To better learn how Big Data and IoT applications are related, let's start with a definition for each. Then we'll take a look at how they work together and offer some practical examples so you can see Big Data for IoT in action.

First, let's take a look at the Internet of Things.

What Is The Internet of Things (IoT)?

To put it simply, the Internet of Things, or IoT, is the name for an aggregate of networked devices. IoT devices can range from personal smart wear like Apple Watches or Fitbits to a full-sized manufacturing plant.

To truly understand how IoT and Big Data work together, it helps to have an idea of how IoT technology usually works. The Internet of Things has two main components—transmitters and receivers. A transmitter sends data, which is then picked up and interpreted by a sensor on the receiving end.

Many devices can do both, depending on the application.

To help you visualize IoT in action, think of an old analog television (the kind with the rabbit ear antennae). The broadcasting station would be the transmitting device in this IoT example.

The rabbit ear antennae would be the sensor.

What Is Big Data?

In our previous example, Big Data would be the television signal on the airwaves. Imagine having a complete log of every pixel that entered that device. You could keep track of what everyone in the household was watching at a given time.

You could measure what programs were more popular than others.

A simple definition of Big Data is "the large, diverse sets of information that grow at ever-increasing rates.” In today's information-heavy, data-centric world—with all of its dashboards and performance reports—it's not hard to imagine Big Data on a conceptual level.

In fact, it might be harder to imagine doing business without Big Data.

Big Data is primarily concerned with “the three v's”—volume, velocity, and variety. This hints at some of the issues that start to arise with data processing for IoT.

We might all be accustomed to looking at and using Big Data, but we're not all equipped for IoT data processing. How do you handle a continual stream of data coming from many disparate sources in countless different formats?

These questions are the heart of Big Data for IoT.

Now let's put the pieces together and examine Big Data for IoT work together specifically. First, someone needs to install some sensors.

Then you'll need to decide what devices you're collecting data from.

Once the transmitters and receivers are decided upon, you're essentially up and running with Big Data and IoT. Except that there's a lot more to it than that.

First of all, the Internet of Things can produce a lot of data. It's called Big Data for a reason. Although all data is persisted, you've got to decide on what parts of the data you're going to look at and, ideally, what you're looking for to be able to use Big Data in any practical way.

The alternative would be like trying to take a sip from a firehose when you're a little dehydrated.

To fine-tune this process, we recommend doing some introspection about what you hope to achieve with IoT data processing before you even begin. This will help you narrow down what data you're examining and what you're looking for.

Examples of Big Data for IoT

Imagine you run a manufacturing plant, and you're wanting to optimize your productivity. Obviously, you're going to monitor output levels—that's a given.

You'll also want to monitor for downtime, as well.

Things get interesting when you begin looking at the reasons for unexpected downtime, however. Big Data might reveal climbing temperatures, for instance. This could help prevent downtime in-and-of-itself, as you could pause a machine for maintenance when you notice the temperature begin to rise.

The causes aren't always going to be that obvious, unfortunately. Grime might be accumulating on rollers, for instance. You'd need to figure out ways that you could measure and monitor for that, which is where the science of Big Data begins to become an art form.

Keep in mind that Big Data for IoT deals with a large volume of data. You'll need to figure out some sort of storage solution, for one thing, whether that's locally-based or on the cloud. However, cloud-based storage solutions are widely-used nowadays, as many real-time analytic programs rely upon connected data.

Cloud-based storage solutions also enable easy file-sharing and collaboration across your entire enterprise.

You'll also need to make sure your connectivity is up to the task. Otherwise, you might experience downtime, which could have potentially disastrous results depending on what you're doing with your data.

Big Data and the Internet of Things are both enormous industries. There's a truly incredible array of powerful tools available, as a result. These can range from dedicated software solutions to low-level command-line tools.

Here are a few of the most common Big Data tools for IoT.

Apache Kafka

Apache products are well-known and loved through the Big Data community, and Apache Kafka is no exception. Apache Kafka is a dedicated software library and open-source distributed event streaming platform that was designed for handling huge amounts of data.

With its distributed architecture and its publish/subscribe pattern, Kafka is ideally suited for receiving real-time messages from many IoT devices and forwarding them to other applications or permanently persisting them, even on a large scale.

Since Kafka is open-source and widely used especially for ingesting and processing IoT data, it provides enterprise features like scalability, permanent storage of events and high availability out of the box.

Seeing as how Kafka is so popular in the Big Data community, there are a ton of great tools available for it as well. It integrates easily with MQTT, the default protocol for transferring IoT data, for instance. An often used cloud-native alternative is Azure IoT Hub, which offers similar functionality fully managed by Azure.

Apache Spark

Apache Spark is another reason Apache products are so popular in the Big Data industry. Like Kafka, Apache Spark is designed for working with large volumes of data. While Kafka is used for ingesting and combining data from many sources, for instance, IoT devices, to a central place Spark is focusing on processing Big Data efficiently.

It conducts processing tasks, as for instance aggregating and combining data or building machine learning models from IoT data, across multiple machines. This makes it ideal for today's containerized world of distributed computing. It also means that you and your team can both analyze and access these analytic tools from anywhere you can get a signal.

It's also built for decentralized computing. It's worth investigating Apache Spark solely for its powerful API, alone.

TimescaleDB and Blob Storage

Last but not least, if you're going to be working with Big Data, you'll need some place to store it. TimescaleDB is particularly well-suited for working with IoT data. It's built on top of PostgreSQL, one of the most powerful relational database systems, and is specifically designed to store time-series data, such as IoT data, for analytical purposes.

It offers full SQL support, can be scaled to store petabytes of data and is available as open-source to host it on your own as well as a managed cloud service.

Especially for batch-processing IoT data, a good alternative—or addition—to TimescaleDB is to persist the data in a Blob Storage. Storing data in a blob storage is cheap, easy to manage and performant, especially when using data formats created for analytical use cases like Apache Parquet.

Not everybody is a data engineer. Or a computer programmer, for that matter. Data-intensive systems like Big Data and the IoT need to be set up properly to yield the most benefits for you and your organization.

If you're ready to find out how to make the most of your IoT applications with Big Data, get in touch with us today to find out about our many data-centric products and services.