A Holistic View on Streaming Data: How it Works, Use Cases, Benefits and More

The development of IoT devices and the extensive use of social media make data analysis processes more essential than ever. Next-generation devices and applications expose an information load called big data. Unstructured big data from different devices enables information to be evaluated collectively. Businesses that use cases of streaming data in the most accurate way gain an advantageous position in the market. Thanks to advanced computing architectures and tools, it is possible to know even in a very niche area.

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What Are the Use Cases of Streaming Data?

Processes of daily life and business life constitute the concept of digital information defined as big data. Technologies such as artificial intelligence, Internet of Things(IoT), geographic location tracking, digital mapping are among the core values of big data. Data flow applications provide a smooth data flow so that these tools can be interpreted in a meaningful whole.

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What is Streaming Data?

Data flow can be summarized as the process of receiving, sending, and processing data obtained in real-time. In areas where instant data is of great importance, it is crucial to use data flow analytically. The ability to process and analyse information with the support of artificial intelligence ensures that the process is sustainable under all conditions. An extraordinary amount of information is stored to make the most effective use of artificial intelligence tools. Businesses may easily analyse and process this data. Online games, stock markets, processing facilities, retail management systems, and location sharing applications are examples of data flow applications.

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How Does It Work?

Flowing data analytics ensure the continuity of the system under the available resources. Transactions are carried out without interrupting the efficiency of storage and corporate systems. Companies may also store data traffic in cloud-based systems. The use cases of flow data reveal a vision of the future, not a static and one-sided perspective. Depending on the variables and the process, it is possible to reach very different results from very different situations. Programmed to choose the most optimum among endless possibilities, artificial intelligence instantly produces the best solutions for business needs.

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What Are the Differences Between Streaming vs. Batch Processing?

Batch Processing is a traditional data analysis technique provided by analysing the collected data retrospectively. For example, to calculate the sales commissions of employees in a store, data such as working hours, periodic price charts, and performance reports are archived daily, weekly, or monthly. In the light of this data, businesses can calculate commission rates for their employees. Even though the batch process is an efficient way to process large volumes of data, it is not suitable for processing recent and short-range data, as minutes and seconds are involved.

On the other hand, data flow technology categorizes and analyses instant data without the need to archive. When it encounters suspicious transaction patterns or defined tasks, it is possible to take action very quickly. Banks and financial institutions frequently use data flow technologies in the field of cybersecurity. You can get help from streaming data services on many issues such as data breaches, fraudulent data processing, network security vulnerabilities, and server security.

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How Streaming Data Platforms Help Businesses?

Streaming data platforms have hardware such as data cloud servers, mobile devices, and sensitive sensors. Streaming data offers the opportunity to query or process information in the light of the most up-to-date data. Being able to interpret data with only millisecond response times makes it possible to avoid potential threats early. Simple response functions, exponential aggregations, and rolling measurements make it easy to create basic indicators. The use of basic indicators enables rapid action against events such as map changes or risky account transactions.

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What Are the Benefits of Streaming Data Technology?

‍Let's take a look at the prominent benefits of real-time data tools that compile, analyse, process, and control big data.

• Business Activity Monitoring (BAM): BAM, which significantly accelerates the decision process in workflows, helps businesses increase their profitability and improve their business processes. It provides the necessary conditions to make the best use of instant opportunities, generates high motivation, and identifies risks.
• Machine Learning: Machine learning, a versatile artificial intelligence application, allows the cumulative processing of information based on certain algorithms and access to more consistent information over time.
• Fraud Detection Tool: Fraud detection instantly detects illegal and high-risk transactions. Fraud detection software that tracks and reports user activity helps uncover software vulnerabilities and prevent fraudulent transactions.
• Data Processing in Real Time: Filtering, analysing, and processing data in real-time offers users a better shopping experience.

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What Are the Challenges to Building Streaming Data Technologies/Applications?

There are some challenges in the process of use cases of streaming data. Scalability is at the top of these. Bottlenecks that may occur due to system problems or transaction volume can prevent feedback. The order of priority of data is also among the issues to be considered in the process of using data flow states. Failure to ensure consistency and validity of information under all conditions is also a critical handicap. The accuracy of the information received from different sources, units, and devices must be ensured. Thanks to cloud solutions and software that come through all these difficulties, it can perform data analysis without any problems in every sector, from improving inventory modelling to preventing network security threats.

Examples:

Today, many industries connected with the Internet of Things benefit from streaming data transactions.

• City lighting automation is among the simplest examples of real-time data flow systems. Traffic lights synchronized according to the traffic density benefit from basic data flow processes.

• Data acquisition allows the collection, integration, and visualization of big data for automation on production lines.
• Smart meters, motion sensor IP cameras, fire sensors, and digital panels of bus stops are equipped with these technologies.
• The car rental application that works in synchronization with the city mapping system is also a great example of using cases of streaming data.
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Data streaming platforms that integrate real-time workflow processes with information from IT allow you to automate your business plan. You can easily organize, manage and maximize your profitability with amounts of data coming every second. To sum up, you can create your infrastructure with real-time data flow and easily manage your entire operation by taking advantage of multi-cloud solutions or serverless services.

You may take advantage of streaming solutions to accelerate your data modelling, reduce your working hours, ease the current workload, ensure data security and increase work motivation. Subscribe for more information about use cases of streaming data.

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