What Is Big Data Impact On Your IoT Solution?

What Is Big Data Impact On Your IoT Solution?

Ask any development company what brings big data and IoT solutions together and you’ll hear something like an optimal architecture and technology stack. Today we’ll try to lay it all out in plain language.

How IoT big data influences your project?

Why is it so important to choose to store original or processed data prior to a project launch? How can you avoid braking in IoT big data readings?

We cover all these and other questions about the special IoT big data type below.

And first...

What differs IoT big data from other data types?

Statistically speaking, each day we send 2,5 quintillion bytes on average. That’s not just more than we can process but even more, than we can simply manage. Meanwhile, the number of connected devices is claimed to triple by 2025.

Probably best not to think too much about the numbers but take into account the fact that both IoT and big data are at their peak of growth. And the tendency of equating the two notions triggered the emergence of a new type known today as IoT big data.

What differs from the new data type from others?

The IoT big data is generated by devices able to communicate;

Hence, the data mostly presents a stream of numbers rather than consciousness;

This is a large volume of constantly streaming data;

The showings extracted from the big data should mostly be real-time;

Besides, the IoT data is multifactorial and stems from not only time but also location.

Given the specific characteristics of IoT big data, you need to see to it that a development company you hire can design the solution with all that suitable technology stuffing. So, let’s dig deeper.

IoT big data analysis

The analysis is the latest stage of work with any raw data, basically, for this reason, we gather it with all those IoT sensors and communicators. Long before we proceed with the procedure and get the desired results, we need to decide on the two preceding stages that are storage and processing. And first, let’s learn what pitfalls to avoid here.

#1. Storage

As you remember, one of the main features of the IoT big data is that we receive a constant stream of location- and time-enabled high volume data. The problem is you can’t simply rely on universal data warehouses. What you need is both a big data warehouse and a data lake.

At first (a) the raw data gets to the landing zone of your split data lake, after that (b) the data transfers to the staging zone where it’s filtered, and then (c) the sorted data gets to the analytics sandbox where it’s further studied and evaluated. Right after the data lake your (d) IoT big data goes to a big data warehouse where it’s transformed and further structured.

#2. Processing

In the intro we gave a task that you have to complete before launching a new IoT project - choose to store original or processed data.

Let’s take an example. Say, you have 10 communicators that transfer 10 slightly different pressure values per second. Here you have to decide whether to save only one average value or all the 10 showings for every second. The decision influences the storage capacity of your future IoT solution.

But say, you want to get real-time results before sending the values to the sandbox. In this case, you can set up triggers that will facilitate your alerting if an anomaly value emerges.

One more big problem we need to cover here is the possibility of a data loss.

How to avoid data losses?

Say your communicators lost connection with the gateway, what then?

To keep your data safe in such cases, pay attention to algorithms and additional communicators.

Complex algorithms are resistant to data losses and...  

Precautionary communicators still measure the needed showing in case the ‘partner’ collapses.

#3. Analyzing

For analyzing the big data for IoT we’ll need batch and streaming.

As you know, batch analytics is common for other big data types as well, so there’s nothing new. We use the type to identify tendencies, correlations between values, patterns, and their dependencies with complex algorithms applied to the stored data.

Streaming type is designed to work with all the IoT big data specifics we described above. Why? It’s because it can deal with high volume data and give nearly real-time results.   


The influence of big data and the internet of things solutions on global technological progress is undeniable. This, in turn, gave birth to such a notion as IoT big data. Nobody would distinguish the data from a separate type if it has no peculiarities. Today we see that this data require another approach in its storing, processing, and analyzing. This is due to its high volume of data reflected in a continuous stream of values dependent on time and location. We hope the simple hints will help you hire a high-quality IoT development company that knows all the nuances. For example, the development company Sumatosoft.

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