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IoT creates an opportunity to measure, collect and analyze an ever-increasing variety of behavioral statistics
Nov. 23, 2017 11:00 AM
Accelerating IoT Deployments to Achieve Business Goals Faster
The "Internet of Things" is an exciting area of tech, one in which industry experts estimate there will be more than 30 billion connected IoT devices by 2020. IoT is the inter-networking and instrumentation of physical devices - everything from streets, cars, factories, power grids, ice caps, satellites, and clothing to phones, microwaves, milk containers, planets, human bodies, etc.
IoT creates an opportunity to measure, collect and analyze an ever-increasing variety of behavioral statistics. That being said, data, and more importantly insight into the data, is key for enhanced business value and achieving goals quickly. The proliferation of IoT devices and the sheer volume of large data streams have changed the data processing characteristics that we were accustomed to, forcing new architectures and infrastructure that are designed for streaming data, real-time insights, and real-time analytics.
In order to gain real-time insights to facilitate automation, data about these devices needs to be collected and analyzed with the help of various technologies making up IoT Architecture. For example, by providing IoT monitoring and control companies can optimize plant safety and security, as well as extend into asset management allowing forpredictive maintenance thatdrives efficiencies and maximizes reliability.
Another example of IoT is home automation (also known as smart home devices) to control and automate lighting, heating, air conditioning and household appliances. IoT in infrastructure management monitors and controls urban and rural solar panels, railway tracks, wind-farms, and manufacturing.
Today's modern applications require action. For developers, in order to take appropriate action given the type of data they are working with, it's becoming essential to have an IoT data platform that can provide a set of tools and services to get metrics and events data from sensors, devices, systems, machines, containers, and applications. With an IoT data platform, developers can closely monitor and analyze data for collecting, normalizing, detecting events, as well as storing, managing and automating the entire system.
Given the potential for mixed workloads and data requirements, and to fully evaluate and manipulate IoT data, companies need to consider an IoT platform that specializes in handling a high volume of writes and queries over large and changing data sets. In addition, consideration of storing time stamped data - and looking at changes over a period of time - has to be taken into account. This leads to using a Time Series Database (TSDB), which makes sense because IoT data is time series data and modern TSDBs are built specifically for handling metrics and events or measurements that are time-stamped. A TSDB is optimized for measuring change over time, which is important because time series data, including IoT data, is very different than other data workloads because it must collect data lifecycle management, summarization and large range scans of many records.
Regarding analytics, there are specific kinds of analytics that are looking at real-time change over time - and for deriving insights from these changes - a TSDB is critical. Time series data such as IoT data comes in two forms: traditional regular (metrics) and irregular (events). Both data forms are present in IoT data. Today's more advanced TSDB platforms are optimized for both regular (example a sensor sending temperature readings every millisecond) and irregular time series data (for example a sensor sending pressure data only if it is above 1000psi). In addition, this class of TSDBs has evolved their data model over earlier time series solutions and have no limits on the number of tags and fields that can be used. This allows timestamp precision in nanoseconds, which is important as sub-millisecond operations become more common with IoT architectures.
Developers also need the ability to perform correlation, aggregation, and pattern detection of the streaming data before it gets to the database - in short the delivery of streaming analytics. They also need the ability to visualize real-time data and set up notification, control and action services to automate the entire IoT system. This can all be delivered by today's advanced TSDB platforms.
Using TSDB platform to help solve the business goals of developing an IoT application - there are three steps that are critical for success:
Developers need to accumulate a comprehensive set of tools and services to get metrics and events data from sensors, devices, systems, machines, containers, and applications. With an open source solution, users can access a number of integrations to popular databases, containers, services, applications, and other monitoring and alerting products.
With the right platform, developers can analyze and access real-time stream processing of the data and storage of the time-series data. They are able to graph and visualize data and perform ad hoc exploration of data as needed.
Today's modern applications require actions. Using plugin custom logic or user-defined functions, developers can process alerts with dynamic thresholds, match metrics for patterns or compute statistical anomalies, automatically scale containers, and basically do anything that can programmed.
The world of IoT seems limitless. Developers need to have visibility into all aspects of their data in real time to help meet the demands of even the largest monitoring and IoT deployments.