Big data analytics is emerging as a key IoT initiative. One of the most important features of IoT is its analysis of information about connected things. Big data analytics in IoT demands to process a huge amount of data and store the data in the storage. Sensors send gigabytes of data in an hour. The real challenge is to store, manage and derive value from these sensors data. Big data technologies will help in storing the unstructured data, process it and deduce insights valuable for business purposes. IoT implementation goal is to increase revenue, improve productivity, efficiency & the lower cost to businesses. All these can be achieved only if there is a proper strategy to harness value out of the data it generates. This allows organizations to gain rapid insights, make quick decisions, and interact with people with devices. The need to adopt big data in IoT applications already been recognized in the fields of IT and business. Although, the development is lagging, these technologies are inter-dependent and mayd be jointly developed in future.
Numerous solutions have enabled to obtain insight into large data generated by the devices. These solutions still in infancy as the domain lacks proper definition. IoT data analytics is definitely a future happening. Different types of analytics are used according to the requirements of IoT applications and divided into real-time, off-line, memory-level, business intelligence (BI) level, and massive level analytics categories.
Tools for Big Data Analysis in IoT
Apache Spark (and Spark alternatives) : Usage of Apache Spark in IoT is in data analysis pipeline, where real-time streams are collected from edge devices, gateways, and then processed by Spark Streaming applications, which in turn generate derived streams for further processing, data aggregates, or trigger other real-time events. Apache Spark has the capability of using similar code for both stream and batch processing can simplify a number of data management issues. As discussed in the article Apache Spark Alternatives, Apache Flink, Apache Apex, Apache Beam, Apache Gearpump, Apache Samza, Apache Kylin etc. For real-time in-memory processing, Apache Ignite is definitely better option. For fast SQL analytics Apache Drill provides similar performance to Spark SQL. For stream processing Apache Beam can overperform compared to Apache Flink.
Apache NiFi, MiniFi : Apache NiFi 1.7+ and MiniFi 0.5+ for IoT data ingestion, simple event processing, conversion, data processing, data flow, and storage.
Apache Cassandra : Apache Cassandra’s distributed architecture ensures availability.
Of course other common tools such as Apache Hadoop itself, Apache Pig also used in IoT. The present topic is quite difficult to easily point “just some software” which specifically used for IoT Big Data analysis but probably helpful to the new to Big Data. Real life usage can be like below :
IoT Connected Vehicles and Apache Spark > Application to generate IoT Data events using Apache Kafka > Application to process IoT Data Streams using Spark Streaming > Process and transform IoTData events into specific count > Build IoT Data Monitoring Dashboard using Spring Boot, Web socket, jQuery, Sockjs and Bootstrap.Tagged With apache iot database , iot and big data analysis