Apache Kafka

Apache Kafka For Absolute BeginnersThis is the central repository for all the materials related to Apache Kafka For Absolute Beginners Course by Prashant Pandey. You can get the full course at Apache Kafka @ Udemy. Description I am creating Apache Kafka for absolute beginners course to help you understand the Apache Kafka Stack, the architecture of Kafka components, Kafka Client APIs (Producers and Consumers) and apply that knowledge to create Kafka programs in Java. Who should take this Course?This course is designed for software engineers, solution architects, and managers willing to implement Kafka and solve real-time stream processing problems. I am also creating this course for data architects and data engineers who are responsible for designing and building the organization’s data-centric infrastructure. Another group of people is the managers and architects who do not directly work with Kafka implementation. Still, they work with the people who implement Kafka Streams at the ground level.Kafka and source code versionThis Course is using the Apache Kafka 2.x. I have tested all the source code and examples used in this Course on Apache Kafka 2.5 open-source distribution.

Skip to main content This browser is no longer supported. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. What is Azure Event Hubs for Apache Kafka? Article12/18/2024 In this article -->This article explains how you can use Azure Event Hubs to stream data from Apache Kafka applications without setting up a Kafka cluster on your own.OverviewAzure Event Hubs provides an Apache Kafka endpoint on an event hub, which enables users to connect to the event hub using the Kafka protocol. You can often use an event hub's Kafka endpoint from your applications without any code changes. You modify only the configuration, that is, update the connection string in configurations to point to the Kafka endpoint exposed by your event hub instead of pointing to a Kafka cluster. Then, you can start streaming events from your applications that use the Kafka protocol into event hubs, which are equivalent to Kafka topics.To learn more about how to migrate your Apache Kafka applications to Azure Event Hubs, see the migration guide.NoteThis feature is supported only in the **standard, premium, and dedicated tiers.Event Hubs for Apache Kafka Ecosystems support Apache Kafka version 1.0 and later.Apache Kafka and Azure Event Hubs conceptual mappingConceptually, Apache Kafka and Event Hubs are very similar. They're both partitioned logs built for streaming data, whereby the client controls which part of the retained log it wants to read. The following table maps concepts between Apache Kafka and Event Hubs.Apache Kafka ConceptEvent Hubs ConceptClusterNamespaceTopicAn event hubPartitionPartitionConsumer GroupConsumer GroupOffsetOffsetApache Kafka features supported on Azure Event HubsKafka StreamsKafka Streams is a client library for stream analytics that is part of the Apache Kafka open-source project, but is separate from the Apache Kafka event broker.NoteKafka Streams is currently in Public preview in Premium, and Dedicated tier.Azure Event Hubs supports the Kafka Streams client library, with details and concepts available here.The most common reason Azure Event Hubs customers ask for Kafka Streams support is because they're interested in Confluent's "ksqlDB" product. "ksqlDB" is a proprietary shared source project that is licensed such that no vendor "offering software-as-a-service, platform-as-a-service, infrastructure-as-a-service, or other similar online services that compete with Confluent products or services" is permitted to use or offer "ksqlDB" support. Practically, if you use ksqlDB, you must either operate Kafka yourself or you must use Confluent’s cloud offerings. The licensing terms might also affect Azure customers who offer

Dockerized Fake Data Producer For Aiven for Apache Kafka®This project aims at creating a Docker version of the Apache Kafka® Python Fake Data Producer.OverviewThe Dockerized Fake Data Producer For Aiven for Apache Kafka®, requires:An Aiven valid account and login TokenAn Aiven for Apache Kafka instance already createdThe run.sh will:login using username and tokenpull all the required information (hostname, port, certificates) from Aiven for Apache Kafkacreate the topic (if the topic is already exist you'll see an error)create the messagesPrerequisitesThe Dockerized Fake Data Producer For Aiven for Apache Kafka, requires:An Aiven valid account and login TokenAn Aiven for Apache Kafka instance already createdSetupclone the repository and navigate to the fake-data-producer-for-apache-kafka-docker foldercopy the conf/env.conf.sample to conf/env.conf and edit the following parametersParameter NameParameter ValuePROJECT_NAMEName of the Aiven Project where the Aiven for Apache Kafka service is runningSERVICE_NAMEName of the Aiven for Apache Kafka service runningTOPICName of the Topic to write messages inPARTITIONSNumber of partitions to set when creating a topic (this will NOT alter existing topics)REPLICATIONNumber of replicas to set when creating a topic (this will NOT alter existing topics)NR_MESSAGESOverall number of messages to produce (0 for unlimited))MAX_TIMEMax time in seconds between messages (0 for no wait)SUBJECTFake data subject (One between pizza, userbehaviour, stock, realstock (using the yahoo finance apis) and metric)USERNAMEAiven account usernameTOKENAiven account tokenPRIVATELINKFlag to say if the service is under a privatelink, to fetch the correct URLSECURITYFlag to say if the Kafka service is using SSL or not, possible values are SSL or PLAINTEXTTo know more about parameters, check the

