The Internet of Things (IoT) is on its way to becoming the next technological revolution. According to Gartner, revenue generated from IoT products and services will exceed $300 billion in 2020, and that probably is just the tip of the iceberg.
Given the massive amount of revenue and data that the IoT will generate, its impact will be felt across the entire big data universe, forcing companies to upgrade current tools and processes, and technology to evolve to accommodate this additional data volume and take advantage of the insights all this new data undoubtedly will deliver.
Let’s take a closer look at the various ways in which the IoT will impact big data.
When we talk about IoT, one of the first things that comes to mind is a huge, continuous stream of data hitting companies’ data storage. Data centers must be equipped to handle this additional load of heterogeneous data.
In response to this direct impact on big data storage infrastructure, many organizations are moving toward the Platform as a Service (PaaS) model instead of keeping their own storage infrastructure, which would require continuous expansion to handle the load of big data. PaaS is a cloud-based, managed solution that provides scalability, flexibility, compliance, and a sophisticated architecture to store valuable IoT data.
Cloud storage options include private, public, and hybrid models. If companies have sensitive data or data that is subject to regulatory compliance requirements that require heightened security, a private cloud model might be the best fit. Otherwise, a public or hybrid model can be chosen as storage for IoT data.
Big Data Technologies
When selecting the technology stack for big data processing, the tremendous influx of data that the IoT will deliver must be kept in mind. Organizations will have to adapt technologies to map with IoT data. Network, disk, and compute power all will be impacted and should be planned to take care of this new type of data.
From a technology perspective, the most important thing is to receive events from IoT-connected devices. The devices can be connected to the network using Wi-Fi, Bluetooth, or another technology, but must be able to send messages to a broker using some well-defined protocol. One of the most popular and widely used protocols is Message Queue Telemetry Transport (MQTT). Mosquitto is a popular open-source MQTT broker.
Once the data is received, the next consideration is the technology platform to store the IoT data. Many companies use Hadoop and Hive to store big data. But for IoT data, NoSQL document databases like Apache CouchDB are more suitable because they offer high throughput and very low latency. These types of databases are schema-less, which supports the flexibility to add new event types easily. Other popular IoT tools are Apache Kafka for intermediate message brokering and Apache Storm for real-time stream processing.