Apache Camel Kafka Connector Simplified: A Comprehensive Guide 101

According to a report, Apache Kafka streams over seven trillion real-time messages per day. Kafka has a dedicated and distributed set of Kafka servers across the well-structured environment responsible for collecting, storing, organizing, and managing real-time messages. Kafka servers are also capable of distributing and fetching real-time data to and fro external applications. However, connecting Kafka to a third party or external application is a tedious process since it involves writing several lines of code for automating data transfer processes. To eliminate such complications, you can use the Apache Camel Kafka connector that allows you to connect the Kafka environment with your preferred external application for producing and consuming messages.

In this article, you will learn about Kafka, Camel Kafka connector, and how to configure a Camel Kafka connector to implement the process of application integration.

Table of Contents

• Prerequisites
• What is Apache Kafka?
  • Key Features of Kafka
  • Components of Kafka
• What is Apache Camel?
  • Key features of Apache Camel
  • When to use Apache Camel?
  • When to not use Apache Camel?
• What is Apache Camel Kafka Connector?
• What are the features of Apache Camel Kafka Connector?
• What is the Architecture of Apache Camel Kafka Connector?
• How to Configure the Camel Kafka Connector for Integration?
• Conclusion

Prerequisites

• Fundamental understanding of real-time Data Streaming.

What is Apache Kafka?

Developed by LinkedIn in 2010, Apache Kafka is an Open-Source Distributed Event Streaming platform used to build recommendation systems and event-driven applications. Kafka has a rich ecosystem comprising three main components: Kafka Producers, Servers, and Consumers. Kafka producers and consumers can write and read real-time messages to and fro the Kafka servers, respectively. 

Since the Kafka ecosystem is distributed in nature, you can attain maximum fault tolerance while streaming data into Kafka servers, thereby attaining maximum throughput. Because of its efficient features and fault tolerance capabilities, Kafka is being used by more than 20,300 organizations worldwide, including 80% of Fortune 500 companies like Walmart, Netflix, Spotify, and Airbnb.

Key Features of Kafka

Apache Kafka is extremely popular due to its characteristics that ensure uptime, make scaling simple, and allow it to manage large volumes, among other features. Let’s take a glance at some of the robust features it offers:

• Scalable: Kafka’s partitioned log model distributes data over numerous servers, allowing it to scale beyond what a single server can handle.
• Fast: Kafka decouples data streams, resulting in exceptionally low latency and high speed.
• Durable: The data is written to a disc and partitions are dispersed and duplicated across several servers. This helps to safeguard data from server failure, making it fault-tolerant and durable.
• Fault-Tolerant: The Kafka cluster can cope with master and database failures. It has the ability to restart the server on its own.
• Extensibility: Since Kafka’s prominence in recent years, several other software has developed connectors. This allows for the quick installation of additional features, such as integrating into other applications. Check out how you can integrate Kafka with Redshift and Salesforce.
• Log Aggregation: Since a modern system is often dispersed, data logging from many system components must be centralized to a single location. By centralizing data from all sources, regardless of form or volume, Kafka frequently serves as a single source of truth.
• Stream Processing: Kafka’s fundamental skill is doing real-time calculations on Event Streams. Kafka ingests, stores, and analyses streams of data as they are created, at any scale, from real-time data processing to dataflow programming.
• Metrics and Monitoring: Kafka is frequently used to track operational data. This entails compiling data from scattered apps into centralized feeds with real-time metrics. To read more about how you can analyze your data in Kafka, refer to Real-time Reporting with Kafka Analytics.

Components of Kafka

Clients

Clients allow producers (publishers) and consumers (subscribers) to be created in microservices and APIs. Clients exist for a vast variety of programming languages.

Servers

Servers can be Kafka Connect or brokers. Brokers are identified as the storage layer and Kafka Connect is identified as a tool for data streaming between Apache Kafka and other systems such as databases, APIs, or other Kafka clusters.

Zookeeper

Kafka leverages Zookeeper to manage the cluster. Zookeepers can easily help coordinate the structure of clusters/brokers.

What is Apache Camel?

Apache Camel is an open-source integration framework designed to make integration systems easy and simple. It also allows end-users to integrate various systems by leveraging the same API, providing support for various protocols and data types, while being extensible and allowing the introduction of custom protocols.

Key features of Apache Camel

Apache Camel integration framework is deemed as one of the best open-source software with a rich set of features. You can leverage the following features to develop loosely coupled applications with considerable ease:

• Payload-Agnostic Router
• Lightweight and Open-Source
• Comprehensive Mediation and Routing Engine
• Enterprise Integration Patterns (EIPs)
• POJO Model along with a Domain-Specific Language (DSL)
• Easy to Configure

When to use Apache Camel?

