What impact does big data have on your IoT solution?

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What impact does big data have on your IoT solution?

Ask any software vendor what Big Data and IoT solutions have in common and you’ll hear something like the optimal architecture and technology stack. Today we will try to explain everything in simple language.

How does Big IoT Data affect your project?

Why is the decision between archiving original or processed data so important before starting a project? How can bottlenecks in IoT big data reading be avoided?

Below we discuss all these and other questions related to a specific type of IoT big data.

And before…

How is IoT Big Data different from other data types?

Statistically, we send an average of 2.5 trillion bytes per day. This is not only more than we can handle, but also more than we can easily handle. At the same time, the number of connected devices is expected to triple by 2025.

It’s probably best not to think too much about the numbers, but to consider the fact that IoT and big data are peaking in growth. And the tendency to equate these two concepts has led to the emergence of a new type known today as IoT Big Data.

How is the new data type different from the others?

IoT big data are generated by devices that can communicate;

Thus data represents a stream of numbers rather than consciousness;

This is a large amount of data that is continuously transmitted;

The emissions extracted from big data should mainly be real-time;

Furthermore, IoT data is multifactorial and not only from time but also from location.

Given the big data nature of the IoT, you need to ensure that the software company you hire can build a solution with all the right technology elements. So let’s move on.

IoT Big Data Analytics

Analytics is the last step when working with the raw data, which is why we collect it with all these IoT sensors and communicators. Before proceeding with the treatment and obtaining the desired effects, the two previous phases, namely conservation and transformation, must be decided. First, let’s look at the pitfalls to avoid here.

No. 1. Storage

As you may recall, one of the main characteristics of IoT Big Data is that we receive a constant stream of large amounts of data in terms of location and time. The problem is that you can’t rely on universal data warehouses alone. You need both a big data warehouse and a data lake.

First (a) the raw data is sent to the split data lake destination zone, then (b) the data is sent to the staging zone where it is filtered, and then (c) the sorted data is sent to the analytics -Sent to sandbox, where they are then examined and evaluated. Immediately after the data lake (d), the IoT big data enters the big data warehouse, where it is transformed and further structured.

No. 2. Processing

In the introduction, we mentioned a task that needs to be done before starting a new IoT project: choose whether to store raw data or processed data.

Let’s take an example. Let’s say you have 10 communicators transmitting 10 slightly different pressure readings per second. Here you have to decide whether you want to collect a single average value per second or all 10 emissions. The decision affects the storage capacity of your future IoT solution.

But let’s say you want live results before sending the values ​​to the sandbox. In this case, you can configure triggers that warn you of an outlier value.

Another important issue that we need to discuss here is the possibility of data loss.

How to avoid data loss?

Let’s say the communicators lost connection to the gateway, so what?

To protect your data in such cases, pay attention to additional algorithms and messengers.

Complex algorithms are immune to data loss and…

Preventive communicators continue to measure the necessary indication in the event of a fall of the “partner”.