Modern businesses of all sizes are dealing with massive amounts of data moving back and forth through their datacenters. It is simply not cost-efficient or resource-efficient for organizations to continue with a data management plan that burdens their systems with countless transactions every hour or even every minute. The solution is a cloud batch processing methodology that handles data transactions in a simple and effective manner.
Key Takeaways:
Batch processing is a method that computers use to execute high-volume repetitive data jobs. Certain data processing tasks that are resource-intensive can be inefficient to perform in individual data transactions, so the system groups these tasks into batches that it can complete in a single transaction for the sake of being resource efficient.
Cloud batch processing, then, is an equivalent methodology that takes place in the public or hybrid cloud. As businesses migrate more processes to the cloud and attempt to deploy and maintain an ever-greater amount of workloads, the efficiency of batch processing becomes a necessary benefit to capitalize on.
Batch processing functions by queuing specified types of tasks for the next upcoming “batch window.” When a batch window arrives, the system runs all queued tasks in one efficient data transaction.
Types of tasks suitable for batch processing are those that are compute-intensive, such as data backups, filtering, and sorting. These are jobs that are frequent, repetitive, and perhaps not exceedingly urgent.
While cloud batch processing brings greater efficiency to IT operations, there is no one-size-fits-all batch processing strategy. Rather, batch processing works based on information provided by the user requesting batch jobs. The batch processing system uses this information to determine the allocation of resources necessary for completing the job.
The details a user should specify when requesting a batch job include the name of the person submitting the job, the programs to run, system input and output locations, and the preferred timing of the batch window.
The user should also provide information on the batch size. This refers to the number of work units that the system will process throughout the batch operation. It is possible to measure batch size by:
Another necessary concept for the operation of cloud batch processing is the presence of dependencies. A dependency is a circumstance that can trigger a batch job task to run in sequence following the completion of an earlier task, such as when customers place complete a transaction in an online store.
For many organizations looking for a data processing solution, there is a question as to whether batch processing is preferable to stream processing. Whereas stream processing allows for real-time monitoring of data and is preferable when the data amount is unknown, batch processing requires less processing power overall and tends to be a better option when the details of a data transaction are predictable.
Other benefits of batch processing include simplicity, efficiency, improved data quality, and increased speed of data processing. Organizations that capitalize on these benefits can see significant improvements in their data management strategies and make better data-driven decisions.
Businesses that rely on high-speed, high-quality data management stand to benefit the most from the efficiency brought about by batch processing. These include organizations in the fields of financial services, medical research, digital media, and software-as-a-service.
The batch processing systems in today’s IT landscape are capable of running hundreds of thousands of simultaneous batch jobs either on-premises or in the cloud. Keeping in mind the scalability of the cloud-as-a-platform, cloud batch processing in particular has seemingly limitless potential.
Though batch processing can take place either in the physical datacenter or in the cloud, it is important to consider the cloud-specific use cases for batch processing. These include the migration of data from on-site locations to public cloud locations or various sites across a vast distributed network.
IT decision-makers should seek a cloud platform that provides users with large batch-processing performance and the capability for batch-processed upgrades. Nutanix Life Cycle Manager makes these types of upgrades possible with one-click simplicity.
Cloud monitoring tools inherent to platforms like Nutanix also make it easier to spot abnormalities that might arise during batch processing. When a batch job takes an irregularly long amount of time to complete, IT teams will have visibility of the issue from a centralized control plane.
Batch processing in the cloud can be simple to use, useful in many scenarios, and scalable to fit the needs of any organization. Implementing batch processing to the most productive extent, though, requires doing so on the right cloud platform.
Nutanix Cloud Platform (NCP) supports batch processing as well as all manner of other workloads across both private and public clouds. With Nutanix Life Cycle Manager on NCP, organizations can effectively centralize batch upgrades, manage dependencies, and unify platform upgrades as part of any batch-processing endeavor.
Cloud batch processing should ultimately be a step toward operational simplicity for the entire business. With a full understanding of batch processing and the right cloud platform to suit an organization’s specific needs, that simplicity can be a matter of fact.
Learn more about other ways to rethink cloud workloads and how similar cloud-native computing practices can benefit the enterprise.
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