AWS IoT Data Ingestion Simplified 101: The Complete Guide

By: Published: May 11, 2022

Extracting Internet of Things (IoT) data (sensors and log data) for analysis requires a heavy investment in custom development. If you want to use machine and sensor data for real-time analytics, the data needs to be continuously delivered to a centralized Data Hub. Amazon Web Services (AWS) provides the AWS IoT solution to connect IoT devices with AWS Cloud Services. With AWS IoT, users can connect their devices and select an appropriate solution to better manage and support their IoT devices. 

In this blog, you will learn about AWS, IoT Data Ingestion, and seven AWS IoT Data Ingestion patterns. 

Table of Contents


  • Basic understanding of IoT devices.

What is AWS?

AWS IoT data ingestion: aws logo | Hevo Data
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Amazon Web Services or AWS is a market leader in IaaS (Infrastructure-as-a-Service) and PaaS (Platform-as-a-Service) for Cloud ecosystems. It’s a collection of Cloud Computing services where users can rent the service to design cost-effective and scalable computing solutions. These services can be combined to create a scalable application where users only pay for services, resulting in faster performance and lower capital expenditure. 

Key Features of AWS

1) Cost-effective

It has no upfront investment or long-term commitment for its solutions. Businesses can scale up or down depending on demand or changes in resources. It has a pay-as-you-go pricing model, allowing businesses to only pay for specific services and for the time. There are three ways to use Amazon Cloud Computing cost plans > Save when you reserve, pay less by using more, and AWS free tier. 

  • Save When you Reserve: Valid for specific services such as Amazon EC2 and Amazon RDS. The upfront cost is directly proportional to the discount accrued, i.e., you’ll receive the maximum discount if you decide to pay the entire cost up-front and vice versa.
  • Pay Less by using More: Volume-based discounts valid for S3 or data transfer OUT from EC2. In this, you’ll pay less per Gigabyte (GB). 
  • AWS Free Tier: Every user gets free access to over 60 AWS services.

2) Flexible

AWS is a platform-agnostic to languages and operating systems. Users can select the development platform and programming model of their choice. They also get a virtual environment to access software and services. There are no rigid protocols or restrictions when subscribing to Cloud services. 

3) Scalable

AWS Cloud lets you iterate and experiment through its vast global Cloud Infrastructure. It can quickly and easily manage the increasing workload by allocating resources based on the demand. You can also use new services rather than wait months for hardware and avoid upfront resource provisioning for projects with short lifetimes and variable consumption rates.

4) Secure

Amazon Web Services offers end-to-end security and privacy. It also maintains integrity, confidentiality, and Data Availability with utmost importance. Amazon’s data centres have several physical and operational security layers to ensure the integrity of your data. It also conducts regular audits to ensure infrastructural security. The latest addition to AWS Security services is the Amazon Detective, making Data Investigations faster and more efficient.

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What is AWS IoT?

AWS IoT data ingestion: aws IoT logo | Hevo Data
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The AWS IoT is a platform that connects and analyzes data collected from Internet-connected devices and connects it to AWS Cloud applications. It offers device support and Cloud services to implement IoT solutions and consists of apps, Cloud services, communication protocols, devices, and interfaces (UI, sensors, actuators). 

The AWS IoT universe consists of:

  • Apps
  • Cloud services
  • Communications
  • Devices
  • Interfaces

AWS IoT services consist of:

  • AWS IoT Device Software: Greengrass, Device Tester, Device SDKs, FreeRTOS, and Core Device Advisor.
  • AWS IoT Control Services: Core, Device Management, Device Defender, and Things Graph.
  • AWS IoT Data Services: Analytics, SiteWise, and Events. 
  • AWS IoT Core Messaging Services: Device gateway, Message Broker, and Rules Engine. 
  • AWS IoT Core Support Service: Alexa Voice Service (AVS) Integration for AWS IoT and Amazon Sidewalk Integration for AWS IoT Core. 

AWS IoT Core supports the following four protocols:

  • MQTT (Message Queuing and Telemetry Transport)
  • MQTT over WSS (Websockets Secure)
  • HTTPS (Hypertext Transfer Protocol – Secure)
  • LoRaWAN (Long Range Wide Area Network)

Use Cases of AWS IoT Platform

  • Optimize Industrial Operations: Businesses can create scalable and rich IoT applications to reduce downtime and monitor operations.
  • Build Differentiated Consumer Products: Users can connect consumer applications for home security, monitoring, networking, and automation.
  • Reinvent Smart Buildings and Cities: AWS IoT can help solve challenges in healthcare, infrastructure, and various other industries.
  • Transform Mobility: Applications can analyze and act on connected vehicle data without managing any infrastructure.

7 Key AWS IoT Data Ingestion Patterns

Following are seven types of AWS IoT Data Ingestion patterns available.

