5 Technologies You Need to Build an IoT Application

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5 Technologies You Need to Build an IoT Application

With data coming in from so many sources, building an Internet of things application isn't easy. However, these five technologies may help.

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Computation (Spark, Impala, Storm, etc.)

IoT applications run on distributed infrastructures. Clusters of servers are responsible for ingesting, analyzing and storing large amounts of data from groups of connected devices. As a result, IoT applications need computational tools that provide real-time parallel processing for distributed datasets. These tools, such as Apache Spark, Apache Storm and Impala, allow developers to access continuous streams of data and run queries on it quickly and in a scalable manner. Computation solutions enable faster analysis, even on large amounts of information.

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Database (Riak, Cassandra, MongoDB)

In the IoT world, databases must not only be able to handle constant reads and writes, but they must also complement the computational tools in the application stack. The database has to be able to provide strong query and retrieval capabilities that allow the rest of the tools in the stack to operate efficiently. Most importantly, databases for an IoT application have to be built specifically to write and query IoT data. Millions of sensors generate petabytes of data that companies want to take advantage of. The database that provides the spine of the architecture must scale and always be available without unnecessarily increasing latency or suffering from data degradation.

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Queuing System (RabbitMQ and Kafka)

IoT applications must deal with massive amounts of data generated by sensors and devices. The data is sent either in real time or batches—usually at some point in the chain of infrastructure it must be batched. Storage and analytics has a limit on how much can be dealt with in real time versus what should be done in near time. Queuing becomes important when dealing with high volumes of data across applications or when trying to combine many disparate data sources. Kafka and RabbitMQ are popular examples of queuing solutions used to solve this problem.

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Application Frameworks (Akka and Spring)

Frameworks offer developers simplicity. Solutions like Akka and Spring help to manage complexity so developers can focus on more important tasks. These frameworks are especially prevalent in companies that are focused on high productivity and where noticeable application downtime can render the business immobile. Akka, for example, is especially good at managing failure, even when a million different actions are taking place simultaneously, such as sudden activity spikes on a series of services handling IoT data.

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Resource Management/Operating Environment (Mesos)

IoT applications are resource-intensive undertakings. As application user bases grow, their infrastructures must grow with them or the apps will stop working. Think Pokemon Go's spate of network outages. Tools like Mesos give companies a better grasp of their resource usage to help predict when it has to scale. With complex, distributed systems, resource management can be a difficult task to accomplish effectively. Mesos, sometimes considered a data center OS, can help lower infrastructure costs and make important processes like scaling distributed workloads much easier and more cost-effective.

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10 BI Mistakes That Could Be Killing Your Analytics Projects

A business intelligence implementation can be difficult for a company of any size. Not only are there new tools to learn, but there are new processes to put in place and new strategies to consider. The ultimate goal, of course, is to gain greater depth of knowledge to inform faster, more efficient decision making. Implemented correctly, an analytics-driven environment will speed workflows and optimize business as a whole. Companies shouldn't only invest in the business intelligence and analytics platform itself, but in new strategies to ensure high user adoption among those who would benefit most from business intelligence. Just as important as adopting new best practices, though, is knowing which pitfalls to dodge along the way. This eWEEK slide show, based on insight from Mads C. Brink Hansen, BI project manager at TARGIT Business Intelligence, illustrates how avoiding these 10 common business...
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