The IoT landscape is seeing an explosion of connected devices. According to IDC, by 2025, more than a quarter of data created in the global datasphere will be real-time in nature and real-time IoT data will make up more than 95% of this.

This fact just adds to the challenge and the urgency businesses already face with implementing IoT. Cisco commissioned a study last year that reported 75% of IoT projects fail. So the fact that data from a burgeoning array of IoT sensors, actuators and other devices are getting larger and faster just raises the stakes.

Businesses will need to understand the challenges of implementing IoT in general, and also with their specific circumstances and requirements (they will need to think big, start small, and move fast). Security, latency requirements, bandwidth, and many other factors will come into play.

As data becomes increasingly available, it needs to be instantly accessible and organisations need to find ways to manage this potential data overload to drive intelligence.

Edge computing is inevitable for the success and growth of emerging low latency IoT applications

Organisations should focus on building a solid data management foundation – understanding what data is being collected, where it is stored and secured, and how quickly it is accessible – as they embark on their IoT journey.

Given the large and fast data and need for real-time processing, they will most likely need a hybrid deployment with both edge and cloud processing, including a blazing fast database.

Managing latency requirements

With IoT implementations becoming more widespread, it has led to the dawn of a fog computing era – implementing cloud principles at the network edge. Devices will generate a lot of data at the end of the network and many applications will be deployed at the edge to process the information, simply because cloud computing models, as a silo, cannot satisfy the needs of IoT applications.

Edge computing is inevitable for the success and growth of emerging low latency IoT applications. IoT applications have stringent latency requirements and must be tightly controlled for these emerging modern applications. This is due to the large communication latency between users and their closest data centre (up to 100ms). This latency problem is exacerbated for applications that serve users across large geographical areas. Proximity to data matters and processing data at the edge will lead to shorter response times, more efficient processing and less pressure on the network.

Distributed IoT solutions are best implemented with a combination of both fog computing at the edge and cloud computing

However, rather than edge locations being used for data caching only, the benefit is maximized when the data management components allow manipulation and querying of local edge partition at extremely high speeds as well. The hybrid solution of fog and cloud computing operating in tandem is the future of IoT.

Investing in the edge

There is a massive trend and investment toward edge computing to handle a wide variety of IoT use cases. Look no further than Microsoft’s announcement a few months ago that they are investing $5 billion in Azure IoT Edge. Also, look at Google’s announcement last month at Next with their own GCP IoT Edge Core and TPU and Amazon’s large investment in AWS Greengrass for IoT Edge.

In Gartner’s first ever 2018 Magic Quadrant for Industrial IoT (IIoT), they reported: “By 2020, on-premises Internet of Things (IoT) platforms coupled with edge computing will account for up to 60% of industrial IoT (IIoT) analytics, up from less than 10% today.”

Tackling the roadblocks

Edge is the destiny for IoT futures, but there are definitely hurdles to overcome. Security will be the number one challenge for the foreseeable future. Also, the resources to implement cloud principles at the edge (like machine learning and other AI concepts) require compute and storage resources only reasonably existing in core data centre or cloud environments, which is why the edge (or more broadly fog) needs to operate in tandem with the cloud. For example, we can train ML models in the cloud and deploy the trained models at the edge.

Distributed IoT solutions are best implemented with a combination of both fog computing at the edge and cloud computing. Critical applications that collect large volumes of data and rely on low latency are getting pushed towards the edge, yet there’s a need to connect the edge servers with the cloud.

The right database platforms can offer the flexibility to process the data at the edge and synchronize between the edge servers and the cloud while minimizing latency as data is disseminated across this hybrid architecture.

Fog computing with machine learning, deep learning and other AI technologies will be table stakes for IoT edge applications

In addition to that, given edge devices are typically resource constrained, the database must be lean, and when the edge device lacks persistence, operate entirely in memory. Given the volume of data being generated in a short amount of time, the overall system must be designed for fast data ingestion and demonstrate the ability to deliver superior performance while scaling.

Transition to maturity

Over the next few years, IoT will fully transition from an emerging, nascent consumer and industrial need to a mature industry norm across virtually all verticals.

This will be fueled by many factors, perhaps most notably by the broad deployment and roll-out of IoT-friendly networking capabilities like 5G and low-power networking (LPWA – Sigfox, LoRa, NB-IoT, etc.)

Major packages will also progress with the integration of Information Technology (IT) and Operational Technology (OT), which along with security, is the holy grail for Industrial IoT (IIoT). Industry 4.0 (OT) and serverless computing (IT – microservices, containerized applications, etc.) will be key enablers for this improved integration.

Fog computing with machine learning, deep learning and other AI technologies will be table stakes for IoT edge applications, and fog and cloud computing will operate seamlessly in tandem to optimize resources while delivering on critical latency and security requirements.


Redis Enterprise is an ideal database for the ‘intelligent edge’ delivering blazing fast performance with the ability to ingest millions of writes per second with <1ms latency at the IoT edge. It can do this with a small hardware and software footprint, so it is perfectly suited to live on fog nodes, edge gateway devices and even IoT devices. Redis Enterprise has many native data structures (sets, sorted sets, lists, hashes, streams, etc.), providing ultimate flexibility for IoT application developers.