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Nov 7, 2023 · Nov 7, 2023. -- Azure Event Hubs is a big data streaming platform and event ingestion service. It can receive and process millions of events per second. It represents the “front door” for an...
Sep 7, 2022 · Azure Event Hubs acts like a “front door” for an event pipeline, often called an event ingestor. An event ingestor is a component or service that sits between event publishers and...
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How does the event hubs ingestion pipeline work in Azure Data Explorer?
What is Azure Event Hubs & Azure Data Explorer?
How do I consume event hub data?
How do I ingest events from Event Hubs?
Sep 7, 2016 · First of all, Event Hubs are more for high throughput, and more for one-way event processing scenarios. While messaging abilities may be lacking in Event Hubs, they can be found in...
- Overview
- Key capabilities
- How it works
- Next steps
Azure Event Hubs is a cloud native data streaming service that can stream millions of events per second, with low latency, from any source to any destination. Event Hubs is compatible with Apache Kafka, and it enables you to run existing Kafka workloads without any code changes.
Using Event Hubs to ingest and store streaming data, businesses can harness the power of streaming data to gain valuable insights, drive real-time analytics, and respond to events as they happen, enhancing overall efficiency and customer experience.
Azure Event Hubs is the preferred event ingestion layer of any event streaming solution that you build on top of Azure. It seamlessly integrates with data and analytics services inside and outside Azure to build your complete data streaming pipeline to serve following use cases.
•Real-time analytics with Azure Stream Analytics to generate real-time insights from streaming data.
•Analyze and explore streaming data with Azure Data Explorer.
•Create your own cloud native applications, functions, or microservices that run on streaming data from Event Hubs.
Apache Kafka on Azure Event Hubs
Azure Event Hubs is a multi-protocol event streaming engine that natively supports AMQP, Apache Kafka, and HTTPs protocols. Since it supports Apache Kafka, you bring Kafka workloads to Azure Event Hubs without doing any code change. You don't need to set up, configure, and manage your own Kafka clusters or use a Kafka-as-a-Service offering that's not native to Azure. Event Hubs is built from the ground up as a cloud native broker engine. Hence, you can run Kafka workloads with better performance, better cost efficiency and with no operational overhead. For more information, see Azure Event Hubs for Apache Kafka.
Schema Registry in Azure Event Hubs
Azure Schema Registry in Event Hubs provides a centralized repository for managing schemas of events streaming applications. Azure Schema Registry comes free with every Event Hubs namespace, and it integrates seamlessly with your Kafka applications or Event Hubs SDK based applications. It ensures data compatibility and consistency across event producers and consumers. Schema Registry enables seamless schema evolution, validation, and governance, and promoting efficient data exchange and interoperability. Schema Registry seamlessly integrates with your existing Kafka applications and it supports multiple schema formats including Avro and JSON Schemas. For more information, see Azure Schema Registry in Event Hubs.
Real-time processing of streaming events with Azure Stream Analytics
Event Hubs integrates seamlessly with Azure Stream Analytics to enable real-time stream processing. With the built-in no-code editor, you can effortlessly develop a Stream Analytics job using drag-and-drop functionality, without writing any code. Alternatively, developers can use the SQL-based Stream Analytics query language to perform real-time stream processing and take advantage of a wide range of functions for analyzing streaming data. For more information, see articles in the Azure Stream Analytics integration section of the table of contents.
Event Hubs provides a unified event streaming platform with time retention buffer, decoupling event producers from event consumers. The producers and consumer applications can perform large scale data ingestion through multiple protocols.
The following figure shows the key components of Event Hubs architecture:
The key functional components of Event Hubs include:
•Producer applications can ingest data to an event hub using Event Hubs SDKs or any Kafka producer client.
•Namespace is the management container for one or more event hubs or Kafka topics. The management tasks such as allocating streaming capacity, configuring network security, enabling Geo Disaster recovery etc. are handled at the namespace level.
•Event Hub/Kafka topic: In Event Hubs, you can organize events into an event hub or a Kafka topic. It's an append only distributed log, which can comprise of one or more partitions.
Stream data using Event Hubs SDK (AMQP)
You can use any of the following samples to stream data to Event Hubs using SDKs. •.NET Core •Java •Spring •Python •JavaScript •Go •C (send only) •Apache Storm (receive only)
Stream data using Apache Kafka
You can use following samples to stream data from your Kafka applications to Event Hubs. •Using Event Hubs with Kafka applications
Schema validation with Schema Registry
You can use Event Hubs Schema Registry to perform schema validation for your event streaming applications. •Schema validation for Kafka applications
May 8, 2023 · The Event Hubs ingestion pipeline transfers events to Azure Data Explorer in several steps. You first create an event hub in the Azure portal. You then create a target table in Azure Data Explorer into which the data in a particular format, will be ingested using the given ingestion properties.
Code sample
"body":{"value": 42},"properties":{"customProperty": "123456789"...Next steps: Understand Azure Event Hubs. Messages Vs Events. A business application might opt for events for certain processes and move to message for other processes. To set the required communication, it is necessary to analyse the application’s architecture and its use cases.
Feb 13, 2022 · Event Hubs allows telemetry and event data to be made available to various stream-processing infrastructures and analytics services. It's available either as data streams or bundled event batches. This service provides a single solution that enables rapid data retrieval for real-time processing, and repeated replay of stored raw data.
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