Billions of connected IoT devices are generating a massive amount of data every second. Meanwhile, as the IoT is booming this data generation has exponential growth. Analyzing this big chunk of data can be very hectic. Analytics tools are playing a very crucial part in collecting, monitoring, and analyzing this data.
These tools help the Internet of Things solutions reach beyond merely connecting devices so that end users can monitor that data for flaws in their systems, make real-time decisions and about where they can become more efficient.
10 best IoT data analytics tools are listed below :
1. Dell Statistica :
Dell Statistica is a general-purpose analytics tool that in fact allows users to access, prepare, analyze, report
as well as deploy advanced analytic models within vendor skeptic environments. This product line allows users to easily create and deploy different types of analytic models such as statistical, predictive, data-mining, machine learning, forecasting, optimization, and text model.
Compelling feature:
Statistica’s collective intelligence feature is designed to help businesses embrace the app marketplace for models. Users can monetize models as well as import models written by others – so the platform has good connectivity to increase its capability based on what the user requires
2. IBM Watson IoT Platform Analytics :
IBM Watson IoT Platform is a secure, smart and one of the best scalable hubs for your IoT landscape. which gives you the ability to completely manage your IoT infrastructure and make better business decisions in real-time indeed.
Compelling Feature :
The Watson analytics services enable users to leverage cognitive analytics with structured as well as unstructured – data to understand situations. It looks at options and learns as conditions change. Users gain access to Watson’s natural language processing, Watson text analytics, Watson video and image analytics as well as Watson machine learning tools.
3. Azure Stream Analytics :
Azure Stream Analytics is a fully managed, server-less engine by Microsoft for real-time analytics. It offers real-time analytics on multiple streams of data from sources such as sensors, web data sources, social media and other applications.
Compelling Features:
- It has recovery capabilities
- You can perform operations on data in temporal windows such as tumbling, hopping, sliding and session windows
- You have built-in geospatial functions
4. SAP HANA’s Smart Data Streaming :
SAP HANA smart data streaming processes streams of incoming event data in real-time, and collects as well as acts on this information.
Smart data streaming is suited specifically for situations where data arrives as events happen. where there is
value in collecting, understanding and acting on this data simultaneously. Data sources that produce streams of events in real-time are listed below:
- Sensors
- Smart devices
- Web sites (click streams)
- IT systems (logs)
- Financial market
Compelling Features :
SAP’s partnership with Dell brings a series of edge models to the enterprise by combining Dell’s Edge Gateway 5000 with SAP’s IoT platform. This allows businesses to address operational challenges such as machine productivity, predictive maintenance, and business continuity.
5. HPE Vitrica analytics platform :
HPE Vertica Analytics Platform from Hewlett Packard Enterprise is a relational database system that is column-oriented and built specifically to handle modern analytic workloads. The platform uses a clustered approach for storing big data, offering high-performance queries as well as for analytics functionality. It is also supported on a variety of Linux distributions.
Compelling Features :
Vertica Advanced Analytics Platform is a column-oriented, relational database built specifically to handle today’s analytic workloads.
6. Intel Analytics Toolkit :
Analytics Toolkit provides a foundation of common algorithms, such as graphs and network-based clustering, that IT teams can build on and customize with domain-specific code. The algorithms can be used across multiple industries such as retail, financial services and health care.
Compelling Features :
Firstly, easier big data analytics programming using Python.
Secondly, Fully scalable graph processing.
7. Amazon web services IoT :
AWS IoT analytics solutions help users to connect devices to the cloud as well as to each other. It authentication techniques such as uses device gateways and IoT device SDKs to connect. AWS IoT Analytics is a fully managed service that of course makes analysis easy. It can analyze data from millions of devices and build fast, efficient as well as responsive IoT applications without managing hardware or infrastructure and lower latency.
Compelling Features :
Easily run queries on IoT data: AWS IoT Analytics can simply run ad-hoc queries by using the SQL query engine. It also provides a series of non-overlapping, contiguous time windows to perform analysis on new, incremental data.
Tools for machine learning: It is easy to apply machine learning to IoT data because of Jupyter notebooks. It can directly connect IoT data to the notebook and build, train, as well as execute models right from the AWS IoT Analytics console.
8. Cisco Connected Streaming Analytics :
CSA is a tool that is providing the organization the potential to manage data effectively without any compromise in speed and real-time streaming despite multitasking. This platform streams live data from multiple sources for real-time insight with big data views.
Compelling Feature :
Firstly, CSA lets the user detect any issue, possible risks as well as the opportunity to work in the right way.
Secondly, With these predictive analytics, CSA gives an upper edge to the organization in front of the others.
9. Oracle Edge Analytics And Stream Explorer :
Oracle’s analytics platform is divided into two pieces, firstly the Stream Explorer, which collects data in the cloud or enterprise, and secondly Edge Analytics, which filters, correlates, processes, and aggregates data on embedded devices. These two tools enable out-of-the-box analytics that detects patterns in streams for vertical markets in particular.
Compelling Features :
Oracle Edge Analytics enables designing, defining, development, as well as implementing event
processing applications that not only meet embedded device requirements but perform to the highest levels of today’s intelligent systems
10. PTC ThingWorx Analytics :
ThingWorx Analytics is designed particularly to tackle the volume, velocity, and variety of challenges of IoT data analysis. Solutions are enhanced with sophisticated analytics made available to users via simple, intuitive user interfaces, easy-to-understand information as well as visualizations. ThingWorx also provides web and mobile applications to enable runtime capabilities, providing rapid development of role-based user experiences.
Compelling Features :
Firstly, it turns raw industrial IoT data into predictive insights and secondly it detects anomalies in real-time.
