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    One of the most critical assets of an enterprise is its Real-time Data (RTD). To compete with competitors, an enterprise has to update its Real-time Data (RTD) management. There are many sources that process and analyze data in real time, for example, user interaction events from a mobile phone application. 

    This has numerous advantages, but what is Real-time Data? What is streaming data? What is real time processing? This article covers all aspects of data processing in comparing Real time Processing vs Batch Processing vs Stream Processing.

    What is Real time Data Processing?

    Real time processing is the fastest method to process data. In this method, data is executed quickly that provides output data with accuracy. This method deals with input data in real time and provides a response based on the data. To provide output data repeatedly, the inputted data needs to be constantly streamed.  

    How Does Real Time Data Processing Work?

    In real time data processing, inputted data is rapidly changed to provide an output. In this process, the change in data from input to output happens very quickly compared to other techniques. This method is usually used when input data needs to be rapidly processed to get an outcome.

    A real time data processing starts when it receives input data from the real time data warehouse. This input data can be singular or multiple. After the data is obtained, the processor sends a signal about what must be done with the inputted data. 

    In this scenario, various things can happen, and programming is needed for every outcome. After the processor matches the procedure to previous data processing, it takes steps to process it instantly. After the steps are taken, and the Real-time Data (RTD) is processed, the output is released immediately. All of this is done in a matter of seconds. 

    What Are Real Time Analytics?

    Real time analytics is preparing and processing Real-time Data (RTD) as soon as it is entered into the database. Through real time analytics, users can get real time data analysis regarding the inputted data and instantly draw conclusions after entering the system. 

    This allows business enterprises to react without any delay. Thus, they can seize marketing opportunities instantaneously to grow their business or prevent problems before affecting their business. 

    Benefits of Real time Analytics

    Real time analytics has proved to be very useful for businesses today. Here are some of the benefits of using real time analytics to its full potential. 


    Real time analytics help process, analyze, and provide crucial Real-time Data (RTD) regarding marketing. Through this, you can snare any beneficial opportunity and expand your business. 

    Moreover, it helps improve profitability by saving money in departments like employee engagement and hiring. This will significantly reduce the workload of the Information Technology (IT) department, giving you a competitive edge in business.

    Customer Data Tracking

    Through real time analytics, you can track customer data efficiently and quickly. After the Real-time Data (RTD) is processed, you can get an immediate report using the Expenses Reporting App. This will also recommend you the best course of action recommended to you. 

    Additionally, real time analytics will reveal all the customer behavior and how to maximize their satisfaction through your sales and marketing. 

    Quicker Response Time

    If you have an Amazon store, a sudden fluctuation in the market can mean opportunities for your business to flourish. Integrating real time analytics with your Amazon store through Amazon Connector will ensure that you get ahead of all the situations that might prove to be costly. Thus, you will be able to exploit the right opportunities at the right time to grow your business successfully. 

    Competitive Advantage

    Compared to other companies and business enterprises that use stale data, you can gain a competitive advantage by accessing real time data analytics. You will be able to view trends and benchmarks to make the best choices according to the situation that will boost your business. 

    Examples of Real Time Analytics

    Financial Operations

    By real time analytics providing business enterprises with strategic insights, the financial management of a business can carry out financial operations through back-office procedures and many other ways. 

    Financial statements must be accurate as they help you make the best decision for the business. Real time analytics helps in spotting errors and aids in reducing operational risks that can ruin the operation. 

    Financial Trading

    In a business enterprise, the financial department needs to make all the purchasing and selling decisions. With real time analytics at their disposal, they can take advantage of additional information regarding the financial situations of the general market. 

    They can also use databases, news sources, weather reports, and social media to have a broader perspective on the market. This perspective will assist the financial department in making wise and the best trading decisions.

    Marketing Campaigns

    With the ability to access real time data analytics, you can run marketing campaigns. Through the data, you can adjust your campaign parameters and strategies to boost success. 

    For example, if you are running an advertisement campaign, you will be able to retrieve data in real time about the people clicking the ads. Regarding those people, you can adjust your message to target the audience directly. 

    Importance of Real Time Analytics

    For many businesses today, like farm managers who use Farm ERP Software, real time analytics is a blessing. Decision-making is an integral part of any business, and the data forms a vital part of this process. They need Real-time Data (RTD) to do their planning, whether for forecasting or testing. 

    With customers spending more and more of their budget and time on online platforms, their reliable data must be analyzed for traffic and monitor statistics. Analyzing Real-time Data (RTD) is incredibly valuable today when we have to make decisive decisions fast. 

    With advances in technology, real time analytics has become an asset for many business enterprises as they cannot gain critical insights related to any data type. 