Pattern 1: AWS Stream Manager

AWS IoT data ingestion: AWS Greengrass stream manager | Hevo Data
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With AWS IoT Greengrass Stream Manager, users can easily and quickly transfer high-volume IoT data to the AWS Cloud and perform AWS IoT Data Ingestion. It can integrate Machine Learning inference and work in an environment with limited or intermittent activity. Users can define time-out behaviour, bandwidth use, and how the data is handled when the core is connected or disconnected. It supports AWS destinations for exporting data > Amazon Kinesis Data Stream, AWS IoT Analytics, Amazon S3 Objects, and AWS IoT SiteWise. 

Pattern 2: AWS IoT SiteWise (+AWS IoT SiteWise Monitor)

AWS IoT data ingestion: AWS SiteWise | Hevo Data
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When paired with AWS IoT SiteWise components, AWS IoT Greengrass can send local equipment and device data for AWS IoT Data Ingestion. The AWS SiteWise Edge software helps users collect, process, organize and monitor equipment data on-premises. This AWS IoT Data Ingestion pattern helps optimize asset maintenance, view live trend charts of data and improve manufacturing operations. 

Pattern 3: AWS IoT Core + AWS IoT Analytics + Amazon QuickSight

AWS IoT data ingestion: AWS Core, Analytics, Quicksight| Hevo Data
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With AWS IoT Core, devices can easily connect and securely interact with other devices or Cloud applications for AWS IoT Data Ingestion. It can also keep track of all devices even when they are offline. AWS IoT Analytics can automate the data analysis, and it can filter, transform, and enrich data before storing it for analysis. AWS IoT Core also enables advanced exploration through Jupyter Notebook and data visualization through Amazon QuickSight. This AWS IoT Data Ingestion pattern is helpful for productive maintenance of IoT devices, automating anomaly detection, and performing a comprehensive analysis of IoT data.

Pattern 4: Amazon Timestream

AWS IoT data ingestion: AWS Timestream | Hevo Data
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In this pattern for AWS IoT Data Ingestion, you publish time series data to AWS IoT Core and then push data to Amazon Timestream. You can also visualize data using various dashboards. You can use Amazon-managed Grafana or Amazon QuickSight as your dashboard and alerting tool. Amazon Timestream can integrate with Grafana. The Timestream AWS IoT Data Ingestion pattern is useful to perform analytical functions on-device data such as approximation, smoothing, and interpolation.

Pattern 5: AWS IoT Core + Amazon Kinesis + Amazon QuickSight

AWS IoT data ingestion: AWS core, kinesis, quick sight | Hevo Data
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Data you publish to AWS IoT core will integrate with Amazon Kinesis, allowing you to collect, process, and analyze large bandwidth data in real-time. This AWS IoT data ingestion pattern enables you to feed real-time dashboards, perform time-series analytics, and create real-time metrics. You can use Amazon QuickSight for reporting and Amazon OpenSearch for real-time changes. This AWS IoT data ingestion pattern is useful when you have high bandwidth streaming data points.

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Pattern 6: Amazon OpenSearch Service + OpenSearch Service Dashboards/Amazon Managed Grafana

AWS IoT data ingestion: AWS OpenSearch service | Hevo Data
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The data is pushed to Amazon OpenSearch in this AWS IoT Data Ingestion pattern, where you can use tools like the OpenSearch Dashboard to visualize and query the data. You can also use Amazon-managed Grafana for dashboarding by adding Amazon OpenSearch Service as a data source. This AWS IoT Data Ingestion pattern is useful to monitor device health or device metrics. You get the ability to perform custom configurations, search on the underlying data, and get a real-time dashboard application.

Pattern 7: AWS IoT Core + AWS Lambda + Amazon DynamoDB + Amazon QuickSight / Custom Dashboards

AWS IoT data ingestion: AWS Lambda, DynamoDB | Hevo Data
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In this AWS IoT Data Ingestion pattern, you can visualize real-time telemetry data in a custom dashboard of your choice. The data is sent via AWS IoT Core using Amazon DynamoDB, AWS Lambda, and AWS AppSync. The data will be stored in an Amazon DynamoDB table, and you’ll have to export the data into an analytics platform to perform advanced analytics. You can use AWS Amplify to create and launch mobile applications and custom dashboards. This AWS IoT Data Ingestion pattern is useful when IoT data has to be delivered via a custom real-time dashboard, so the end-user can access data as soon as it changes. 


AWS IoT Data Ingestion pattern offers seven ways for users to ingest data in AWS. Amazon’s IoT service allows bi-directional communication between Internet-connected things such as embedded devices, appliances, sensors, and other services on the AWS Cloud. In this blog, you learned about Amazon IoT services and how to perform AWS IoT data ingestion. 

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Share your experience of learning about the AWS IoT Data Ingestion in the comments section below.

Osheen Jain
Freelance Technical Content Writer, Hevo Data

Osheen has extensive experience in freelance writing within the data industry and is passionate about simplifying the complexities of data integration and data analysis through informative content for those delving deeper into these subjects.

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