IoT Data Analytics Tools

IoT solutions find their use-cases across various industries - logistics, warehouse monitoring, manufacturing, quality management, facility management, vehicles in transit, etc. The volume of IoT sensor data is growing each day along with the need to analyze and gather insights from them [10,11]. As an organization, one needs a robust IoT analytics solution to analyze both historical and real-time data. There are many existing tools to analyze our data. Some of them are discussed below:

i. Microsoft Azure Stream Analytics: Microsoft’s azure stream analytics can be easily integrated with azure IoT hub and azure IoT suite to perform real-time analytics on IoT sensor data. Azure stream analytics helps companies deploy Al-powered real-time analytics and unlock the full value from the data. It is also easy to create dashboards with power business intelligence (BI) and visualize the data and to view actionable insights.

It is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft- managed data centers. It provides Software as a Service (SaaS), Platform as a Service, and Infrastructure as a Service and supports many different programming languages, tools, and frameworks, including both Microsoft- specific and third-party software and systems.

ii. AWS IoT Analytics: AWS IoT Analytics automates the most difficult tasks associated with the analysis of IoT data and is a fully managed service, which makes it easy to run complicated DA algorithms. It is one of the easiest IoT analytics platforms to run analytics on the edge and get accurate insights. With AWS IoT Analytics, only relevant data from the sensor are stored, the data is enriched with device-specific metadata such as device type and locations. AWS IoT Analytics is fully managed and can support up to petabytes of IoT data. Thus, IoT applications are easily managed, without worrying about the hardware and infrastructure. Using AWS IoT Analytics, users can easily run queries on IoT data, run time analytics, optimize data storage, and analyze using machine learning.

AWS IoT Analytics automates the steps required to analyze data from IoT devices. It filters, transforms, and enriches IoT data before storing it in a time- series data store for analysis. There are services to collect only the data that is needed from the devices, apply mathematical transforms to process the data, and enrich the data with device-specific metadata such as device type and location before storing it. Then, extracted data is analyzed by running queries using the built-in SQL query engine, or more complex analytics and machine learning inference. AWS IoT Analytics enables advanced data exploration through integration with Jupyter Notebooks. Additionally, it enables data visualization through integration with Amazon QuickSight.

Traditional analytics and BI tools are designed to process structured data. IoT data often comes from devices that record noisy processes (such as temperature, motion, or sound). Therefore, the data from these devices can have significant gaps, corrupt messages, and false readings that must be cleaned up before analysis. Further, IoT data is often only meaningful in the context of other data from external sources. AWS IoT Analytics enables us to address these issues and collect large amounts of device data, process messages, and store them. It also enables extracting data using queries and running sophisticated analytics on it. AWS IoT Analytics includes prebuilt models for common IoT use-cases so that it can answer questions, such as which devices are about to fail or which customers are at risk of abandoning their wearable devices.

iii. SAP Analytics Cloud-. SAP Analytics Cloud has options to integrate IoT data to its analytics solution and analyze and visualize the data better. SAP Analytics cloud is enhanced with the power of predictive analytics and machine learning technology. SAP also has Streaming Lite module, which is a to-the-edge component designed to remotely deploy streaming projects. Streaming Lite is relevant for projects deployed on remote gateway devices - it is not required as part of a standard smart data streaming installations.

SAP Analytics Cloud (or SAP Cloud for Analytics) is a SaaS BI platform designed by SAP. Analytics Cloud is developed specifically with the intent of providing all analytics capabilities to all users in a single product. In addition to business planning, the other key components are BI (for reporting, dashboarding, data discovery, and visualization), predictive analytics, and governance, risk, and compliance.

Built natively on SAP HANA Cloud Platform (HCP), SAP Analytics Cloud allows data analysts and business decision makers to visualize, plan, and predict all from one secure, cloud-based environment. SAP claims this differs from other BI platforms, which often require data to be integrated from various sources and users to jump between different applications when performing tasks, such as creating reports. With all the data sources and analytics functions in one product, Analytics Cloud users can work more efficiently, according to SAP. The key functions are accessed from the same user interface that is designed for ease-of-use for business users.

iv. IBM Watson IoT Platform-. Analytics is a part of IBM Watson’s IoT platform. With this solution, users can easily analyze and visualize the IoT data and perform complicated analytics on the data from various IoT devices. IBM uses cognitive computing to extract valuable insights from structured and unstructured data and help users understand the data better. IBM Watson provides NLP, machine learning, and image and text analytics to enrich IoT apps. Watson IoT Platform Service is an IoT device message broker for device registration, IoT data management, and IoT device management. It also provides secure communication to and from devices by using MQTT and TLS.

Watson IoT Platform Service is built on the following key areas:

  • - Connection Management-. Connect and control IoT devices.
  • - Data Management-. Use device twins to normalize, transform, and review device data for use with the Watson IoT Platform components and other services.
  • - Risk Management: Configure secure connectivity and architecture with access control for users and applications.

v. Cisco Data Analytics: With Cisco Data analytics it’s easy to run analytics applications in the entire network from the cloud to the fog. Cisco provides infrastructure and tools for businesses to perform analytics on the collected IoT data. Cisco IOx APIs helps companies to make the data available to internal applications to improve operational efficiency. Cisco IoT analytics infrastructure offers infrastructure for real-time analytics, cloud to fog, enterprise analytics integration, and analytics for security, vi. Oracle Stream Analytics and Oracle Edge Analytics: Oracle’s IoT Analytics solution is a combination of both Oracle Stream Analytics and Oracle Edge Analytics. Oracle’s solutions help us develop analytics application that can read and analyze data from various sensors and devices and provide valuable insights. Both Stream Analytics and Edge Analytics can process and analyze huge volumes of streaming data collected from sensors and devices.

Oracle Stream Analytics is a new tool provided as a part of Oracle Event Processing technology platform. The Oracle Stream Analytics caters to the business needs of the users. This tool enables users to proactively identify and act on emerging streaming real-time threats and opportunities in their enterprise, as well as improve the operational efficiencies of their business. Oracle Stream Analytics helps in enhancing functional and operational efficiencies of businesses with actionable insight from real-time data by only processing and storing relevant data. Users can build applications and monitor them against the real-time streaming data within no time and with no complexity or knowledge of the underlying technologies using Oracle Stream Analytics.

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