In today’s data-driven world, organizations of all sizes should handle and analyze data in real time. From financial institutions to eCommerce companies, real-time processing helps organizations make faster, more informed decisions, improve the customer experience, and optimize operations.
But for many people, the concept of real-time processing can be confusing and overwhelming. What exactly is real-time processing? How does it work? And how can it benefit businesses?
If you’re new to real-time processing and want to learn more about it, you’re in the right place. This article will help you understand real-time processing and how it can help your business in today’s data-driven world.
In this guide, we’ll explain real-time processing and how it can assist your organization.
We’ll also compare real-time processing with near-real-time and batch processing and describe its real-life applications. After reading this 10-minute guide, you’ll have a solid understanding of real-time processing, its advantages, and the tool that works best for it.
So let’s dive in.
What Is Real-Time Data Processing?
Real-time data processing refers to the ability to collect, process, and analyze data as it is generated. This means that data can be processed and made available for use almost instantly. This enables organizations to make decisions quickly and intelligently based on the most recent data available.
Typically, real-time processing is used in situations where data must be processed and made immediately accessible, such as in financial markets, eCommerce solutions, and online gaming.
Real-time processing should not be confused with stream processing. While stream processing refers to the continuous processing of data streams as they are generated, real-time processing refers to the timely processing of input data. Most stream-processing platforms do work in real-time, but not all real-time processing is stream processing.
In terms of time constraints on data processing, real-time processing can be divided into 3 main types:
- Hard real-time: Hard real-time systems must always meet deadlines. These systems are frequently utilized in mission-critical applications, such as aviation or healthcare, where a missed deadline could have catastrophic effects. Missed deadlines are seen as system failures.
- Soft real-time: Soft real-time systems try to fulfill deadlines most of the time but may miss one without any real consequence. These methods are widely used in video streaming or online gaming when missing a deadline is not important but should be avoided.
- Firm real-time processing: Firm real-time systems prioritize completing deadlines over other metrics. These systems are utilized when fulfilling deadlines is more important than data accuracy or system performance. Data delivered after the deadline may be considered invalid but isn’t a system failure.
Let’s now examine how real-time processing functions.
How Does Real-Time Processing Work?
Real-time processing involves several steps that can change based on the needs of the system and how it is built. But a general outline of how real-time processing works is as follows:
a. Data Collection
The first step in real-time processing is to collect data events as soon as they occur from sensors and devices, other applications, or databases.
b. Data Processing
As soon as the data has been collected, it is processed and put into a format that other systems or applications can use. Data can be filtered, aggregated, enriched, or transformed.
c. Data Storage
After data has been processed, it is often saved in a database so that it can be accessed and analyzed at a later time. This can be a relational database management system (RDBMS), a streaming platform, or an in-memory database optimized for real-time processing. Processed real-time data can also be stored in an analytical data store to be used for historical reporting and analysis.
d. Data Distribution
Processed and stored data is made available to downstream systems or applications via APIs. This helps organizations access and query data in real time and make prompt, informed decisions.
e. Data Analysis
This is the final step in real-time processing. It generates insights from the processed data that might drive business activities or decision-making. Machine learning, data visualization, and BI software can be used for this.
Real-Time Processing Vs Near Real-Time Processing Vs Batch Processing
At this point, you might be wondering if there are any alternatives to real-time processing. The answer is yes. While real-time processing is a powerful and effective data processing approach, it’s not the only one. When developing their data pipelines, data scientists can choose from several different approaches to data processing.
Besides real-time processing, there is also batch processing as well as near real-time processing. These approaches differ in the rates at which data is processed and made available for use, and each has advantages and disadvantages of its own.
What Is Near-Real-Time Processing?
Near-real-time processing, also known as “low-latency” processing, involves processing data with a few seconds to a few minutes of delay. In comparison, real-time processing requires a few milliseconds of delay, if any at all.
Near-real-time processing is employed when a business has to handle data quickly, but a delay isn’t critical. Applications where near real-time processing may be suitable include handling online transactions and managing inventory levels.
Some benefits of near-real-time include:
- Scalable: Near real-time processing is scalable, making it more flexible than real-time processing.
- Flexible: Near real-time processing is more flexible as it allows firms to trade off cost and speed as needed.
- Cost-effective: Depending on the implementation, near-real-time processing can require less infrastructure and computing power and thus be more cost-effective.
What Is Batch Processing?
Batch processing involves processing data in large groups, or “batches,” rather than individually. Batch processing is best for jobs that don’t need to be done right away. It works well for companies with a lot of data but the results are not time-bound.
Applications, where batch processing can be employed effectively, include:
- Generating invoices
- Analyzing sales data
- Preparing financial reports
The key benefits of batch processing include:
- Most cost-effective: Batch processing usually requires less infrastructure and computing power than real-time or near-real-time processing.
- Efficiency: Rather than handling incoming data sequentially, batch processing allows businesses to process large amounts of data in parallel.
- Simplicity: Batch processing systems are easier to set up and manage than real-time or near-real-time processing since they require less complicated infrastructure.
