Stream Data with Azure Event Hubs and Azure Kafka

Stream Data with Azure Event Hubs and Azure Kafka is a powerful combination for handling high-volume data streams.

Azure Event Hubs is a fully managed event ingestion service that can handle large volumes of data from any source.

You can use it to capture and process real-time data from IoT devices, applications, and other sources.

With Azure Event Hubs, you can store data in a scalable and durable way, making it easier to manage and analyze.

Azure Kafka, on the other hand, is an open-source event streaming platform that provides a unified, language-agnostic way to stream data between systems.

It's designed to handle high-throughput and provides low-latency, fault-tolerant, and scalable data processing.

By integrating Azure Event Hubs with Azure Kafka, you can create a robust and scalable data pipeline that can handle large volumes of data in real-time.

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To build a Kafka Azure Client Tool, you'll need to start by understanding the objective: achieving High Availability Kafka Service in Microsoft Azure Cloud.

The first step is to set up a Kafka cluster on Azure, which can be done by creating a new Azure Kubernetes Service (AKS) cluster. This will serve as the foundation for your Kafka service.

With your AKS cluster set up, you can then install the Kafka operator, which will manage the lifecycle of your Kafka cluster and ensure it remains highly available.

Before we dive into the nitty-gritty of getting started, let's make sure you have the necessary foundation.

To get the most out of this quickstart, you'll need to have an Azure subscription. If you don't already have one, create a free account before proceeding.

Having a solid understanding of Event Hubs for Apache Kafka is also essential. Make sure you've read through the article on that topic.

You'll also need a Windows virtual machine, which you can create as part of this process.

Here's a list of the specific components you'll need to install on your virtual machine:

To get started with building a high availability Kafka service in Microsoft Azure cloud, it's essential to understand the objective of this project. The objective is to build a Kafka Azure Client Tool.

This tool aims to achieve high availability for Kafka services in Microsoft Azure Cloud.

The goal is to ensure that the Kafka service is always available and running smoothly, even in the event of failures or outages.

Azure Kafka Basics is a fundamental concept to grasp when working with the Azure Kafka service.

Azure Kafka is a fully managed service that allows you to create and manage Apache Kafka clusters in the cloud. It's a popular messaging system used for building real-time data pipelines and streaming applications.

Kafka clusters can be scaled up or down as needed, with the ability to add or remove brokers as required. This flexibility makes it an ideal choice for applications with varying message throughput.

The Azure Kafka service provides a simple and secure way to connect to your Kafka clusters, using the same APIs and tools you're familiar with.

Kafka application is designed to be highly available and resilient to node failures if you manage your Kafka and topic configuration properly.

To achieve this, you should use the right replica factor for redundancy and rack awareness, which spreads replicas across racks. However, rack awareness is impaired if you set up a Kafka cluster on Microsoft Azure Cloud.

Microsoft Azure Cloud's Virtual Machine can be impacted by unplanned hardware maintenance, unexpected downtime, and planned maintenance.

This can cause VMs to experience unexpected downtime, loss of temporary drives, and even relocation to different servers or racks.

To reduce the impact of these events, Microsoft recommends configuring multiple virtual machines in an availability set for high availability and redundancy.

An availability set is a logical grouping of VMs within a data center to provide for redundancy and availability.

Each virtual machine in Azure Cloud is assigned a fault domain and an update domain by the underlying Azure platform.

Fault domains define the group of virtual machines that share a common power source and network switch.

Update domains indicate groups of virtual machines and underlying physical hardware that can be rebooted at the same time.

The Azure Kafka Client Tool is a powerful tool that allows you to achieve High Availability in Azure Cloud. It can be used as a standard Kafka client to create topic partitions in a high availability set.

One of the key features of this tool is its ability to rebalance existing partitions that are at risk and reassign them to a high availability set. This ensures that your data is always accessible and safe.

To take it a step further, the tool can also be set up to detect VM domain changes and automatically trigger a rebalance job for impacted partitions. This keeps your data highly available even in the face of changing VM domains.

This tool is not limited to just being used on the command line, it can also be used as a Java API. This means you can call it from any Java program to create topics, reassign topic partitions, and take advantage of the High Availability feature in Azure.

Here are some of the key features of the Azure Kafka Client Tool:

Event Hubs Schema Registry is a centralized repository for managing schemas of event streaming applications. It comes free with every Event Hubs namespace and integrates with your Kafka applications or Event Hubs SDK-based applications.

Schema Registry ensures data compatibility and consistency across event producers and consumers. This is achieved through schema evolution, validation, and governance, which promotes efficient data exchange and interoperability.

You can use Event Hubs Schema Registry to perform schema validation for your event streaming applications. This includes schema validation for Kafka applications.

Here are some key benefits of using Event Hubs Schema Registry:

Schema Registry integrates with your existing Kafka applications and supports multiple schema formats, including Avro and JSON schemas.

Event Hubs is a cloud-native broker engine that natively supports Advanced Message Queuing Protocol (AMQP), Apache Kafka, and HTTPS protocols. This means you can bring Kafka workloads to Event Hubs without making any code changes.

