Building the future of real-time IoT solutions with AWS IoT Analytics.

Explore massive volumes of IoT data and produce meaningful insights in real-time with AWS IoT Analytics service.

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AWS-IoT-Analytics

What is AWS IoT Analytics, and how does it help businesses?

AWS IoT Analytics is a fully managed service that filters, transforms, and enriches huge volumes of IoT data. It simplifies and functionalizes complex IoT analytics that helps take predictive measures, detect abnormalities, and monitor the efficiency of IoT devices in real-time. AWS IoT Analytics also enables you to execute custom analytics, run ad-hoc or in-built SQL queries, and apply Machine Learning techniques.

What We Offer?

Our AWS IoT Analytics dataflow comprises several stages, from IoT data collection to transformation, retrieval, and utilization of data insights derived from advanced analytics functions.

Data collection

Massive volumes of IoT data are collected from AWS IoT Core and AWS Kinesis data streams coupled with AWS Lambda for custom pre-processing.

Channels

Data enters AWS IoT Analytics through Channels that accept data in multiple formats and from various data sources, including AWS S3, IoT Core, and Kinesis.

Pipelines

Pipelines help convert unstructured and noisy messages from IoT devices to usable data by filtering, transforming, and enriching them.

Data stores

The cleaned and processed data is stored in time series format within the database (AWS S3). You can also keep the raw data within the datastore for future processing.

Data sets

Data is retrieved from the data stores through ad hoc or scheduled SQL queries to create customized and up-to-date data sets.

Analytics and machine learning

Perform advanced analytics and machine learning functions on the data sets using AWS Quicksight (business intelligence tool) and Jupyter Notebook (web-based computing and visualization tool).

Massive volumes of IoT data are collected from AWS IoT Core and AWS Kinesis data streams coupled with AWS Lambda for custom pre-processing.

Data enters AWS IoT Analytics through Channels that accept data in multiple formats and from various data sources, including AWS S3, IoT Core, and Kinesis.

Pipelines help convert unstructured and noisy messages from IoT devices to usable data by filtering, transforming, and enriching them.

The cleaned and processed data is stored in time series format within the database (AWS S3). You can also keep the raw data within the datastore for future processing.

Data is retrieved from the data stores through ad hoc or scheduled SQL queries to create customized and up-to-date data sets.

Perform advanced analytics and machine learning functions on the data sets using AWS Quicksight (business intelligence tool) and Jupyter Notebook (web-based computing and visualization tool).

How can AWS IoT Analytics service benefit your business?

AWS IoT Analytics service is a better alternative to traditional data analytics stack. Check out the remarkable benefits that it offers below:

  • It helps unlock the potential of massive IoT data, which is otherwise difficult to handle.
  • AIt visualizes data patterns and insights that help take predictive measures, detect abnormalities, and improve efficiency.
  • It operationalizes the entire analytics workflow and automates the execution.
  • It enables building, training, and executing Machine Learning models and algorithms on specific data sets.
  • It is a cost-effective option as it follows a pay-as-you-go approach that lets you pay for only the used services.
  • It scales automatically to handle any volume of IoT data.

Why Choose Info Services For Real Time IOT Analytics?

Multi-format-Multi-source-data

Multi-format & Multi-source data

Multi-format & Multi-source data

Allows ingestion of multi-source and multi-format input data that are either structured or unstructured from multiple sources.

desired-data

Use only the desired data

Use only the desired data

Enables filtering data in a particular format or frequency through MQTT topic filters and validates if the data is in a specific parameter range.

machine-learning

Sophisticated analytics and machine learning

Sophisticated analytics and machine learning

Supports sophisticated analytics, AWS-authored machine learning models, and visualization using Jupyter Notebooks with built-in templates.

custom-logic

Apply custom logic

Apply custom logic

Create custom rules to fill in missing data or perform specific calculations by defining AWS Lambda functions within AWS IoT analytics.

Quick-Retrieval

Quick Retrieval

Quick Retrieval

Makes data retrieval quicker using a time-series data format and a built-in SQL engine.

reprocessing

Allows reprocessing

Allows reprocessing

Allows reprocessing raw data from the Channels and creating new pipelines or modifying the existing ones.

Custom-containers

Custom containers

Custom containers

Enables execution and automation of custom-authored code in containers available within AWS IoT Analytics or imported from third-party tools like Matlab, Octave, etc

Visualization

Visualization

Visualization

Visualizes the results of ad-hoc queries in the AWS IoT Analytics console. It also connects with AWS Quicksight to analyze and picture the data in its dashboard.

Take action now

If you're interested in breaking down data silos, performing advanced data analysis, increasing data accessibility, and accelerating machine learning, we can help you achieve it. Making data lake work for you. Customized data lake strategy and implementation for your needs. Let our experts take you the way forward in your journey to a modern data lake.

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