Real-Time Data Processing: Essential Insights You Need to Know

Many data sources broadcast data in real-time such as user interaction events from mobile applications, POS (Point of Sales), IoT devices, and bank transactions. Monitoring systems need real-time data such as health monitoring systems, fitness trackers, and traffic control systems.

Developers are working on capturing real-time streaming data at varying scales and complexities. So, what is real-time data processing with data streaming?

What is Real-Time Data Processing?

Real-time data processing is the quickest data processing technique that executes data in a short period of time and provides the most accurate output. Real-time data processing deals with the inputted data that are captured in real-time and provides an automated response based on the streams of data.

To provide a continuous output, the inputs need to continuously be streaming. For example, a real-time traffic monitoring system such as Google Maps is collecting data in real-time to show congestion, or automatically start high-occupancy lanes or other traffic management systems. Google collects that data in real-time to dynamically update its maps.

Real-time processing is also known as stream data processing which is faster and more accurate than batch processing. In the batch processing system, at first, data is collected and then processed in bulk. Let’s see the key differences between real-time data processing and batch data processing.

How Real-Time Data Processing is Better Than Batch Data Processing

Let’s start comparing real-time processing with batch processing by defining batch data processing.

Batch Data Processing Definition

The process through which a computer completes a series of jobs is called batch processing. Batch processes are often done simultaneously and non-stop. This is done to ensure that large jobs are done in small sections for the efficiency of the debugging process.

Batch data processing has a lot of names. Some of them include Job Scheduling and Workload Automation (WLA).Over the course of time, batch processing has been named differently, but all these different terms refer to the same thing. With batch data processing described, let’s jump right into the differences between batch data processing and real-time data processing:

#1. Overall procedure

The first step to understanding which one of the processing systems is better is understanding the process. In short, batch data processing means collecting data over a certain period. And upon collecting all the data, the information enters the system at once.

Batch data processing is waiting to do everything at once. This relies on your ability to handle everything at once. Batch data processing is used for payment processing, packing slips and printing shipping labels. But, real-time processing handles each transaction and enters the information in your system as soon as it arrives. This means all your staff must be in sync all the time.

#2. Required time frame

The time between the input of data and the computer performing expected output is known as response time. Response time is critical for a data processing system. Real-time systems are predictable when it comes to response time. In a real-time system, outputs are successfully provided timely and accurately. It isn’t important for response times to be quick. But what a real-time system has, is deadlines. A real-time process might not be lightning-fast, but it sets a definitive time of when you’ll receive your product.

On the other hand, batch processing doesn’t have any set time limits. There isn’t a certain time limit when the task will be completed by the computer. The tasks will be completed whenever the computer can complete them. Everything depends on things such as the volume of work and the processing speed of your computer.

#3. Deadline delay

Although it’s rare for real-time processing to not being able to meet the deadline, it can happen. The reasons may be a complete system failure or an inability to work during the system’s peak time. On the other hand, batch data processing may not be able to meet the deadline but in that case, it only needs more processing capability to finish the given task.

#4. Dependancy

Real-time systems are quite reactive. A real-time system will behave according to the condition it has been put in. Real-time processors are usually independent which means that they don’t have an operating system and are subject to control hardware devices.

For example, a digital thermometer has a real-time processor built in that continuously gives out the correct temperature every time. But a batch processor isn’t independent. The majority of the time a batch processor is part of a larger computer system.

#5. Predictability and Flexibility

Real-time systems are subject to specific and predictable results that occur in response to an input. The number of outputs a real-time system can give out is fixed. We will take our old friend thermometer for example again, a thermometer has fixed readings and won’t show things such as “It will rain today”.

Batch processors, on the other hand, don’t have any fixed readings. Administrators can adjust batch processors to work for different purposes.

#6. Postponing a procedure

When it comes to batch processing, processes are saved when a computer isn’t performing a task. An important action will be preferred over a less important one. For example, an antivirus scan won’t be conducted at office time as employees have to work at that time. Thus, it will be completed after office hours. Real-time processing doesn’t have any such problems. You can multitask in a real-time system. The processor will start a process as soon as an input is received.

#7. Outside of computing

Batch processing isn’t confined to computers only. For example, a company might send a bill every month instead of every week. This small step saves a ton of resources that would be used on postage. Real-time processing only refers to digital things such as computers and microcontrollers.