Apache Camel can be leveraged to integrate various applications with different protocols and technologies. No matter the protocol, technology, or domain-specific language you use, it will serve the same purpose in the end. Here, one application consumes the other application’s developed services and vice-versa. There is a consumer, and producer, along with endpoints, there are EIPs, custom processors (beans), and there are parameters that tackle credentials.

Apart from the features mentioned above, Apache Camel also provides support for automatic testing and error handling.

When to not use Apache Camel?

Despite Fuse Source offering commercial support for Apache Camel, it is not a recommended tool for substantial integration projects. Instead, it is suggested that you pick an ESB (like TIBCO, Mulesoft, etc.) for these types of projects.

Although it provides various features such as BAM and BPM, you’ll still need to use something other than Apache Camel to keep your options open. If you come across a situation to integrate two or three technologies like sending a JMS message or reading a file, it is probably much easier and faster to use some existing libraries such as Spring JMS template or Apache Commons IO.

What is Apache Camel Kafka Connector?

Apache Camel Kafka connector is one of the popular adapter components of the Apache Camel ecosystem, an Open-Source Application Integration framework. With Apache Camel Kafka Connector, you can connect with 300 external sources or third-party apps like SQL Server and PostgreSQL to implement integration between Kafka servers and the respective applications. In addition, using the Camel Kafka Connector, you can seamlessly integrate any applications, protocol, or endpoints to the Kafka ecosystem without involving any external automation process or writing a long set of code. This allows you to implement a hassle-free integration process for producing and consuming real-time messages between Kafka and extrinsic applications.

Apache Camel Kafka connectors are mainly categorized into two types: Camel Kafka Source connector and Camel Kafka Sink Connector. The former fetches data from external systems and stores it inside Kafka servers while the latter distributes real-time data present inside Kafka servers to other systems. In simple terms, Kafka’s source and sink connector writes and reads real-time messages to and fro the Kafka servers for implementing integration between the Kafka environment and other external applications. 

What are the features of Apache Camel Kafka Connector?

The Apache Camel Kafka Connector provides the following primary features:

• Kafka Connect 2.5
• OpenShift Container Platform 4.3 or 4.4
• AMQ Streams 1.5
• Camel 3.1
• Selected Camel Kafka Connectors

Here is a preview of Camel Kafka Connectors depicting the sink/source supported:

Connector	Sink/Source
Amazon AWS S3	Sink and Source
Java Message Service (JMS)	Sink and Source
Amazon AWS Kinesis	Sink and Source
ElasticSearch	Sink Only
Salesforce	Source Only
Cassandra Query Language (CQL)	Sink Only
Syslog	Source Only

What is the Architecture of Apache Camel Kafka Connector?

AMQ Streams is a scalable and distributed streaming platform based on Apache Kafka that includes a publish/subscribe messaging broker. Kafka Connect offers a framework to integrate Kafka-based systems with external systems. You can use Kafka Connect to configure sink and source connectors to stream data from external systems into and out of Kafka Broker.

Camel Kafka connector reuses the flexibility of Camel components and makes them available in Kafka Connect as sink and source connectors that can be used to stream data into and out of AMQ streams. For instance, you can ingest data from Amazon Web Services (AWS) for processing using an AWS S3 instance connector, or collate events stored in Kafka into an ElasticSearch instance for analytics using an ElasticSearch sink connector.

How to Configure the Camel Kafka Connector for Integration?

Since Apache Camel allows you to connect Kafka with various external systems or applications for producing and consuming data, you must use the respective Camel Kafka connector plugins. In the following steps, you will see how to establish a basic integration between a SQL database and Kafka using the respective sink and source connector. 

To use the connector for implementing the integration between a respective database and Kafka locally, i.e., on your local machine, you have to satisfy certain prerequisites. You should readily have an actively running Kafka Server, Zookeeper Instance, and Kafka Connect framework on your local machine. In addition, the creation of Kafka topics is also a must before starting with the integration process.

• Initially, you can start the Kafka server and Zookeeper instances. Open a new command prompt and execute the following command for starting a Kafka server. 

$KAFKA_HOME/bin/kafka-server-start.sh $KAFKA_HOME/config/server.properties

• Now, open another command prompt and run the following command to start the Zookeeper instance.