Event Hubs is built to provide better performance, cost efficiency, and no operational overhead. You can run Kafka workloads with ease and scalability.

To create an Event Hubs namespace, follow the step-by-step instructions in the Create an event hub using Azure portal. This will automatically enable the Kafka endpoint for the namespace.

Event Hubs for Kafka isn't supported in the basic tier, so you'll need to choose a higher tier to use this feature. You can choose from Standard, Premium, or Dedicated tiers to meet your data streaming needs.

Azure Schema Registry in Event Hubs provides a centralized repository for managing schemas of event streaming applications. It comes free with every Event Hubs namespace and integrates with your Kafka applications or Event Hubs SDK-based applications.

With Event Hubs, you can ingest, buffer, store, and process your stream in real time to get actionable insights. It uses a partitioned consumer model that enables multiple applications to process the stream concurrently.

Event Hubs integrates with Azure Functions for serverless architectures and provides a broad ecosystem for the industry-standard AMQP 1.0 protocol. SDKs are available in languages like .NET, Java, Python, and JavaScript to help you process your streams from Event Hubs.

To set up Kafka, start by creating an Azure Event Hubs namespace, which automatically enables the Kafka endpoint for the namespace.

This allows you to stream events from your applications that use the Kafka protocol into event hubs. Follow the step-by-step instructions in the Azure portal to create an Event Hubs namespace.

Event Hubs for Kafka isn't supported in the basic tier, so be sure to choose a tier that allows for Kafka support.

To set up a Kafka Connect cluster, use the Strimzi container images, which include built-in file connectors like FileStreamSourceConnector and FileStreamSinkConnector.

A custom Docker image seeded with the Azure Data Explorer connector is available on Docker Hub and can be referenced in the KafkaConnect resource definition.

You can also build your own Docker image using the Strimzi Kafka Docker image as a base and adding the Azure Data Explorer connector JAR to the plugin path.

Start by downloading the connector JAR file, then use the provided Dockerfile to build the Docker image.

This technique has been illustrated in the Strimzi documentation, so be sure to check it out for more information.

On a similar theme: Azure Vm Image

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You can capture streaming data for long-term retention and batch analytics in Azure Blob Storage or Azure Data Lake Storage.

Event Hubs integrates with Azure Stream Analytics to enable real-time stream processing. With the built-in no-code editor, you can develop a Stream Analytics job by using drag-and-drop functionality, without writing any code.

To send and receive messages with Kafka in Event Hubs, you'll need to enable a system-assigned managed identity for the virtual machine, assign the Azure Event Hubs Data Owner role to the VM's managed identity, and restart the VM.

Here are the steps to send and receive messages with Kafka in Event Hubs:

Apache Kafka on Azure Event Hubs is a multi-protocol event streaming engine that natively supports Advanced Message Queuing Protocol (AMQP), Apache Kafka, and HTTPS protocols.

Data Management is a crucial aspect of working with Azure Kafka. You can capture streaming data for long-term retention and batch analytics by storing it in Azure Blob Storage or Azure Data Lake Storage.

This approach allows you to achieve real-time analytics and micro-batch processing on the same stream. Setting up capture of event data is fast, making it a convenient option for many use cases.

For long-term retention, consider storing your data in Azure Blob Storage or Azure Data Lake Storage, where you can capture streaming data in near real-time.

Schema validation is a crucial step in ensuring the integrity and consistency of your data. You can use Event Hubs Schema Registry to perform schema validation for your event streaming applications.

Event Hubs Schema Registry provides a centralized repository for managing schemas, which comes free with every Event Hubs namespace. This means you can easily integrate schema validation with your existing Kafka applications.

Schema Registry ensures data compatibility and consistency across event producers and consumers, enabling schema evolution, validation, and governance. It also promotes efficient data exchange and interoperability.

To perform schema validation for Kafka applications, you can use Azure Schema Registry, which seamlessly connects with your Kafka applications using Event Hubs. This allows you to validate schemas for Apache Kafka applications using Avro.

Here are some key benefits of using Event Hubs Schema Registry for schema validation:

Connecting to Azure Databricks is a crucial step in your data management journey. You can use SSL to connect Azure Databricks to Kafka, but first, you need to enable SSL connections to Kafka by following the instructions in the Confluent documentation Encryption and Authentication with SSL.

To do this, you'll need to provide configurations described in the documentation, prefixed with kafka., as options. For example, you specify the trust store location in the property kafka.ssl.truststore.location. Store your certificates in cloud object storage, and restrict access to the certificates only to clusters that can access Kafka.

You can also store your certificate passwords as secrets in a secret scope. Here's an example of how to enable an SSL connection using object storage locations and Databricks secrets:

Alternatively, you can connect Kafka on HDInsight to Azure Databricks by following these steps. Create an HDInsight Kafka cluster, configure the Kafka brokers to advertise the correct address, and create an Azure Databricks cluster.

Additional reading: Databricks on Azure