Now that you are familiar with the fundamentals of real-time processing, near real-time processing, and batch processing, it’s time to focus on their main differences.
Understanding The Key Differences Between Real-Time Processing, Near Real-Time Processing, And Batch Processing
When determining which technique is best for your company’s requirements, keep the following factors in mind:
A. Latency
Latency is the time needed to process and consume data. It’s an important factor when choosing a data processing method. Of the data-processing approaches discussed, real-time processing has the lowest latency—a few milliseconds or less. It’s perfect for time-sensitive operations.
Near-real-time processing comes in second with a few seconds to a few minutes delay. This is still low latency, and can be used by many applications that need quick results but can deal with a little delay.
Batch processing has the longest latency, which can be up to several hours or even days. This less time-sensitive technique is appropriate for non-time-sensitive processing tasks.
B. Cost
Real-time processing is traditionally the most expensive because of the system’s complexity and necessary infrastructure. To cut down on latency, real-time data processing systems usually need specialized hardware, software, and a high-speed network.
Near-real-time processing is less expensive than real-time processing and provides a balance between cost and latency.
Finally, batch processing is the most cost-effective solution of the three approaches because it requires minimal infrastructure.
Fortunately, new advances in real-time and near-real-time technologies are closing the gap in costs, and real-time processing can actually become cost-efficient at scale.
C. Complexity
Real-time, near-real-time, and batch processing differ greatly in terms of system complexities.
Real-time processing requires a hard-to-build software platform to process data in real time. For this method to work, you need a solid engineering team or to enlist the help of a pre-built product or service.
Near-real-time processing is less complex than real-time processing but still requires advanced infrastructure. This strategy strikes a balance between complexity and delay and is used for tasks that can handle a lot of delays.
Batch processing is the least complex method since it processes data in large, predefined groups rather than in real-time or near real-time. It’s easiest for small teams to build and manage from scratch.
D. Use Case
Perhaps the most important deciding factor when selecting the correct data processing approach for your business is the use case.
Real-time processing is the only correct choice for time-sensitive tasks like fraud detection and stock trading. This strategy works effectively when immediate and accurate results are essential.
Near-real-time processing is utilized for activities with a high delay tolerance. This strategy works well for applications like analyzing social media or answering service questions, where low latency is nice but not essential.
Lastly, batch processing is used for jobs that don’t have to be done right away, like making financial reports and doing sales analysis within a certain time window. This technique works effectively when data correctness is critical but latency isn’t.
Now let’s talk about some of the benefits that businesses can get from using real-time processing.
5 Advantages Of Real-Time Data Processing
Real-time processing has a lot of benefits, from improving customer experience to making better business decisions. Here are 5 of the most important benefits you can get from processing your data in real time.
1. Immediate Insights And Decision Making
Real-time processing enables quick insights and decisions based on the freshest available data, allowing business leaders to respond to events and changes as they happen. For example, in finance, where making decisions quickly is very important, real-time processing can help find fraudulent financial transactions and stop losses right away.
In retail, real-time processing can be used to deliver personalized suggestions to customers based on their browsing and purchasing histories, monitor supply chain and logistics operations in real time, or optimize marketing efforts by targeting the appropriate customers at the right time.
Real-time processing can also make it easier for teams to work together and talk to each other on the fly. This lets companies respond more quickly to changing market conditions and customer requests.
2. Improved Data Quality And Accuracy
Real-time processing also improves data quality and accuracy, allowing companies to detect and rectify errors and inconsistencies in real time.
This is especially crucial in industries like healthcare, where even tiny errors and processing delays can have major implications. Real-time processing can find and fix mistakes in patient records, keep track of prescriptions, and check the performance of medical devices.
Real-time processing can also increase data accuracy in retail and manufacturing. With real-time processing, you can:
- Find and fix mistakes in customer records
- Keep track of the number of items in stock
- Keep an eye on how well production equipment is working
This enables better decisions, boosts customer happiness, and reduces errors and losses by enhancing the data’s quality and completeness. Businesses may improve decision-making and decrease risk by processing and consuming data in real time.
3. Enhanced Customer Experience
Real-time processing can also be used in customer service systems to improve the customer experience. With real-time processing, firms can evaluate client data in real time and generate relevant, engaging recommendations.
Real-time processing in retail allows businesses to personalize products, services, and marketing to specific customers, track customer preferences, and propose customized products and services. Businesses may enhance sales and revenue by giving individualized recommendations and experiences.
Real-time processing can also be used to watch how a client uses a website or mobile app, find and fix problems in real time, and give personalized help.
4. Real-Time Monitoring And Control
Real-time processing enables real-time system monitoring and control. Businesses and agencies can spot problems and take rapid action by processing and analyzing data instantaneously.
In manufacturing, for example, it allows real-time monitoring and control of manufacturing operations. By collecting and analyzing data, manufacturers can spot problems with equipment or processes as soon as they happen and fix them quickly. This reduces downtime and increases efficiency and production.
