real-time monitoring and alerting make it possible for IT to respond to or prevent delays, failures, and incomplete runs, accelerating response times when issues do occur.
Automatic restarts and auto-remediation workflows are also increasingly common, while batch jobs can be prioritized to ensure that resources are available at runtime.
Additionally, extensible batch processing and workload orchestration tools make it possible to consolidate legacy scripts and batch applications, enabling IT to simplify and reduce operational costs.
Future of Batch Processing
Traditional batch scheduling tools have given way to high-performance automation and orchestration platforms that provide the extensibility needed to manage change. They enable IT to operate across hybrid and multi-cloud environments and can drastically reduce the need for human intervention.
Machine-learning algorithms are being used to intelligently allocate VMs to batch workloads to reduce slack time and idle resources. This is critical for teams managing high-volume workload runs or with large numbers of virtual or cloud-based servers.
With machine learning running in real-time, additional resources can be reserved if an SLA-critical workload is at risk of an overrun. This includes provisioning additional virtual or cloud-based machines based on dynamic demand. Coupled with auto-remediation, this provides a powerful tool to make sure that service delivery isn’t delayed to the end-user or external customer.
In the long-run, IT is becoming more diverse and distributed, and the types of workloads IT is responsible for will continue to expand. The maturation of new technologies -artificial intelligence, IoT, edge computing- will place new pressures on IT teams to quickly integrate new applications and technologies.
IT is rapidly changing, but some things, such as batch processing, stay the same.
Frequently Asked Questions
A batch workload is typically a large volume of tasks that are run consistently as a part of business operations. These workloads usually contain large amounts of data and take longer periods of time to run. They are usually run during off-hours or at night when more compute resources are available.
A batch job can include everything from collecting client files to running financial reports or generating reports. It is typically run at night when more compute resources are available and are usually large jobs that take up a lot of time and computer resources.
Batch workloads are a more general term that applies to any large workloads that are run on-premises, in private or public clouds or in a hybrid ecosystem. Online or cloud workloads refer specifically to any batch processes that are run on cloud native resources or web services.
Batch workloads run large jobs, typically with a significant amount of data, all at one time. Workflows are set up using triggers, or events that kick off the next part of the workflow once that specific step has been completed.
Batch processing workloads is the method of scheduling groups of jobs into batches that can be automated. This frees up IT and business resources and ensures that these jobs are run consistently and accurately as they are scheduled.
Batch processing is best used for repetitive tasks such as collecting daily sales reports, running data on customer usage for a utility company or doing the daily customer billing. These types of batch processing apply broadly across industries, including healthcare, finance and insurance, accounting and more.