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This solution demonstrates how to ingest and analyze high volumes of IoT data and generate real-time recommendations and insights.
Potential use cases
This solution utilizes the following functionality:
- Real-time data ingestion and processing pipeline capable of detection and notification within seconds.
- A secure API gateway that connects to back-end services running anywhere.
- Elastic provisioning of compute capacity without the need to manage the infrastructure.
Download an SVG file of the diagram.
The data flows through the solution as follows:
- Sensor data is generated and streamed to Azure API Management.
- AKS cluster runs microservices that are deployed as containers behind a service mesh; containers are built using a DevOps process and stored in Azure Container Registry.
- Ingest service stores data in an Azure Cosmos DB instance.
- Asynchronously, the analysis service receives the data and streams it to Apache Kafka and Azure HDInsight.
- Data scientists can analyze the big data for use in machine learning models using Splunk.
- Data is processed by the processing service, which stores the result in Azure Database for PostgreSQL and caches the data in an Azure Cache for Redis.
- A web app running in Azure App Service is used to visualize the results.
This example scenario uses several Azure components:
- Azure Pipelines: Automate build and push images to container registry and deployment of containers to Kubernetes.
- Azure API Management: Selectively expose data and services to the outside world and manage incoming API calls.
- Azure Container Registry: Build, store, secure, scan, replicate, and manage container images and artifacts.
- Azure Kubernetes Service: The fully managed Azure Kubernetes Service (AKS) makes deploying and managing containerized applications easy.
- Azure Key Vault: Safeguard cryptographic keys and other secrets used by cloud apps and services.
- Azure Cosmos DB: A fully managed database service with turnkey global distribution and transparent multi-primary replication.
- Azure HDInsight: A cost-effective, enterprise-grade service for open-source analytics.
- Azure Web Apps: Build and host web apps, mobile back ends, and RESTful APIs without managing infrastructure.
- Azure Database for PostgreSQL: a fully managed relational database service based on the open-source Postgres database engine.
- Azure Databricks: An Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform.
- Azure Cache for Redis: A fully managed, open source–compatible in-memory data store to power fast, scalable applications.
This article is maintained by Microsoft. It was originally written by the following contributors.
- Stella Lin | Principal Product Manager
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