How Does Real-Time Data Processing Work

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Real-time processing is the process in which a system can input rapidly changing data and then provide output instantaneously so that the change over time can be seen very quickly. Real-time data processing is a method that is used when data input requests need to be dealt with quickly. This quick input needs to be dealt with a quick output. This is called ‘the latency’

A real-time data process starts with receiving data input. The data input can be in single or multiple quantities. After the data is received the computer sends out a prompt regarding what it should do with the data it has been given. Various scenarios can happen in this sequence, and programming needs to be done for every possible outcome. The system will then match the scenario and take steps accordingly. And after steps have been taken, the output will be shown instantly. All of these are done within a few moments.

An example

The computers inside a car’s engine unit manage the engine every single second based on what the actions were taken by the driver. An input event can’t be missed while programming for real-time processing. Because if forgotten, it will result in a cataclysmic scenario.

One thing you’ll have to keep in mind is that real-time processing does not have to be ‘fast’. A good example of this is the traffic light system. Traffic lights are a real-time system but it only needs to process data whenever required. On the other hand, a car engine deals with input events happening every nanosecond. Therefore, a very fast computer is needed.

Some of the examples of real-time data processing are:

• Traffic lights
• Heart rate monitoring
• Aircraft control
• Computer games
• Controlling a spacecraft.

Read the use case Apache Kafka drives Rabobank Real-Time Financial Alerts.

Tools related to real-time data processing

As real-time data processing has developed over time so has its tools. There are so many data processing tools out there now that it’s very hard to separate the best from the rest. We are producing an immense amount of data and then as there is this change in technology over the years, many real-time data streaming technologies have become more and more available.

The corporate industry is producing a huge amount of data every day. And there is even more technology to process all this data. Entrepreneurs are adapting to real-time data streaming tools as they make business marketing campaigns easier. It also makes marketing messages and financial trading easier. Leading companies like Netflix use these data streaming platforms.

It seems interesting, doesn’t it?

There are a few real-time data streaming tools that will help you if you know the process and what to do with it. You need to know why you’re selecting a particular tool before deciding to go for it. Down below are some of the Real-Time Data Processing tools that will help you.

Real-time data processing and streaming tools

Real-time data streaming is the process of analyzing a large amount of data upon producing it. You can process all the valuable information for your business when using a real-time processing tool. For example, data streaming tools such as Flume and Kafka permit direct connections to Hive, Spark, and HBase.

Real-time data processing tools help data to get integrated into the system and process the entire thing without writing. Thus, the robust functionality is used here which is the ideology of data lake architecture.

Here are some of the top real-time data streaming tools that could pique your interest.:

#1. Flume

Is known to have well-established connectivity, it is compatible with Hadoop and it requires a preset target called sink. Flume is one of the most widely supported tools among all of the commercial Hadoop distributions. Aside from being an attractive and essential feature, Kafka and Flume both complement each other very well.

Flume doesn’t have many drawbacks, except for one, which is quite daunting If Flume data streaming tool fails, data will be erased completely, and consequently, you won’t be able to retrieve or replicate any past events.

#2. Kafka

Kafka is available everywhere and highly redundant. It’s also quite scalable and has features such as one-to-many messaging.

Kafka boasts of features like fault tolerance and data redundancy. For example, whenever a Kafka agent goes down, then some other Kafka agent will re-broadcast the topic. In short, you will not experience the same commercial connectivity as Flume.

Kafka and Flume both are perhaps the best bet for you. You will be able to link both of them in a large scale production system. But for the small scale systems, it is better if you choose the system that caters to your overall needs. Even though Kafka is redundant, it is a bit harder to operate as it is a relatively new technology. Besides that, Kafka lacks in the commercial support department. It also doesn’t have the built-in connectors which are important.

#3. Apache Nifi

Is another good tool for Real-Time Data Processing. Apache NIFI has built-in data logistic features. It creates a platform for automating the data movement between different destinations.

NIFI supports distributive sources such as files, social feeds, log files, and videos. NIFI is capable of moving data from any source to any destination. It also traces the data in real-time.

#4. Apache storm

Built by Twitter, Apache Storm is a must-have tool for real-time data processing. Unlike Hadoop that carries out batch processing, Apache Storm was specifically built for flowing streams of data. It has other uses too. Online machine learning and ETL among the other things Apache Storm could be used for.

