Capture events through Azure Event Hubs in Azure Blob Storage or Azure Data Lake Storage

Azure Event Hubs enables you to automatically capture the streaming data in Event Hubs in an Azure Blob storage or Azure Data Lake Storage Gen 1 or Gen 2 account of your choice, with the added flexibility of specifying a time or size interval. Setting up Capture is fast, there are no administrative costs to run it, and it scales automatically with Event Hubs throughput units in the standard tier or processing units in the premium tier. Event Hubs Capture is the easiest way to load streaming data into Azure, and enables you to focus on data processing rather than on data capture.

Image showing capturing of Event Hubs data into Azure Storage or Azure Data Lake Storage


Configuring Event Hubs Capture to use Azure Data Lake Storage Gen 2 is same as configuring it to use an Azure Blob Storage. For details, see Configure Event Hubs Capture.

Event Hubs Capture enables you to process real-time and batch-based pipelines on the same stream. This means you can build solutions that grow with your needs over time. Whether you're building batch-based systems today with an eye towards future real-time processing, or you want to add an efficient cold path to an existing real-time solution, Event Hubs Capture makes working with streaming data easier.


The destination storage (Azure Storage or Azure Data Lake Storage) account must be in the same subscription as the event hub.

How Event Hubs Capture works

Event Hubs is a time-retention durable buffer for telemetry ingress, similar to a distributed log. The key to scaling in Event Hubs is the partitioned consumer model. Each partition is an independent segment of data and is consumed independently. Over time this data ages off, based on the configurable retention period. As a result, a given event hub never gets "too full."

Event Hubs Capture enables you to specify your own Azure Blob storage account and container, or Azure Data Lake Storage account, which are used to store the captured data. These accounts can be in the same region as your event hub or in another region, adding to the flexibility of the Event Hubs Capture feature.

Captured data is written in Apache Avro format: a compact, fast, binary format that provides rich data structures with inline schema. This format is widely used in the Hadoop ecosystem, Stream Analytics, and Azure Data Factory. More information about working with Avro is available later in this article.

Capture windowing

Event Hubs Capture enables you to set up a window to control capturing. This window is a minimum size and time configuration with a "first wins policy," meaning that the first trigger encountered causes a capture operation. If you have a fifteen-minute, 100 MB capture window and send 1 MB per second, the size window triggers before the time window. Each partition captures independently and writes a completed block blob at the time of capture, named for the time at which the capture interval was encountered. The storage naming convention is as follows:


The date values are padded with zeroes; an example filename might be:

In the event that your Azure storage blob is temporarily unavailable, Event Hubs Capture will retain your data for the data retention period configured on your event hub and back fill the data once your storage account is available again.

Scaling throughput units or processing units

In the standard tier of Event Hubs, the traffic is controlled by throughput units and in the premium tier Event Hubs, it's controlled by processing units. Event Hubs Capture copies data directly from the internal Event Hubs storage, bypassing throughput unit or processing unit egress quotas and saving your egress for other processing readers, such as Stream Analytics or Spark.

Once configured, Event Hubs Capture runs automatically when you send your first event, and continues running. To make it easier for your downstream processing to know that the process is working, Event Hubs writes empty files when there is no data. This process provides a predictable cadence and marker that can feed your batch processors.

Setting up Event Hubs Capture

You can configure Capture at the event hub creation time using the Azure portal, or using Azure Resource Manager templates. For more information, see the following articles:


If you enable the Capture feature for an existing event hub, the feature captures events that arrive at the event hub after the feature is turned on. It doesn't capture events that existed in the event hub before the feature was turned on.

Exploring the captured files and working with Avro

Event Hubs Capture creates files in Avro format, as specified on the configured time window. You can view these files in any tool such as Azure Storage Explorer. You can download the files locally to work on them.

The files produced by Event Hubs Capture have the following Avro schema:

Avro schema

An easy way to explore Avro files is by using the Avro Tools jar from Apache. You can also use Apache Drill for a lightweight SQL-driven experience or Apache Spark to perform complex distributed processing on the ingested data.

Use Apache Drill

Apache Drill is an "open-source SQL query engine for Big Data exploration" that can query structured and semi-structured data wherever it is. The engine can run as a standalone node or as a huge cluster for great performance.

A native support to Azure Blob storage is available, which makes it easy to query data in an Avro file, as described in the documentation:

Apache Drill: Azure Blob Storage Plugin

To easily query captured files, you can create and execute a VM with Apache Drill enabled via a container to access Azure Blob storage. See the following sample: Streaming at Scale with Event Hubs Capture.

Use Apache Spark

Apache Spark is a "unified analytics engine for large-scale data processing." It supports different languages, including SQL, and can easily access Azure Blob storage. There are a few options to run Apache Spark in Azure, and each provides easy access to Azure Blob storage:

Use Avro Tools

Avro Tools are available as a jar package. After you download the jar file, you can see the schema of a specific Avro file by running the following command:

java -jar avro-tools-1.9.1.jar getschema <name of capture file>

This command returns



You can also use Avro Tools to convert the file to JSON format and perform other processing.

To perform more advanced processing, download and install Avro for your choice of platform. At the time of this writing, there are implementations available for C, C++, C#, Java, NodeJS, Perl, PHP, Python, and Ruby.

Apache Avro has complete Getting Started guides for Java and Python. You can also read the Getting started with Event Hubs Capture article.

How Event Hubs Capture is charged

Event Hubs Capture is metered similarly to throughput units (standard tier) or processing units (in premium tier): as an hourly charge. The charge is directly proportional to the number of throughput units or processing units purchased for the namespace. As throughput units or processing units are increased and decreased, Event Hubs Capture meters increase and decrease to provide matching performance. The meters occur in tandem. For pricing details, see Event Hubs pricing.

Capture does not consume egress quota as it is billed separately.

Integration with Event Grid

You can create an Azure Event Grid subscription with an Event Hubs namespace as its source. The following tutorial shows you how to create an Event Grid subscription with an event hub as a source and an Azure Functions app as a sink: Process and migrate captured Event Hubs data to a Azure Synapse Analytics using Event Grid and Azure Functions.

Next steps

Event Hubs Capture is the easiest way to get data into Azure. Using Azure Data Lake, Azure Data Factory, and Azure HDInsight, you can perform batch processing and other analytics using familiar tools and platforms of your choosing, at any scale you need.

Learn how to enable this feature using the Azure portal and Azure Resource Manager template: