The Top 7 IoT Analytics Platforms

AWS IoT Analytics is a fully managed service that helps organizations to analyze vast volumes of IoT data by automating essential analytics tasks. The platform is compatible with various data sources, including Amazon Kinesis, S3, or third-party tools, using a BatchPutMessage API, and it offers a seamless integration with AWS IoT Core for intuitive data analysis and collection.

AWS IoT Analytics allows you to select the data you want to store and analyze. You can configure channels using MQTT topic filters and direct them to appropriate pipelines for transformation and enrichment after validation. You can also use the platform to cleanse data, transform it using mathematical or conditional logic, and enrich it with external sources. The platform also offers the flexibility to reprocess raw data when needed, for sharper insights or testing hypotheses.

AWS IoT Analytics stores processed data in a time-series data store for further analysis. Both the processed data and the raw ingested data are stored simultaneously, allowing for future processing. The platform also offers built-in SQL query engines that enable you to run ad hoc or scheduled queries and provide quick results. The service also allows you to import customized code containers, as well as automating the execution of containers.

Overall, AWS IoT Analytics is a robust and flexible IoT data analytics solution that offers valuable insights and helps organizations to make data-driven decisions based on their IoT data. We recommend this platform particularly to organizations that are already leveraging AWS data management products or IoT devices from Amazon.

Datadog IoT Monitoring is a comprehensive solution that helps businesses manage software performance, device hardware metrics, network performance data, and application logs across their IoT devices. It offers visibility into every device within a network, from a single platform.

One of Datadog IoT Monitoring’s main features is its ability to aggregate and analyze IoT device data. Its custom tagging function allows users to group and compare performance data across multiple devices, which is ideal for quick troubleshooting. The platform also offers an alerting feature that focuses on significant device failures and utilizes machine learning algorithms to spot unusual activity, helping your team to focus on the most critical areas whilst reducing alert fatigue.

Datadog IoT Monitoring also enables you to examine business data from IoT devices alongside device health metrics. The IoT monitoring feature is compatible with almost all hardware platforms and operating systems, and it offers integrations with Amazon IoT, Google Cloud IoT, and Azure IoT Hub.

In summary, Datadog IoT Monitoring brings together all necessary operational data for monitoring IoT devices, providing comprehensive coverage of an organization’s IoT devices. It offers predictive maintenance, analytics, and the capacity to drill into IoT data across any dimension, leading to improved operations and more efficient troubleshooting.

Elastic Stack’s Elasticsearch Platform is an AI-powered IoT analytics tool that combines observability, security, and search applications to provide unified visibility, actionable insights, and advanced security.

Key features of the Elasticsearch Platform include comprehensive data ingestion with numerous ready-to-use integrations, secure and highly scalable data storage, and advanced search functionalities. It offers real-time results using supervised and unsupervised machine learning, and its highly flexible architecture allows seamless integration and deployment across different environments, locations, and data types.

Schedulers can also benefit from workflow automation abilities that include configurable rules for automated actions on alerts, anomalies, and insights.

When it comes to analytics, the Elasticsearch Platform uses AI and machine learning to present relevant results from structured and unstructured data in real-time. Via the platform’s fully integrated data visualization interface, users can explore data, identify trends and discrepancies, derive insights, and share results.

Overall, the Elasticsearch Platform provides a comprehensive suite of IoT analytics tools. From secure data storage to advanced search and AI-powered analytics, the platform streamlines data operations with scalability, flexibility, and efficiency.

Microsoft Azure IoT Edge helps organizations to consolidate operational data in the Azure Cloud. It enables you to deploy and manage cloud-native workloads (e.g., AI, Azure services, or your own business logic) to run on your IoT devices, as well as optimize cloud storage and ensure reliable operations even during offline periods securely and remotely.

Microsoft Azure IoT Edge comprises three core components: IoT Edge modules, IoT Edge runtime, and a cloud-based interface. IoT Edge modules are containers that operate Azure services, third-party services, or custom code. These modules are deployed and executed locally on IoT Edge-enabled devices. The IoT Edge runtime manages the modules on each device, while the cloud-based interface allows for the remote monitoring and management of IoT Edge-enabled devices.