    Real time analytics has played an essential part in the lifecycle development of data processing. Data processing is still in its developing stage, and its pace is primarily dependent on advanced real time analytics. As real time analytics develops further, the performance pace of data processing is also improving in critical areas, such as computing. 

    Tools for Real time Processing

    Real time data processing has revolutionized with time, and its tools have evolved as well. There are numerous real time data processing tools that you can use to implement real time processing in your computer systems. With advances and innovations in technology, many real time data processing tools have been developed equipped with faster Central Processing Units (CPU).  

    Entrepreneurs use real time data processing tools to start more effective marketing campaigns, making their products and services accessible to many consumers. The corporate industry is also building vast amounts of data daily, and they use even more sophisticated technologies to consume, process, and analyze this data. 

    These tools aid you in integrating Real-time Data (RTD) into your system to be processed and analyzed. Thus, the devices must be robust in their functionality and provide quick and accurate responses. Here are some real time data processing tools:

    1. Kafka
    2. Kafka is a tool that you can get very easily. It is pretty scalable and has impressive features, such as data redundancy and fault tolerance. Thus, you will not have to worry about removing any extra data that you feel is unnecessary. Real time data processing will do all the work for you. 

      Kafka data processing tool can be seamlessly integrated with your system by WooCommerce Connector, and it will not affect your productivity. It is built explicitly for small-scale to growing systems that cater to your overall needs and do not go into detailed necessities. 

      Although Kafka is a data redundancy tool, it may prove challenging to use as it is a newer and sophisticated technology. Additionally, it does not have in-built connectors, which are essential in transferring data and processing it. 

    3. Flume
    4. Flume has a reputation in the market for having well-established connectivity. You can integrate it with many e-commerce stores, like Magento through Magento Connector. It is one of the broadest supported visualization tools among all its competitors. 

      This real time data processing tool does not necessarily have any downsides. However, if this tool fails, the entire business data will be deleted. As a result, you will not be able to recover any lost data. 

    5. Apache Storm
    6. Apache Storm is an outstanding real time processing tool for a business that wants to process data loads in less time. This tool is built by Twitter, and, unlike other batch processing examples, this tool can process streams of data efficiently and quickly with accurate results. This is especially useful for agricultural businesses who use Agriculture ERP Software, as they would be able to process all the data in seconds. 

      Apart from processing and analyzing data quickly, it can differentiate various processes at the node it is assigned to work on. In addition to that, it can integrate with other real time data processing tools to extend its capabilities. 

    Real Time Data Visualization Tools

    Real time data visualization tools are essential for business enterprises that use real time data processing or analytics. These tools allow all the output to be displayed in charts, graphs, videos, and other visual presentations. 

    Data visualization tools interpret Real-time Data (RTD), such as customer data, and connect it to your database or data dashboard. This helps you to see and analyze the outputs yourself quickly and more straightforwardly. 

    • Databox

    The Databox visualization tool is used by more than 15,000 business enterprises and agencies. This visualization tool gathers all your data in one place and tracks real time performance with detailed visuals. 

    This tool is best for marketing agencies or marketing managements in business enterprises who want to set up their data dashboard quickly. 

    Databox allows real time data integrations with many other software and apps, such as Shopify. You can integrate it with Shopify using Shopify Connector. Therefore, you can use this visualization tool with almost any application you want. 

    Furthermore, it has more than 200 pre-built reports covering almost everything, from Google Ads to email performance and marketing automation. This makes it very fast and efficient to use in your business. 

    • Tableau

    Tableau is the best visualization tool if you want to organize and see your Real-time Data (RTD) quickly. It assists you in creating graphs, charts, maps, and other types of graphics. This visualization tool enhances your visual data representations with its desktop version, which is now faster than ever. Moreover, it allows real time integration with your mobile devices so you can track all the Real-time Data (RTD) from your mobile phone. 

    This tool offers you JavaScript extensions as well to customize your reports through charting libraries. You can access many different types of real time data analytics through this tool, such as IT analytics, marketing analytics, and insurance analytics. 

    However, this tool cannot automatically refresh the outputted data reports, which may bother some users who want efficient processing. Also, the solution is not very comprehensive, and you would be required to have prior knowledge about such visualization tools. 

    Limitations of Real time Data Processing

    Although real time data processing is efficient, accurate, and quick in processing data and producing results, it does have some weak areas where its efficiency is limited. 

    Firstly, real time data streams can be complex and advanced for some users who do not have prior knowledge about such data processes. This may displease the user who may not even try to implement it in their business. Consequently, they would lack some essential tools that can help them flourish their business, for example, real time analytics, real time data processing, and 

    Furthermore, real time data processing can occasionally be slow. Although it is popularly known that real time processing is the fastest technique to process data, there are still some lags in real time processing. This is because it is made of codes and programs, and such a sophisticated program may occasionally slow down in its process. 