Apache Storm can process data ridiculously fast. Apache Storm differentiates in carrying out processes at the node it is assigned in. Furthermore, it can be integrated with Hadoop to further extend its abilities.

#5. Amazon kinesis

With Amazon Kinesis, companies can build real-time streaming applications using Java libraries and SQL editor. Kinesis takes care of the heavy-lifting of running the applications and scaling to match requirements when needed. Because of kinesis, you will be able to get rid of the hassle of managing servers and other complexities regarding building and managing applications for real-time processing.

The flexibility provided by Kinesis helps businesses to start with basic reports and insights of their desired data. But as demands scale up, it can also be used for learning algorithms for in-depth analysis.

Impact of Real-Time Data Processing in Financial Market

These days most of the data is processed through batch processing. But one thing you should keep in mind is that any kind of data benefits from being real-time processed.

Data that is valuable to your business should be updated in real-time. Therefore, it is always available exactly when you need it This enables your business to function more effectively. A good example of this is sales promotion, as you can adjust your sales based on the market’s trends. This increases the level of customer satisfaction your brand provides.

As the use of E-commerce is on the rise, customers expect real-time interaction with online retailers. This increases their trust in retailers. Most customers wouldn’t want to wait a day or two to see their transactions appear on their credit cards. They want confirmation of purchase right away.

E-commerce has become the center point of the corporate sector today because of the urgency that consumers feel. With the rise of real-time processings, business owners are now able to increase the level of satisfaction of their customers. They can deliver what customers want within a quick time. Positive customer experience results directly in an increase in sales.

If you want your business to stay in the competition then you need to switch to a real-time data processing system. This allows your customers to have new experiences as well as new features that you couldn’t have offered with batch data processing. real-time data processing provides your customers with exactly what they are looking for.

Read more about the challenges of the banking sector.

Limitations and Future Improvement of Real-Time Data

While real-time data processing is the clear cut favorite when it comes to choosing a data processing system, there are factors that need to be taken into consideration

Real-time data processing can turn out to be both complex and expensive for users that have never seen it before. Though it is expensive now there’s a great possibility of the price being toned down once it is available everywhere

Furthermore, real-time processing can be considered tedious. While it is true to a certain extent, it is because people aren’t used to it yet. Think of it like this. Riding a bicycle can seem tedious too before learning to ride properly. It’s the same with adapting to real-time processing.

The final complaint most people have is that backing up data needs to be done daily. Though this is a severely frustrating issue, steps are being taken to implement a system where the daily backup isn’t necessary.

Daily backing up data mainly depends on the number of transactions you have each day. However, this is mainly done to ensure that the system can fetch the latest transaction upon request.

Implementing real-time data processing systems

Implementing real-time data processing systems in legacy IT infrastructures presents unique challenges. Legacy systems often lack the flexibility and scalability that real-time data processing requires. Integrating modern, real-time systems with these older infrastructures can necessitate significant modifications or complete overhauls to ensure compatibility and efficiency. This process can be complex and resource-intensive, as it may involve updating or replacing outdated hardware, software, and data management practices to accommodate the high-speed, continuous nature of real-time data streams.

For small businesses, adopting real-time data processing in a cost-effective manner is indeed possible, especially with the advent of cloud computing and as-a-service platforms. These technologies allow businesses to leverage powerful data processing capabilities without the need for significant upfront investment in hardware and infrastructure. By utilizing cloud-based services, small businesses can scale their data processing needs according to demand, ensuring they only pay for what they use. Furthermore, open-source tools and platforms offer cost-effective alternatives to more expensive proprietary solutions. By carefully selecting the right tools and technologies that match their specific needs and budget constraints, small businesses can effectively implement real-time data processing to enhance decision-making, improve customer experiences, and stay competitive in their respective markets.

The Future of Data Processing: Why Real-Time is the Way Forward

While people have adapted to batch data processing before, real-time data processing in the future, gone are the days when you had to wait for every single piece of data to appear before inputting it. With real-time data processing, you can enter data as soon as it arrives.

Real-time data processing is better in every way possible whether it is the overall procedure or dependency or flexibility, real-time data protection has everything covered.

Real-time-data processing has a lot of resources to work with. You can work with both open source and premium tools. Industry revolutionizing tools such as Kafka or Apache Samza are at your disposal to work with.

Though some things need to be tweaked to make real-time data processing even more perfect, it’s the best way of processing your data and by far has no competition. It will be a wise decision to switch to using Real-time data processing.

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