We are all aware that enterprise data needs an upgradation constantly, and of late, that change has come at an increasing pace. Organizations that were once processing all their data on-prem have now transitioned to the cloud computing technology. The structures that are known and used suddenly become obsolete. However, an interesting debate that still subsists is how to get data processed faster.
If you’re trying to understand Big Data and Data Analytics, but are confused between Real-time, Batch processing, and Stream processing, here’s a guide for you:
Real-time data processing contains a continual input, an unstoppable process, and output of data. In this kind of processing, the processor got to be busy all the time. Here, the data must be processed in a very short duration (or almost real time). Real-time data processing technique allows an organization to take an instant action for those instances when taking an action within seconds or minutes is required. Bank ATMs, Radar systems, air-ticket reservation, and customer services are examples where real-time processing is used.
Examples of real-time processing:
– Live video processing
– Online ticketing system
– Radar system
– Weather forecasts
– Temperature measurement
– Bank ATM transactions
Batch processing involves dealing with non-continuous data. Here, the data is processed in parts and is done at the end of the day, bi-monthly, or monthly. Batch processing is excellent at handling data sets quickly but fails to get near the real-time requirements of the majority of today’s businesses. Batch processing works well in times where you do not require real-time analytics results, and when it is more important to process large volumes of data than having quick analytics results. In other words, this technique also means waiting to do everything together and heavily relying on your system to handle it all at once.
Examples of batch processing:
– Transactions of credit cards
– Generation of bills
– Clearing of cheque
– Processing of bulk images
– Financial report generation
Video Stream processing involves dealing with continuous data and is practical “the key” to turn big data into fast data. By building data streams, you can incorporate data into analytics tools as soon as it is generated and get almost instant analytics results. This process is specifically useful for tasks like fraud detection. You can detect differences that detect fraud in real-time by stream-processing transaction data. This way, you can stop the fraudulent transactions before they are completed and have a reasonable solution to improve the outcome.
Examples of Stream processing:
– Smart patient care
– Surveillance, Intrusion, Fraud detection (e.g.: Uber)
– Traffic monitoring, vehicle, and wildlife tracking
– Supply chain optimizations
– Stock market surveillance
– Network monitoring
Which one is better?
Whether your data is suited for real-time, batch or stream processing, they’re all better when working together. Although streaming processing is best suited for cases where time is significant, in real time processing, there’s never a delay in response and the information is always up to date. On the other hand, batch processing functions well when all the data has been collected. Ultimately, it’s not about which one is better than the other – it all relies on your business objective.
A takeaway from the debate
Now that we’ve seen a comparison between real-time, batch processing, and stream processing in detail, making a decision of selecting either of the methods depends on the current business system. Basically, there are numerous conditions on which it relies, whether to use one over the rest. For instance, it is a clear scenario that enterprises are inclining toward real-time analytics and data streams to fetch actionable data in real-time. While a few outdated tools cannot cope with the desired speed or scale involved in scrutinizing data, today’s streaming applications and databases are well-equipped to handle today’s business requirements.
Thus, select the method that best suits your business system. This way, you can obtain faster results and work on the problems or opportunities before losing the ability to leverage the output from them.