    Lastly, there is always the danger of all the Real-time Data (RTD) being erased if real time processing fails. As the data is being processed, the work is not automatically saved, and a breakdown in between the process may result in a deletion of all the Real-time Data (RTD). 

    What is Batch Processing?

    Batch processing is when a large volume of data is processed all at once. Batch processing stores all the data until it is completely transferred and received and processes it as a batch. 

    When data is processed as a batch, it is collected and organized into a single file. This file is then stored until all the data has been collected from the source. Afterward, the central database is updated through batch data integration at scheduled periods. Hence, data is not only collected together but also processed together. 

    Batch data processing is also another efficient way to process and analyze data collected over time. This helps reduce the costs of various operations that the business might spend on employees. In addition, batch data processing can be used offline when there is no internet connection and allows data managers to completely control the flow of data from when it is processed until stored in the central database.  

    Challenges of Batch Data Processing

    Batch data processing has its downsides as well. Firstly, debugging any bugs or glitches that might halt data processing can be a big issue. Debugging the system can become tricky if you do not have the expertise to do it. 

    Another issue is that batch processing can be costly as it is not very simple to use, and technical experts must operate it in the right way. Additionally, the employees will need to understand how to trigger batch data processing, schedule a data processing, and what specific notifications from batch processing mean. 

    Batch Vs Real Time Processing: Which is better?

    People have always compared batch vs real time, though most often real time data has been the one emerging with the upper hand. Let’s look at why real time data processing is better than batch processing. 


    The time frame between inputting the data into the processing unit and getting the output is called response time. This response time is crucial to process data across the system. 

    Real time data processing is entirely predictable regarding response time. In this real time processing, output data is usually successfully provided accurately and timely. 

    It sets deadlines for itself to complete the data processing, which results in producing the output quicker. Although it may not be lightning-fast as you might expect, it does give you a deadline of when you will receive the required output. 

    Batch data processing does not provide any specific time to expect the output to be delivered. Also, there is no time limit in batch data processing, and it may consume a lot of your crucial time.  

    The data processing will be completed whenever the batch processing system can complete them. Though, it will only depend on the processing speed of your systems and the volume of data assigned to be processed. 

    General Procedure

    Real time data processing has a straightforward and efficient procedure for collecting and receiving the input data, processing it, and then transferring it to its respective database. In real time transaction processing, only a change of data from input to output occurs. 

    Unlike batch data processing, data only goes through two main processing stages: entering the processing unit as input data and leaving it as output data.  

    Batch processing, on the other hand, collects all the received data. Only when it is satisfied that all the data has been received successfully does it start processing it. 

    After processing the data, it collects it in a transaction file. This transaction file is then sent to either the central database or to the data dashboard, where the data manager can use visualization tools to see it. 


    Batch data processing prefers a vital task over a less important one. For instance, during data processing, batch processing will not conduct an antivirus scan. Due to this, a potential virus may find its way into the system and corrupt it with all the files.  

    Real time data processing does not have such issues. It multitasks in a system. Apart from processing Real-time Data (RTD), it can run antivirus scans and receive commands from its operator simultaneously. 


    Real time data acquisition systems are pretty reactive and independent. It will behave according to the situation and will find the best course of action. 

    However, a batch data processing system is dependent on its operator. Usually, batch processing is a part of a more extensive processing system. For example, a batch processing system will not start processing data until it is commanded to. That is why is it only processes data after the complete information is received and collected. 

    What is Stream Data Processing?

    Stream data processing refers to the process in which data is instantaneously analyzed and streamed from one device to another. This procedure of studying and streaming data across devices happens as data flows through the computer system of a business enterprise. Therefore, with data flowing easily across devices in a business enterprise, there is no need to store large amounts of data in a particular place. 

    This data processing method is highly beneficial if you want to track all the activities and events occurring in your computer system. Stream processing identifies activities and responds right away. 

    Limitations of Stream Data Processing

    One of the biggest challenges of stream data processing is that its system starts to have issues with storage and memory if the data output rate is slow. Moreover, it can become tough to cope with the enormous amount of output data and organize it. To keep the flow of output data smooth, you will have to create a plan to ensure efficiency in the data processing. 


    Real time data processing is a crucial innovation, as you would not have to wait for data to appear before entering it into the real time processing system. Through real time data services, you will be able to enter the data on the system as soon as you receive it. You will not have to fret about anything as real time data processing will cover everything for you. Though, you may need to make slight tweaks to make real time data processing impeccable. 

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