IoT is transforming our world - the way machines interact with humans and other machines. Today, organizations in all industries are looking to leverage data originating from sensors with machine learning, analytics, and artificial intelligence to improve operations, become more agile, and to capture next-generation business opportunities. With IoT applications grow in size and popularity, physical sensor networks are more often running multiple complex applications. The technologies are new, systems are complex, security concerns are huge and the path to realizing benefits is uncertain.

Challenges of growing data in IoT

Businesses across industries are adopting the Internet of Things at a rapid pace but challenges surmount. With the explosion of sensors-adds a lot of challenges in how to deal with all of these simultaneously connected devices producing lots of data to be retrieved, aggregated, analyzed and pushed back out the devices while maintaining responsiveness.

Challenges of scaling up includes:

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    Managing huge bursts in traffic in receiving sensor data at peak times

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    Processing of large amounts of data in both batch processes and real-time

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    Doing massive simulations, simulating real-world usage patterns

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    Back end IOT services also require to manage the devices, not just absorb the data sent from the devices

The back-end systems managing all this needs to be able to scale on demand and be fully resilient. This is a perfect fit for reactive architectures.

Scaling the Internet Of Things with Scala and Rust

Knoldus engineering expertise from sensors to insights, an extensive Internet of Things (IoT) ecosystem and a practical approach towards IoT-led digital transformation, help clients to realize tangible and significant benefits from IoT. With an end-to-end reactive architecture that addresses enterprise-level, IoT needs and help you efficiently deliver and manage highly secure, reliable, and scalable IoT solutions. We are making it easier for organizations by providing a validated, modular, flexible architecture built to be open, interoperable, and cost-effective.

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End-to-End IoT Architecture to capture, process, and analyze data from connected devices, as well as ship machine learning models and intelligence back to the edge

IOT With Scala

Today, IoT devices generate a massive amount of data, which are extremely varied, noisy, time-sensitive and often confidential. In many scenarios, these data need to be processed in real-time to detect anomalies, filter events, enrich the data for machine learning and prediction. This requires an asynchronous, non-blocking, fully back-pressured architecture.

Knoldus helps organizations with end-to-end scalable IoT systems. We connect millions of devices with a reactive model that copes with the information flow.

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Akka Actors have delivery guarantees and isolation properties that are perfect for the IoT world, making it a great tool for simulating millions of concurrently connected sensors producing real-time data. Scala and Akka provide comprehensive features in actor routing, clustering, sharding, and persistence to address the real-world need of scalability as well as fault-tolerance.

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Both play a really important role in keeping up with inbound rates and managing overflow so that there are proper data bulkheads in IoT systems.

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With Apache Spark, you can analyze the vehicle telematics data, detect anomalies if any, and trigger a corresponding action back to the vehicle in real-time.

IOT With Rust

As the number of connected devices increases, known as the Internet of Things (IoT), reliability and security become more important. C and C++ are common programming languages for IoT devices, mostly due to their fine-grained memory management and low runtime overhead. As these languages are not memory-safe and memory management has proven to be difficult to handle correctly, problems such as buffer overflows and dangling pointers are likely to increase as the IoT devices become more prevalent. These problems can result in reliability and security issues.

In contrast to C and C++, Rust is a memory-safe systems programming language that, just like C and C++, provides low runtime overhead and fine-grained memory management. Potentially, Rust can reduce the number of unreliable and insecure connected devices as a result of memory safety.

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Knoldus help you to uncover actionable insight, using the right tools and technology, at the right time, to help a company achieve its most desired business outcomes.

Transform Your Industry with IoT

IoT is being used to solve specific industry challenges - interactive use case spotlights.

  • knoldus-monitor-icon Automotive

    Vehicle telematics to provide fleet management and operational efficiency

  • knoldus-chart-settig Healthcare & Medical Devices

    From diagnostics to patient care, IoT systems are revolutionizing healthcare. Leverage IoT-driven data to create groundbreaking customer engagement programs and research models.

  • knoldus-simulation-icon Mining

    Real-time monitoring of mine equipment, management of assets, worker health and safety

  • knoldus maintaince-icon Retail

    Supply chain optimization, along with real-time situational awareness to improve customer experience and returns management

  • knoldus maintaince-icon Insurance

    Gather the digital information you need to develop new, innovative business models. Improve your ability to evaluate and manage risk, mitigate costs, and increase profitability.

  • knoldus maintaince-icon Construction

    Use IoT devices for predictive maintenance of your facilities. Take advantage of IoT sensor data for real-time, condition-based maintenance of facility assets and enhanced situational awareness at construction sites.

How we did it before

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Case Study

Hewlett Packard Enterprise

Knoldus helps HPE not only build customer value but also gain momentum for analytics transformation.

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Case Study


Philips Cares digitizes the aging and caregiving platform with the help of Knoldus API solution.

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Case Study


Explore how Decooda support the streaming, real-time or batch processing of extremely large datasets

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