Of processing units assigned to the namespace.Is Apache Kafka the right solution for your workload?Coming from building applications using Apache Kafka, it's also useful to understand that Azure Event Hubs is part of a fleet of services, which also includes Azure Service Bus, and Azure Event Grid.While some providers of commercial distributions of Apache Kafka might suggest that Apache Kafka is a one-stop-shop for all your messaging platform needs, the reality is that Apache Kafka doesn't implement, for instance, the competing-consumer queue pattern, doesn't have support for publish-subscribe at a level that allows subscribers access to the incoming messages based on server-evaluated rules other than plain offsets, and it has no facilities to track the lifecycle of a job initiated by a message or sidelining faulty messages into a dead-letter queue, all of which are foundational for many enterprise messaging scenarios.To understand the differences between patterns and which pattern is best covered by which service, see the Asynchronous messaging options in Azure guidance. As an Apache Kafka user, you can find that communication paths you have so far realized with Kafka, can be realized with far less basic complexity and yet more powerful capabilities using either Event Grid or Service Bus.If you need specific features of Apache Kafka that aren't available through the Event Hubs for Apache Kafka interface or if your implementation pattern exceeds the Event Hubs quotas, you can also run a native Apache Kafka cluster in Azure HDInsight.Security and authenticationEvery time you publish or consume events from an Event Hubs for Kafka, your client is trying to access the Event Hubs resources. You want to ensure that the resources are accessed using an authorized entity. When using Apache Kafka protocol with your clients, you can set your configuration for authentication and encryption using the SASL mechanisms. When using Event Hubs for Kafka requires the TLS-encryption (as all data in transit with Event Hubs is TLS encrypted), it can be done specifying the SASL_SSL option in your configuration file.Azure Event Hubs provides multiple options to authorize access to your secure resources.OAuth 2.0Shared access signature (SAS)OAuth 2.0Event Hubs integrates with Microsoft Entra ID, which provides an OAuth 2.0 compliant centralized authorization server. With Microsoft Entra ID, you can use Azure role-based access control (Azure RBAC) to grant fine grained permissions to your client identities. You can use this feature with your Kafka clients by specifying SASL_SSL for the protocol

Confluent's .NET Client for Apache KafkaTMconfluent-kafka-dotnet is Confluent's .NET client for Apache Kafka and theConfluent Platform.Features:High performance - confluent-kafka-dotnet is a lightweight wrapper aroundlibrdkafka, a finely tuned Cclient.Reliability - There are a lot of details to get right when writing an Apache Kafkaclient. We get them right in one place (librdkafka) and leverage this workacross all of our clients (also confluent-kafka-pythonand confluent-kafka-go).Supported - Commercial support is offered byConfluent.Future proof - Confluent, founded by theoriginal creator/co-creator of Kafka, is building a streaming platformwith Apache Kafka at its core. It's high priority for us that client features keeppace with core Apache Kafka and components of the Confluent Platform.confluent-kafka-dotnet is derived from Andreas Heider's rdkafka-dotnet.We're fans of his work and were very happy to have been able to leverage rdkafka-dotnet as the basis of thisclient. Thanks Andreas!Referencingconfluent-kafka-dotnet is distributed via NuGet. We provide the following packages:Confluent.Kafka [netstandard2.0, net462, net6.0, net8.0] - The core client library.Confluent.SchemaRegistry.Serdes.Avro [netstandard2.0, net6.0, net8.0] - Provides a serializer and deserializer for working with Avro serialized data with Confluent Schema Registry integration.Confluent.SchemaRegistry.Serdes.Protobuf [netstandard2.0, net6.0, net8.0] - Provides a serializer and deserializer for working with Protobuf serialized data with Confluent Schema Registry integration.Confluent.SchemaRegistry.Serdes.Json [netstandard2.0, net6.0, net8.0] - Provides a serializer and deserializer for working with Json serialized data with Confluent Schema Registry integration.Confluent.SchemaRegistry [netstandard2.0, net6.0, net8.0] - Confluent Schema Registry client (a dependency of the Confluent.SchemaRegistry.Serdes packages).Confluent.SchemaRegistry.Encryption [net6.0, net8.0] - Confluent Schema Registry client-side field-level encryption client (a dependency of the other Confluent.SchemaRegistry.Encryption.* packages).Confluent.SchemaRegistry.Encryption.Aws [net6.0, net8.0] - Confluent Schema Registry client-side

Import and export feature. DataStax Bulk Loader Package DataStax Bulk Loader for Apache Cassandra® is an efficient, flexible, easy-to-use command line utility that excels at loading, unloading, and counting data. It gets data in and out of the database faster than any other copy commands and community tools. Use it with JSON or CSV files to jump start your application development. Load Load from JSON or CSV files to the database. Unload Unload data stored in the database to JSON or CSV files. Count Quickly count the number of rows in a given table. DataStax Apache Kafka® Connector The DataStax Apache Kafka Connector automatically takes records from Kafka topics and writes them to a DataStax Enterprise or Apache Cassandra™ database. This sink connector is deployed in the Kafka Connect framework and removes the need to build a custom solution to move data between these two systems. Flexible Flexible mapping to allow reads from many Kafka topics and writes to many DataStax tables, fits nicely to the common denormalization pattern used with Cassandra. Performance Created by the authors of the DataStax Drivers for Apache Cassandra and uses best practices for database writes, resulting in fast data movement. Simple You have to write zero lines of code if you use the connector to ingest from Kafka to a DataStax Enterprise or Cassandra database. DataStax Apache Pulsar Connector Package The DataStax Apache Pulsar Connector automatically takes records from Pulsar topics and writes them to an Apache Cassandra™, DataStax Enterprise, or Astra database. The connector is deployed into an Apache Pulsar cluster and removes the need to build a custom solution to move data between these two systems. Flexible Flexible mapping to allow reads from many Pulsar topics and writes to many Cassandra tables, fits nicely to the common denormalization pattern used with Cassandra.