$KAFKA_HOME/bin/zookeeper-server-start.sh $KAFKA_HOME/config/zookeeper.properties

• On executing the above commands, you successfully started the Kafka environment. Now, you can create a Kafka topic to store real-time messages later. 
• Open a new command prompt and run the command given below to create a new Kafka topic.

$KAFKA_HOME/bin/kafka-topics.sh --create --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1 --topic mytopic

• Using the above-given command, you created a new Kafka topic named mytopic with a single partition and replication factor.
• After satisfying the prerequisites for implementing the integration process, the next step is to download and install the Camel-Kafka connector according to the respective external application. In this case, assume that you are integrating Kafka with the PostgreSQL database so that you have to download the SQL connector component from the Apache Camel framework. 
• There are two ways to download the required connector. You can download the appropriate connector from the official website of Apache Camel, or you can clone the Apache Camel’s Github repository to access the respective Camel Kafka connector. In both ways, you can access the connector based on the external application that you are about to integrate with the Kafka environment.

• For downloading the connector manually, visit the official website of Apache Camel Kafka.
• Click on the “Download” button on the welcome page of Apache Camel Kafka. You will be redirected to the new page where you can download various Camel-Kafka components.

• Since you are about to download the Camel Kafka connector, scroll down to find the “Apache Camel Kafka Connector” segment. You can also use the search bar to find the respective Camel component.
• In the “Apache Camel Kafka Connector” segment, click on “Connectors download list”.
• Now, you are directed to the new page having all the connectors and their respective documentation. 
• In the vast collection of connector lists, find the appropriate Camel Kafka connector for integrating Kafka with PostgreSQL. On scrolling through the connectors list, you can find the sink and source connector for connecting the PostgreSQL database with Kafka.

• Now, download both the sink and source connectors by clicking on the download buttons on the respective connectors. Now, you successfully downloaded the Camel Kafka connectors to the local machine. 
• Another straightforward way of downloading the Camel-Kafka connector is by using the CLI tools such as command prompt or power shell. 
• Initially, create a home directory to extract the Camel-Kafka connector. As given in the official documentation, the home directory’s file path is

“/home/oscerd/connectors/.”

• Before downloading the connector packages, navigate to the “connectors” folder by executing the command given below.

cd /home/oscerd/connectors/

• Open a new command prompt and execute the following command to download the Camel-Kafka connector from the Github repository.

wget https://repo1.maven.org/maven2/org/apache/camel/kafkaconnector/camel-sql-kafka-connector/0.11.0/camel-sql-kafka-connector-0.11.0-package.tar.gz

• On executing the above command, you successfully downloaded the Camel-Kafka connector components.
• To unzip or extract the Camel-Kafka connector package, run the following command.

untar.gz camel-sql-kafka-connector-0.11.0-package.tar.gz

• After executing the above command, you have successfully extracted the Camel Kafka’s sink and source connector to the connectors folder.
• Now, you have to download the driver component to implement the integration between PostgreSQL and Kafka. 
• Initially, navigate to the newly created Camel-Kafka SQL package folder to download the respective driver by running the following command.

cd /home/oscerd/connectors/camel-sql-kafka-connector/

• In the next step, you can use the “wget” command to download the driver of PostgreSQL.

wget https://repo1.maven.org/maven2/org/postgresql/postgresql/42.2.14/postgresql-42.2.14.jar

• Then, you have to configure the Kafka connect framework for establishing a connection between the external application and the Kafka environment.
• Open the default or home directory where Apache Kafka is installed on your local machine. Navigate to the “config” folder. You can find various script files of Kafka components. Search for the “connect-standalone.properties” and right-click on the respective file. Open the properties file with your favorite editor tool. 
• Now, in the properties files, find the “plugin-path” parameter. 
• After navigating to the “plugin-path” parameter, configure it as shown below.

plugin.path=/home/oscerd/connectors

• On editing the parameter, you customize the plugin path property as per your preferred location. In this case, you provided the location as /home/oscerd/connectors where you previously downloaded the Camel Kafka’s source and sink connector.

By following the above-mentioned steps, you successfully downloaded and configured the Camel Kafka connector to set up integration between external applications. After configuring the connector setup, you can run the sink and source connectors of Camel Kafka to establish integration of the Kafka environment and the respective external applications.

Conclusion

In this article, you learned about Kafka, the Camel Kafka connector, and a basic configuration method to set up the Camel Kafka connectors. This article mainly focused on setting up the Kafka integration environment locally or with your local machine. However, you can also configure or set up the Camel Kafka connection environment using independent container management systems like Kubernetes and OpenShift.

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