How Big Data Influences Your IoT Solution

The number of Internet connected devices is projected to triple by 2025. Correspondingly, IoT is joining the line of important big data sources. This makes data practitioners turn their attention to IoT big data.

The nature of IoT big data

IoT big data is distinctly different from other big data types. To form a clear picture, imagine a network of sensors that continuously generate data. In manufacturing, for example, it can be the temperature values of a particular machinery part, as well as vibration, lubrication, humidity, pressure and more. So, IoT big data is machine-generated, not created by humans. And it mainly represents the flow of numbers, not chunks of text.

Now, imagine that each sensor produces 5 measurements per second and, overall, you have 1,000 sensors installed. And this high-volume data is incessantly flowing (by the way, such data has a special name – streaming data). Definitely, pure data collection is not your ultimate goal – you need valuable insights, some of which as close as possible to real-time. If the pressure starts suddenly plunging to the critical level, you won’t be happy to know about this only in a couple of hours. By that time, your maintenance team might have already been trying to repair a broken machinery unit.

Besides, IoT data is location and time specific. While examples can be numerous, here we’ll mention only a couple: location data is critical to understand which of the sensors communicates the readings that are likely to signal an upcoming failure, while a timestamp is essential to identify a particular pattern that is likely to cause a machinery breakdown. For instance, every ten seconds a temperature value increases by 5 F still without surpassing a threshold, which leads to increasing pressure by 1,000 Pa for one minute.

Storage, preprocessing and analysis of IoT big data

Of course, it’s your business objectives that always lay the foundation for the solution’s architecture. Still, the nature of IoT big data leaves its mark on data storage, preprocessing and analysis. So, let’s take a closer look at the specific features of each process.

IoT big data storage

As you’ll have to deal with high volumes of quickly arriving structured and unstructured data in different formats, a traditional data warehouse will not meet your requirements – you need a data lake and a big data warehouse. A data lake may be split into several zones such as a landing zone (for raw data in their original format), a staging zone (for the data after a basic cleaning and filtering applied and for raw data from other data sources), as well as analytics sandbox (for data science and exploratory activities). A big data warehouse is required to extract the data from a data lake, transform it and store in a more organized way.

IoT big data preprocessing

It’s important to decide whether you would like to store raw or already preprocessed data. In fact, answering this question right is one of the challenges connected to IoT big data. Let’s return to our example with a sensor that communicates 5 temperature values per second. One option is to store all 5 readings, while the other is to store only one value such as their average/median/mode per aggregation period of one second. To clearly visualize what difference such an approach makes to the required storage capacity, you should multiply the overall number of sensors by their expected running time and then by their reading frequency.  

If you belong to 70% of the organizations that value managing data in real time, and a part of your plan is getting real-time insights, it’s still possible to have real-time alerts without sending all the readings to the data storage. For example, your system is able to ingest the whole flow of data, and you’ve set critical thresholds or deviations that trigger instant alerts. Still, only some filtered or compressed data is sent to the data storage.

Ways to avoid data losses

It’s also necessary to think in advance what if the flow of readings stops for some reason, let’s say due to a temporary failure of a sensor or a loss of its connection with the gateway.

Here, two approaches are possible:

• Using robust algorithms that are reliable to data omissions.
• Using redundant sensors, for example, having several sensors to measure the same parameter. On the one hand, this increases reliability: if one sensor fails, the others will continue sending their readings. On the other hand, this approach requires more complicated analytics, as the sensors may generate slightly different values what should be processed by analytical algorithms.

IoT big data analysis

IoT big data demands two types of analytics: batch and streaming. Batch analytics is inherent in all big data types, and IoT big data is not an exception. It is widely used to run a complex analysis on the captured data to identify trends, correlations, patterns and dependencies. Batch analytics involves sophisticated algorithms and statistical models applied to historical data.

Streaming analytics perfectly covers all the specifics of IoT big data. It is designed to deal with high-speed flows of data generated within small time intervals and to provide near real-time insights. For different systems, this 'real-time' parameter will vary. In some cases, it can be measured in milliseconds, while in others – in several minutes. To get insights as fast as possible, the captured data can be analyzed at the system’s edge or even in a data streaming processor.

To sum it up

By nature, IoT big data is machine-generated, high-volume, streaming, location and time specific. Big data consulting practice proves how important it is to have these features considered prior to designing and developing an IoT solution. We are sure that you don’t want to run out of storage space in just a couple of months, or miss real-time insights just because your solution does not support streaming analytics, or face any other problem that undermines the robustness of your IoT solution. To avoid this, it’s necessary to clearly identify your short-term and long-term business requirements, as well as carefully choose an optimal big data architecture and technology stack from multiple options.