Analyst Report | A Blueprint for Modern Monitoring
What is real-time data processing?
Real-time data processing refers to a system that processes data as it’s collected and produces near-instantaneous output. To understand the advantages it offers, it’s important to look at how data processing works and contrast real-time data processing with another commonly used method: batch data processing.
The goal of data processing is to take raw data (from social media, marketing campaigns and other data sources) and translate it into usable information and, ultimately, better decisions. In the past, this task was performed by teams of data engineers and data scientists. Today, however, much of data processing is done by artificial intelligence and machine learning (ML) algorithms. While the nature of processing indicates at least some kind of time delay, the speed or lack of "heavy" processing or near parallel processing provides a faster, as well as more complex, analysis. There are six steps for turning raw data into actionable insights, which are repeated cyclically.
- Collection: Gathering data is the first step in the processing cycle. Data is collected from data warehouses, data lakes, online databases, connected devices or other sources.
- Preparation: The data is “cleansed” to remove corrupt, duplicate, missing or inaccurate data and organized into a suitable format for analysis. This helps ensure that only the highest quality data is processed.
- Input: The raw data is converted into a machine-readable form and fed into the processing system.
- Processing: The raw data is processed and manipulated using artificial intelligence (AI) and machine learning algorithms to generate the desired output.
- Output: The processed data is passed on to the user in a readable form such as documents, audio, video or data visualizations.
- Storage: The data is stored for future use. It can be easily retrieved when information is needed, or used as an input in the next data processing cycle.
Batch processing and real-time processing both follow these steps, but they differ in the way they’re executed, which makes them suited for different uses.
Batch data processing is commonly used for handling large volumes of data. In this method, data is gathered over a certain period of time and stored, after which all the data is entered into the system at once and processed in bulk. Once the data is processed, a batch output is produced.
Batch data processing has several advantages. It’s ideal for processing large volumes of data. There is no deadline to be met, so data can be processed independently from collection at a designated time. And because data is processed in bulk, it’s highly efficient and cost-effective. The one major drawback is a delay between data collection and the result yielded from the processing, making it ideal for processing accounting data, such as payroll and billing.
In real-time processing, data is processed in a very short time to produce a near-instantaneous output. Because this method processes data as it is put in, it requires a continuous stream of input data to produce a continuous output. Latency is much lower in real-time processing than in batch processing and is measured in seconds or milliseconds. This is attributed, in part, to steps that eliminate latency in the network i/o, disk i/o, operating environment and code. Also, “formatting” the incoming data can be seen as an impediment or heavy lift for users and customers. Real-time data processing is at work in many daily activities, such as ATM transactions and e-commerce order processing.
Speed is one of the main benefits of real-time data processing; there is little delay between inputting data and getting a response. It also ensures that information is always current. Together, these features enable users to take accurately informed action in the minimum amount of time. However, real-time data processing uses big data analytics and computing power, and the associated cost and complexity of these systems can make them prohibitive for organizations to implement on their own.
How is real-time data used?
Real-time data is used primarily to drive real-time analytics — the process of turning raw data into insights as soon as it’s collected. Also called business intelligence or operational intelligence, these analytics can be used across industries in any scenario where a quick response is critical. Some examples of real-time use cases include financial institutions that use real-time analytics for credit card fraud detection as the transaction is taking place. Similarly, real-time analysis can help ITOps teams predict a device failure. Virtually any complex task that requires immediate insights can benefit from real-time analytics.
There are two types of real-time analytics. On-demand real-time analytics requires an end user or system to create a query after which the analytic results are delivered. Continuous analytics, also called streaming data analytics, analyzes data as it is collected and alerts users or triggers a response to detected events. As mobile devices, Internet of Things (IoT) products, sensors and other sources create more data at greater speeds, real-time analytics has become increasingly essential, as it allows a constant flow of data to be processed in motion rather than after it’s stored.
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