The Role of RTOS in the Future of Big Data Processing
As the name suggests, real-time operating systems (RTOS) handle real-time applications that undertake data and event processing under a strict deadline. As opposed to general-purpose operating systems like Windows and MacOS, RTOS performs repeated tasks within specific time constraints and ensures that processes are predictable.
RTOS is important in the operation of embedded systems because of their efficient real-time performance. Their deterministic nature also guarantees that task completion can be accurately determined and that there are no latency problems. They also support multitasking, resource management, and customization with their modular architecture.
With the advent of big data in the modern world, RTOS is becoming increasingly important. As software expert Tim Mangan explains, a purpose-built real-time OS is more suitable for apps that involve tons of data processing.
The Big Data and RTOS connection
IoT and embedded devices are among the biggest sources of big data. They are used in various settings, from industrial facilities to offices and homes. These connected devices collect and generate massive amounts of data, which form part of what is known as big data. RTOS happens to be one of the preferred operating systems for these devices.
RTOS powers the multitude of devices that process huge amounts of data for various purposes. It is the preferred operating system for data processing heavy operations for many reasons (more on this below). Around 70 percent of embedded systems use this OS and the RTOS market is expected to grow by 23 percent CAGR within the 2023–2030 forecast period, reaching a market value of over $2.5 billion.
There is no doubt that real-time operating systems (RTOS) have an important role in the future of big data collection and processing. It is the dominant OS used in IoT and embedded systems. With edge computing and generative artificial intelligence now becoming a part of modern digital life, big data is set to grow even bigger, and it is important to have a reliable embedded OS to match this growth.
How does RTOS help advance big data processing?
The advancement of big data processing relies on a number of crucial elements. In particular, its progress depends on the availability of related technologies that make the handling of huge volumes of data possible. These technologies include the following:
Data governance and management — It is crucial to have a solid data management system and governance practices to ensure data accuracy, consistency, and security. It is also important to establish data quality standards and strict access controls.
Data integration and interoperation — Data comes from an endlessly growing number of sources, which include various kinds of devices and systems. For big data to harness these sources, it is important to have the ability to integrate data and make them interoperable across different sources.
Advanced analytics and AI — It is virtually impossible to extract insights from big data through conventional evaluation and analysis, let alone manually. Advanced analytics aided by artificial intelligence enables the continuous processing of big data to make sense of all the available information and reveal hidden trends and patterns.
Scalable and flexible infrastructure — Processing big data requires an infrastructure that adapts to rapidly growing processing needs and different scenarios of data storage and usage. This entails the use of other technologies such as distributed computing, edge computing, and cloud computing.
Aside from these technologies, it is also important to build a data-driven culture among organizations. Big data processing advances when organizations develop a habit of collecting and using data in making decisions, experimenting, and fostering innovations. Also, it is necessary to foster data literacy and support training on data processing and data system design and implementation.
All of these can get a boost from the use of real-time operating systems. RTOS does not directly provide data management, advanced analytics, and machine learning features and functions. However, it can be the OS that runs powerful embedded systems capable of collecting, governing, and managing huge amounts of data and running advanced analytics. When it comes to data integration, RTOS can work with systems that employ data warehousing, API management, and ETL technologies.
Moreover, RTOS is built to be scalable and flexible. It can work with various implementations and architectures. It is a lightweight operating system with minimal overhead requirements that can also support distributed systems. It is useful in systems that involve a multitude of devices working together to carry out multiple tasks, especially data processing.
RTOS advantages that support big data handling
Real-time operating systems are prominently used in embedded systems and IoT devices because of their significant advantages. These advantages perfectly match the low-resource nature and various real-time constraints affecting the devices used in embedded and IoT systems.
For one, RTOS has excellent resource management mechanisms to ensure that it maximizes the available memory and processing power of a device. IoT devices that collect data like package tracking tags and smart locks barely have the memory and processing power to run a full-fledged OS, but they are able to operate efficiently and be part of a broader ecosystem of devices that drive big data accumulation and processing.
Another important benefit of RTOS is its multitasking support. It can run multiple tasks at a time to do more within a limited time. It can obtain, process, and transmit data concurrently instead of waiting for all data gathering before other processes can be undertaken.
Additionally, the deterministic performance of real-time operating systems ensures the timely and predictable completion of tasks, which is vital for real-time applications. Big data processing does not always require real-time processing, but there are settings where real-time operation matters. Examples of these are data-driven industrial automation, running self-driving vehicles, and financial trading.
Moreover, RTOS is known for its low latency. As mentioned, its deterministic nature makes it work according to time constraints, which not only leads to efficiency and predictability but also aligns with the requirements of real-time operations like the rapid decision-making needed in autonomous vehicles and manufacturing robots.
Lastly, RTOS is a reliable operating system for embedded and IoT systems. It is designed for resource-limited devices, so it performs tasks efficiently with little overhead and low chances of encountering system failures. OS-related crashes, freezing, and other hiccups are hurdles in big data processing, so it makes sense to use an operating system that is not as likely to glitch or fail as heavier OSes.
Fostering efficiency and reliability
RTOS plays an important role in big data processing not only because it is the most widely-used operating system in embedded systems. This OS is important because it provides the efficiency and reliability needed to enable continuous data collection and processing through low-resource devices that work together as part of the broader big data pipeline and ecosystem. RTOS takes away most of the OS-associated obstacles or challenges in advancing big data processing.
About the Author — Tim Ferguson is a tech writer and the editor of Marketing Digest. He enjoys writing about SaaS, AI, machine learning, analytics, and Big Data. He spends his free time researching the most recent technological trends. You can connect with him on LinkedIn.
Originally posted on OpenDataScience.com
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