Use more keys than partitions to avoid unbalanced partition loads. To avoid starving consumers, use at least as many partitions as consumers. The cost of Event Hubs depends on the throughput unit (TU). To achieve high throughput, there are a few key points that need to be considered while implementing the solution:Īpply partitioning in the Event Hubs instance to maximize raw I/O throughput and to parallelize the consumers' processing. But the solution will be more complex to develop and maintain. The solution you choose depends on the source of your streaming data, and whether you need cloning and bidirectional communication with the reporting devices.Īzure Synapse serverless Apache Spark pools can replace Azure Stream Analytics by using Spark Structured Streaming. Stream Analytics offers rapid, elastic scalability, enterprise-grade reliability and recovery, and built-in machine learning capabilities.Īzure IoT Hub could replace or complement Event Hubs. Event Hubs can ingest data from anywhere, and it seamlessly integrates with Azure data services.Īzure Stream Analytics is a real-time, serverless analytics service for streaming data. A dedicated SQL pool represents a collection of analytic resources that are provisioned when you use Synapse Analytics.Īzure Event Hubs is a real-time data streaming platform and event ingestion service. The Dedicated SQL Pool refers to the enterprise data warehousing features that are available in Azure Synapse Analytics.The Stream Analytics job writes the data in a Synapse Analytics dedicated SQL pool table.Īzure Synapse Analytics is an analytics service that combines data integration, enterprise data warehousing, and big data analytics. Apart from doing a simple passthrough of the data stream to target, the Stream Analytics job can perform standard stream processing tasks, such as JOINs, temporal aggregations, filtering, anomaly detection, and so on. The Stream Analytics job has an output configured to sink the data to a Synapse Analytics dedicated SQL pool. Stream Analytics has first-class integration with Event Hubs to consume data streams. Next, a Stream Analytics job processes the data stream from the Event Hubs instance. Event Hubs is a big data streaming platform and event ingestion service that can receive millions of events per second. The source systems generate data and send it to an Azure Event Hubs instance.
Data flows through the architecture as follows: The architecture shows the components of the stream data ingestion pipeline. Then, store the result in the data warehouse. Ingest data from a stream to a data warehouse in near real time.Īpply different stream processing techniques (JOINs, temporal aggregations, filtering, anomaly detection, and so on) to transform the data.
Several scenarios can benefit from this architecture: This example architecture will show how you can use an Azure Stream Analytics job to ingest stream to an Azure Synapse Analytics dedicated SQL pool. Azure Stream Analytics is a serverless stream processing PaaS service that can scale with customer's needs. This requirement goes beyond traditional batch jobs and needs the support for stream ingestion to their data warehouses.Īzure Synapse Analytics seamlessly supports both enterprise data warehousing and big data analytics workloads. Today, more customers require real-time representations of their business in their data warehouse. But in today's world, we see many use cases where the customers can't wait for a batch job to complete for our data scientists, analysts, or dashboards to access the data. Traditional data warehouses work well when we're dealing with batch jobs to load data.
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