Real-time processing can also be used to monitor and control the systems that make and distribute energy. This lets energy companies find and fix problems as they happen which can increase energy systems’ reliability and stability, benefiting businesses and consumers.
5. Enhanced Security And Fraud Detection
Real-time processing can make it much easier for a business to spot fraud and make its system more secure. By processing and analyzing data in real time, businesses can prevent data breaches and other security problems.
It can also help monitor network traffic in real-time to discover and prevent security concerns. With real-time processing, businesses can spot strange patterns of behavior that could indicate a security problem and prevent a breach from happening.
Real-Life Applications Of Real-Time Processing
Real-time processing can revolutionize how we live and work, whether it’s for monitoring and control, increased security, or fraud detection.
Let’s explore some of this technology’s real-world uses:
I. Financial Trading
Real-time processing allows traders to make informed judgments based on current data and market conditions. By digesting real-time data, traders may quickly react to market developments and trade accordingly. This optimizes their portfolio and profits.
Machine learning classifiers using high-quality real-time data can analyze real-time market data and make predictions about stock prices. In fast-moving markets, a few seconds’ delays can affect the outcome of a trade. Real-time processing lets traders make more educated, strategic decisions, giving them a market edge.
II. Fraud Detection
Real-time processing allows organizations to immediately identify and react to suspicious activities. By looking at data in real-time, companies can set up automated alerts for strange behavior or sudden increases in costs that they didn’t expect.
For example, in the financial industry, an event hub can be used to monitor real-time financial transactions for fraudulent activity. By analyzing the data in real time, the event hub can send an alert to security personnel if it detects any unusual activity, allowing them to quickly investigate and prevent financial loss.
III. Healthcare
Real-time processing can be used in healthcare to manage electronic health records and data from medical systems. By processing and analyzing real-time data, healthcare providers can quickly get information about a patient and make treatment decisions based on that information.
A single-process algorithm could be used to continuously analyze the data from a patient’s vital signs monitor and alert healthcare professionals if any values fall outside of normal ranges. This allows for timely intervention and can potentially prevent serious complications or even save a patient’s life.
IV. Transportation
Real-time traffic control systems for processing transportation data enable the monitoring and optimization of routes and the tracking of vehicles. With a huge amount of GPS data available from tracking devices and cell phones, real-time traffic updates can be generated and recommendations of alternative routes can be made to drivers.
Real-time processing architecture can be used in radar systems and can enhance public transportation schedules and routes and improve the customer experience. It can also be used to update passengers on arrival times and delays, helping them plan their journeys better.
V. Security And Surveillance
Real-time processing can greatly improve the ability of organizations to detect threats, improving their security and surveillance systems. Real-time data along with deep learning techniques can be used to analyze security camera video feeds and report questionable activity.
Border and airport security use real-time processing to track people and threats. By analyzing real-time data, these systems can respond quickly to possible threats, ensuring human and facility safety.
VI. ECommerce
Real-time processing is a game-changer for online retailers. It empowers businesses to leverage cutting-edge ecommerce solutions that deliver personalized product recommendations based on browsing behavior, manage inventory levels with precision, and detect potentially fraudulent transactions as they occur.
Why Estuary Is The Best Tool To Leverage Real-Time Processing
Estuary is a real-time, open-source streaming data platform. Our Estuary Flow platform simplifies the setup and management of data pipelines. It blends batch-based ETL’s ease of use with data streaming’s scalability and efficiency and delivers real-time Change Data Capture (CDC) and ELT (Extract, Load, Transform) to help enterprises collect and analyze data.
The platform guides data from collection through operationalization in real time. Estuary Flow also features real-time transformation tools. It can handle both streaming and batch data, making it suitable for a wide range of monitoring, control, data processing, and reporting tasks.
Estuary Flow supports a wide range of data connectors, including Kafka, Postgres, Firestore, and more, making cross-platform data pipelines easy to build. It lets developers easily design streaming data pipelines and workflows thanks to robust CLI support that blends seamlessly into its GUI-based web app.
Estuary Flow also supports type-checked, on-the-fly data transformation in addition to its connectors. This makes it easy to restructure and alter data during processing, allowing firms to get more value from their data.
Overall, Estuary Flow is a strong real-time data processing solution for setting up and managing data pipelines. Our diverse range of connectors and native data transformation capabilities make it an ideal choice for real-time data analysis.
Conclusion
Real-time processing helps businesses analyze and act on data in real time. It can improve efficiency and production, as well as help in improving security and detecting fraud. Several real-time processing options are available for you to choose from. Each solution has unique benefits and limitations, so it’s crucial to choose the one that is ideal for your organization.
If you’re in the market searching for the best solution for your real-time processing needs, we suggest giving Estuary Flow a try. Combining real-time event streaming and batch data processing, a multitude of data interfaces, and an easy-to-use graphical user interface, Estuary Flow is a powerful and adaptable tool for enterprises of all sizes.
Plus, with our cloud-managed service, you can start quickly and effortlessly without much technical skill. Want to try it for yourself? Start your free trial, or join the Estuary community to get your questions answered.
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