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.
Prerequisites
- Basic understanding of IoT devices.
What is AWS?
Amazon Web Services or AWS is a market leader in IaaS (Infrastructure-as-a-Service) and PaaS (Platform-as-a-Service) for Cloud ecosystems.
What is AWS IoT?
The AWS IoT universe consists of Apps, Cloud Services, Communications, Devices & Interfaces
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)
7 Key AWS IoT Data Ingestion Patterns
Pattern 1: AWS Stream Manager
- 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 behavior, bandwidth use, and how the data is handled when the core is connected or disconnected.
Pattern 2: AWS IoT SiteWise (+AWS IoT SiteWise Monitor)
- 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
- 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. Can automate the data analysis, and it can filter, transform, and enrich data before storing it for analysis.
- Enables advanced exploration through Jupyter Notebook and data visualization through Amazon QuickSight.
- Helpful for productive maintenance of IoT devices, automating anomaly detection, and performing a comprehensive analysis of IoT data.
Pattern 4: Amazon Timestream
- 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
- 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.
Pattern 6: Amazon OpenSearch Service + OpenSearch Service Dashboards/Amazon Managed Grafana
- The data is pushed to Amazon OpenSearch, 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.
- 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
- 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.
Conclusion
- 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.
Frequently Asked Questions
1. What is IoT data ingestion?
IoT data ingestion refers to the process of collecting, processing, and storing data generated by Internet of Things (IoT) devices.
2. What is the ingestion layer of IoT?
The ingestion layer in the context of IoT refers to the part of the data pipeline responsible for handling the incoming data from IoT devices.
3. What is the data ingestion?
Data ingestion is the process of acquiring and importing data from various sources into a data storage or processing system.
Osheen is a seasoned technical writer with over a decade of experience in the data industry. She specializes in writing about B2B, technology, finance, and SaaS domains. Her passion for simplifying intricate technical concepts has established her as a respected expert in the field, making her an invaluable resource for those looking to deepen their understanding of data science.
