ONICA's IoT Cloud

A collection of AWS CloudFormation resources

IoT Cloud is a cloud configuration tool that codifies best practices in building a serverless IoT backend on AWS. Utilizing native AWS services -- including automation with AWS CloudFormation -- it provides an accelerated pathway to production without compromising the flexibility required for each customer’s unique business.

Gain intelligence from your IoT data within 5-minutes of deploying IoT Cloud resources

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IoT Cloud provides resources that can be used to easily create serverless ingestion pipelines, device monitoring metrics, and accelerate data flow from your IoT devices.

IoT Cloud installs a CloudFormation macro that processes resources and converts them into ready-to-use resources for your fleet of IoT devices. The CloudFormation will help you process messages from your devices, perform transformational logic and store it in Amazon S3, Amazon DynamoDB or Amazon ElasticSearch.

  • Simplifies the deployment of cloud native IoT ingestion pipelines
  • Doesn’t abstract the underlying cloud services; built on AWS CloudFormation
  • Built on battle-tested best practices from years of IoT deployments
  • Provides device monitoring capabilities beyond the AWS IoT services
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Gain intelligence from your connected devices’ data instantly with IoT Cloud

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Deploy a data ingest pipeline in less than 5 minutes for your fleet of IoT Devices
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Save weeks of development time writing and testing CloudFormation templates and code
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IoT Cloud utilizes AWS best practices around security and architecture

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Build your own dashboard to track messages, and connection metrics with Athena & Quicksight

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Focus on data transformation, and the business needs, vs how to invoke that logic in the backend

IoT Cloud Launched at AWS re:Invent 2019!

Ingest Pipeline

The primary mechanism for data ingestion. A pipeline will create a link between a single data source and one or more destinations. You can optionally configure transformers and filters to modify your data as it passes through the pipeline. You can combine pipelines to create workflows that meet your data ingestion needs.

Ingest Source

Sources define where messages are generated from. This can be MQTT, Amazon Kinesis, Amazon SNS, Amazon SQS, etc. Typically a source will be from an AWS IoT Core MQTT topic. The resources that are created depend on the type of source. For MQTT, rules and actions will be created.

Data Filter

A filter for pipelines to limit type of messages that are sent to destinations.

Data Transformer

A collection of transformation or enrichment operations to apply to an input message.

Data Lake Data Store

Resource that provides the details on how and where to store messages in a data lake. Underlying data store is S3.

Snapshot Data Store

This resource provides the details on how and where to store messages in a snapshot repository which contains the most current data points for a device across all messages being processed. A primary use case for using a snapshot data store is to get quick access to the current state of a device. Not suitable for historical data points.

Time Series Data Store

This resource defines the core location information for an underlying time series based resource. Currently supports Amazon ElasticSearch provides destination specific parameters for how to deliver messages to a time series data store. In IoT Cloud, a time series data store can be linked from multiple time series destinations to get reuse out of the underlying infrastructure.