How to Build IoT Solutions With Big Data Analytics

In the era of rapidly evolving technologies, big data analytics and Internet of Things (IoT) are the two leading revolutionary technologies which can change the domain of business operations. Both the technologies are still in its nascent stage and hold massive potential and opportunities for the future. The technologies can be coupled together for a more efficient implementation and can help all units by making smarter decisions.

More about IoT

IoT mainly offers a platform for devices to communicate effortlessly within a 'smart' environment thus making sharing of data and information a lot more convenient. It is identified as a revolutionary technology which can benefit almost all the sectors. All our surrounding devices such as home appliances and electronic equipment are now connected to an IoT network and can transfer real-time data. An IoT system typically is embedded with sensors that collect data and transfers them to a gateway. The gateway, in turn, sends the data to a processing system. Often, the gateway is a mobile phone. Mobile and electronic devices, transports, offices, home appliances all can be used as data acquisition equipment using IoT.

More about Big Data Analytics

With increased digitalization and realization of the importance of analytics, humungous volumes of data are being generated every second in almost all spheres of life. This massive data generation results in big data. Big data analytics involves examining, processing and analyzing large-scale data sets to gain insights from the data, which can be used in making predictions, identifying recent trends, finding hidden information, and ultimately, making decisions.

Need for Big Data Analytics in IoT

The exponential growth in the IoT connected devices and increased amount of data generation reflects how the two technologies can converge to yield a more efficient solution. Organizations today have a huge amount of data and they have the need to derive value and insights from it. Big data analytics can solve many IoT analytics challenges, particularly system challenges including large-scale data management, learning, processing, and data visualizations.

Moreover, previously, data storage was costlier, and there was a lack of advanced technology which could process the data in an effective manner. Now the storage costs have come down considerably and the availability of technology to transform big data is available. The application of big data technologies also accelerates the research advances and business models of IoT. So, the need for an efficient analytics system in IoT is very compelling.

The Data Pipeline Architecture

Data pipeline is a series of steps that the acquired data moves through. All data transformation happens in the data pipeline.  It consists of the following layers:

•  Data Ingestion Layer– Data coming from various sources enter this layer in the first step. Data here is smoothened and prioritized which makes data flow easier in further layers. As the number of IoT device increases, both the volume and variability of data sources expand rapidly. So, it becomes increasingly difficult to ingest data at the reasonable speed and further process it properly. Some of the data ingestion tools include Apache Flume, Stream Data etc.

•  Data Collector Layer– Here the focus is on the transportation of data from ingestion layer to rest of the data pipeline. A messaging system is used that acts as a mediator between all the programs that can send and receive messages. The tool often used here is Apache Kafka.

•  Data Processing Layer– In this layer, the primary focus is to specialize the data pipeline processing system. This is the first point where actual analysis of data takes place.

•  Data Storage Layer– This layer focuses on storage of large datasets. Storage becomes a challenge when the size of the data becomes very large. Some common tools in this layer are HDFS, Gluster file systems (GFS), Amazon S3, etc.

•  Data Query Layer– In this layer, active analytical processing takes place. This is a field where interactive queries are required, and it's a zone traditionally dominated by expert data analysts. Some tools in this layer are Apache Hive, Spark SQL, and Amazon Redshift.

•  Data Visualization Layer– In this layer the actual value of the data is felt as it deals with the presentation of insights. It provides full business infographics and findings from the data. This layer actually measures the success of the analysis. Some tools which are used for dashboards are Custom Dashboards, Real-Time Dashboards, and Tableau etc.

Requirements of an Efficient IoT Analytics System

•  It should be able to handle huge volumes and variety of data. The system should be well adapted to process millions of data sources as a number of IoT devices increase.

•  It should have good response time so that the impacted entity can be notified of impending event or failure.

•  The system should be scalable with scale on various parameters like number of devices, messages, and storage.

•  It should be diverse and be able to serve a variety of use cases.

•  It should be flexible enough to accommodate new use cases.

•  The system should be cost-effective so that the benefits of building such a system should not be neutralized by its cost.

Conclusion

So, there is an increasing need to integrate these two technologies. The interaction between IoT and big data is currently at an initial stage. In future, with more IoT devices, requiring an analytics pipeline will be absolutely necessary for any business. The amalgamation of these two technologies is in the process which will ultimately be beneficial for everyone.