Additional features of Microsoft Azure IoT Edge include zero-touch device provisioning, integration with Azure Monitor, automatic device configuration service, an extensive array of supported SDKs, module development tooling, and CI/CD pipeline integration with Azure DevOps. Azure IoT Edge also offers language consistency and support for popular languages like C, C#, Java, Node.js, and Python, making skillset integration easy for developers.

Microsoft Azure IoT Edge enables businesses to more easily monitor their remote IoT devices, reduce IoT solution costs, and ensure operation during offline periods or intermittent connectivity. This allows for easier, more cost-effective data analysis and seamless transition between connectivity states.

SAS Analytics for IoT is an AI-embedded solution designed to deliver scalable and fast outcomes from IoT data. Whether for predictive maintenance or process optimization, it is built to overcome complexities and meet scalability requirements. Leveraging SAS Viya, users can organize, access, and transform their IoT data, empowering even non-technical users to make reliable, accurate decisions.

One of this platform’s key features is its SAS Event Stream Processing, which accelerates IoT outcomes with real-time analytics. These analytics capabilities are embedded with AI, integrating data exploration and machine learning into a singular, scalable environment. The platform also offers robust data management capabilities to prepare IoT data for analytics, no matter where the data was generated from. Additionally, its extensible data model enables you to transform data into intelligence, streamlining ETL tasks to promote efficient collaboration within your organization.

SAS Analytics for IoT also offers an open API, which allows data selections and launchers to surface in SAS or third-party applications. Finally, it supports IoT analytics across the entire life cycle, simplifying data complexity from data discovery to deployment.

Overall, SAS Analytics for IoT is a comprehensive solution that enables businesses to extract maximum value from their IoT investments. It is a robust tool that facilitates user-friendly access to IoT data, promotes collaboration among various users within the organization, and delivers real-time analytics for efficient decision-making.

Software AG’s TrendMiner is a self-service industrial analytics solution designed for smart factories and Industry 4.0 applications. Developed specifically for engineers, it employs a high-performance analytics engine for sensor-generated time-series data, allowing for direct querying of process data without requiring data scientist input.

TrendMiner offers personalized dashboards that enable users to visualize analytics for faster decision-making, see specific trends, investigate process anomalies, and identify areas requiring attention. The platform also offers a high-speed search engine and advanced filter options, as well as patented pattern recognition technology, which facilitate rapid root cause analysis and enable users to identify potential areas for optimization.

TrendMiners’ monitoring capabilities allow for continuous surveillance of live processes, and automatically notify users of events of interest. The solution also offers predictive capabilities using patent-pending technology. Through utilizing historical data, it can forecast the likely trajectory of batch runs, transitions, or equipment start-ups within minutes. Finally, TrendMiner enables businesses to contextualize their operations, with users able to leverage contextual information from different data sources to drive decision-making and control business outcomes.

Overall, thanks to its comprehensive range of in-built analysis, monitoring, and predictive tools, TrendMiner is an effective solution for process engineers and other industry professionals that want to use their IoT data to maximize production efficiency and make data-driven decisions.

ThingSpeak is a cloud-based IoT analytics platform that focuses on the collection, visualization, and analysis of live data streams. It functions as an intermediary, receiving data from IoT devices and making it readily accessible for analysis and actions.

ThingSpeak’s core features include the ability to collect data in both private and public channels. It provides RESTful and MQTT APIs for seamless integration with other tools and services. The platform carries out data analysis and visualization using the MATLAB analytics engine, offering vivid representations of the data flows. It also offers event scheduling and alerts, to ensure that users can act quickly upon any changes in performance or issues that arise.

In terms of compatibility, ThingSpeak provides integrations with MATLAB and Simulink, Arduino, Particle devices, ESP8266 and ESP32 Wifi Modules, Raspberry Pi, LoRaWAN, Things Network, Senet, Libelium, and Beckhoff. These integrations make it easier for teams to manage and analyze their data across different systems, reducing the need for manual data entry and minimizing the risk of human error.

Overall, ThingSpeak is a comprehensive solution for IoT data analytics. Its ability to aggregate, visualize, and analyze data in real-time provides distinct benefits for businesses looking to get the most out of their IoT technologies.