Real-Time Processing

What is Real-Time Processing?

Real-time processing is a method of processing data at a near-instant rate, requiring a constant flow of data intake and output to maintain real-time insights.

What is real-time data?

Real-time data is the process of analyzing data to create insights in real time. When raw data is received, it is immediately processed to empower near-instant decision-making. Instead of being stored, it is made available to promote insights as quickly as possible, furthering organizations’ profitability, efficiency, and business outcomes.

Why is real-time data important?

Real-time data is incredibly important to businesses, providing tremendous insights from data sets that are being processed in real time. Enterprise organizations benefit dramatically from real-time data, as the insights produced have the capacity to enhance operations, boost monitoring and visibility for IT architecture, optimize business outcomes, and even improve overall customer experiences.

What is batch data processing?

Batch data processing does not happen in real time. It gathers and stores a large volume of data, then processes it all at once. Compared to real-time data processing, this approach trades immediacy for more complete, complex analyses. 

What is an example of real-time processing?

Real-time processing has benefits across all industries in today’s markets. With a growing focus on Big Data, this system of processing and acquiring insights can drive enterprises to new levels of achievement.

Some real-world applications of real-time processing are found in banking systems, data streaming, customer service structures, and weather radars. Without real-time processing, these industries would not be possible or would deeply lack accuracy.

For example, weather radar is heavily reliant on the real-time insights provided by this system of data processing. Due to the sheer volume of data that is being collected by supercomputers to study weather interactions and predictions, real-time processing is absolutely critical to successful interpretation. 

What are examples of batch data processing?

The key difference between real-time processing and batch data processing is that batch data processing functions as large volumes of data are broken into groups based on transactions, and collected over time before insights are given. Instead of occurring in real time, data is being batched constantly, within a given timeline, to produce insights.

Batch processing only occurs when workloads are present, unlike the rapid and continual system of data intake and output of real-time processing. Additionally, it establishes efficient use of compute as processing in batches is more economical. Sorting occurs to group similar jobs together, then they are processed simultaneously. Based on measurements, this style of processing functions opposite of real-time’s action-oriented structure.

A common example of batch data processing includes credit card or debit transactions and subsequent billing systems. Financial accounting benefits from this data processing architecture because reports can be run after a given timeline, such as when all transactions have been finalized and closed at end of day. This keeps the system flowing efficiently and in a highly organized manner without the demand of rapid, immediate response by real-time processing architectures.

What are the three methods of data processing?

The three methods of data processing are mechanical, manual, and electronic. Each method is effective and necessary within given applications, with varying benefits to each system.

Mechanical data processing

Mechanical data processing occurs through machines or devices including calculators, printing presses, typewriters, or other mechanical means. The benefit to this method lies in minimized errors, however it has quickly become unrealistic in today’s data landscape. There is no reasonable way for it to maintain pace with the sheer volume of data being acquired, studied, and processed. With increasing data volume comes greater complexity, making this method better in simple, low-volume applications.

Manual data processing

Manual data processing involves manual data acquisition and sorting with direct human participation. It requires logical rigor and disregards the use of any type of automated systems or software. Although this method is economical, making it an attractive choice for small or new businesses, it can lead to frequent errors due to the human element.

Electronic data processing

Electronic data processing utilizes modern technologies and processing programs. It requires the most initial spend, as it involves the procurement of all the technology necessary to build an effective data architecture. Essentially, software runs all processing tasks on demand, and produces corresponding insights. This is the most accurate form of data processing.

HPE and real-time processing

Solve the most complex problems and answer the biggest questions with HPE high performance computing solutions, expertise, and global partner ecosystem. The power of supercomputing with HPE allows enterprise organizations to scale up or scale out, on-premises or in the cloud, with intentional storage and software to power your innovation. Every workload, aligned to your budget.

HPE GreenLake for HPC powers fast deployment with ease, for all your consumption-based projects. And it’s fully managed and operated for you. With a driving demand for real-time insights, HPE Cray Exascale Supercomputers are purpose-built to handle the convergence of data modeling, simulation, AI, and analytics workloads. It hasn’t met a workload it can’t accomplish.

Experience incredible agility, simplicity, and economics with HPC cloud technologies. With deep learning and AI capabilities, along with high-performance data analytics that drive better business models, you can accelerate your digital evolution and outpace even the best competition.

Deliver business outcomes quicker with HPE GreenLake for Data, a model designed for the largest workloads, all delivered as a service. It establishes an end-to-end solution that truly drives innovation within your digital landscape. As enterprises leverage Big Data to gain real-time insights and power better decision-making to gain competitive advantages, the HPE GreenLake edge-to-cloud platform reduces complexities and costs of deployment. All while simultaneously simplifying your environments, including the complex infrastructure of Apache Hadoop.