Organizations today have access to a wide stream of data. Apache Kafka, a popular Data Processing Service is used by over 30% of Fortune 500 companies to develop real-time data feeds. Now, it is a fact that in the world of Information Storage and Retrieval, there are other Data Systems as well that are not Kafka. And sometimes you’d like to move data in and out between Kafka and these external Data Systems. This is where Kafka Connectors come in.
Kafka Connect is basically a set of connectors that allow you to get data from an external Database straight into Kafka, and to put your data from Kafka into any other external Data Sink/System. Simply put, Kafka Connectors help you simplify moving data in and out of Kafka. This article will take you through some of the best Kafka Connectors.
Table of Contents
What is Kafka?
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Apache Kafka is a popular Distributed Data Streaming software that allows for the development of real-time event-driven applications. Being an open-source application, Kafka allows you to store, read, and analyze streams of data free of cost. Kafka is distributed, which means that it can run as a Cluster that spans multiple servers. Leveraging its distributed nature, users can achieve high throughput, minimal latency, high computation power, etc., and can handle large volumes of data without any perceptible lag in performance.
Written in Scala, Kafka supports data from a large number of external Data Sources and stores them as “Topics”. Kafka employs two functions “Producers” and “Consumers” to read, write, and process events. Producers act as an interface between Data Sources and Topics, and Consumers allow users to read and transfer the data stored in Kafka. The fault-tolerant architecture of Kalka is highly scalable and can handle billions of events with ease. In addition to that, Kafka is super fast and is highly accurate with data records.
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What are Kafka Connectors?
Kafka Connectors are pluggable components responsible for interfacing with external Data Systems to facilitate data sharing between them and Kafka. They simplify the process of importing data from external systems to Kafka and exporting data from Kafka to external systems. Whether you want to pull data from external systems or push data to external systems, Kafka Connectors are here to share your load of moving data.
You can use existing Kafka Connectors for common Data Sources and Sinks or even implement your own connectors. A Source Connector is used to collect data from systems such as Databases, Stream Tables, Message Brokers, etc., and a Sink Connector is used to deliver data to Batch Systems, Indexes, or any kind of Database.
There’s a lot of connectors available to move data between Kafka and other popular Data Systems, such as S3, JDBC, Couchbase, S3, Golden Gate, Cassandra, MongoDB, Elasticsearch, Hadoop, and many more.
Key Features of Kafka Connect
Kafka Connect is basically a group of pre-built and even custom-built connectors using which you can transfer data from an exact Data Source to another exact Data Sink. Simply put, it is a framework for connecting Kafka to external systems using connectors. Take a look at some of the promising features of Kafka Connect.
- It aids in the development, deployment, and management of Kafka Connectors, making it is easy to establish connections with external systems.
- It comes with a REST interface allowing you to manage and control connectors using a REST API.
- It is designed to be scalable and fault-tolerant, meaning you can deploy Kafka Connect not just as an individual process but also as a Cluster.
- Kafka Connect automates the process of Offset Commit, which saves you the trouble of manually implementing this tedious and error-prone process.
- Kafka Connect is an ideal solution for both streaming and batch data processing.
How to Setup Kafka Connect?
It’s very easy to get started with Kafka Connect. To get started, you can download the open-source edition of Confluent Platform (the parent company of Kafka).
For the purpose of this demonstration, the most basic Connectors (the file Source Connector and the file Sink Connector) are used. You can easily find them on the Confluent Platform.
- Source Connector Configuration
- Sink Connector Configuration
- Worker Configuration
Source Connector Configuration
For the Source Connector, you can find the reference configuration at $CONFLUENT_HOME/etc/kafka/connect-file-source.properties:
name=local-file-source
connector.class=FileStreamSource
tasks.max=1
topic=connect-test
file=test.txt
$CONFLUENT_HOME is the working directory.
Take a look at the Source Connector configuration parameters:
- name is a name given by the user for the connector instance.
- connector.class specifies the implementing class or the kind of connector.
- tasks.max specifies the number of instances of Source Connector running in parallel.
- topic defines the Topic to which the connector should send the output.
- file is a connector-specific attribute that defines the file from which the connector should read the input.
Sink Connector Configuration
For the Sink Connector, you can find the reference configuration at $CONFLUENT_HOME/etc/kafka/connect-file-sink.properties:
name=local-file-sink
connector.class=FileStreamSink
tasks.max=1
file=test.sink.txt
topics=connect-test
The parameters are all exactly the same as discussed for the Source Connector, except for “connector.class” and “file”. Here, connector.class specifies the Sink Connector implementation class, and file is the file where the connector should write the content.
Worker Configuration
Connect Worker will integrate the two connectors and read from the Source Connector and write to the Sink Connector. You can find the reference Worker configuration at $CONFLUENT_HOME/etc/kafka/connect-standalone.properties:
bootstrap.servers=localhost:9092
key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter
key.converter.schemas.enable=false
value.converter.schemas.enable=false
offset.storage.file.filename=/tmp/connect.offsets
offset.flush.interval.ms=10000
plugin.path=/share/java
plugin.path holds a list of paths where connector implementations are available. You can set plugin.path to $CONFLUENT_HOME/share/java.
Take a look at the worker configuration parameters:
- bootstrap.servers holds the addresses of the Kafka brokers.
- key.converter and value.converter define converter classes.
- key.converter.schemas.enable and value.converter.schemas.enable are converter-specific settings.
- offset.storage.file.filename defines where the Connector should store its offset data.
- offset.flush.interval.ms defines the interval at which the Worker tries to commit offsets for tasks.
And with that, you can set up your first connector. However, Workers run in two modes: Standalone Mode and Distributed Mode. Before getting started, you must identify which mode would work best for your environment.
- Standalone Mode is ideal for developing and testing Kafka Connect on a local machine. It can also be used for environments that typically use single agents.
- Distributed Mode connects Workers on multiple machines (nodes) forming a Connect Cluster. It allows you to add or remove nodes as per your needs.
Top Kafka Connectors
Use Kafka Connectors to move data between Apache Kafka® and other external systems that you want to pull data from or push data to. You can download these popular connectors from Confluent Hub.
JDBC Source and Sink Connector
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A JDBC Driver enables a Java application to interact with a Database. The Kafka Connect JDBC Source Connector is capable of importing data from any Relational Database with a JDBC Driver into a Kafka Topic. Similarly, the JDBC Sink Connector is capable of exporting data from Kafka Topics to any Relational Database with a JDBC Driver. Being able to connect to any Relational Database with a JDBC Driver, the JDBC Connector is one of the most popular Kafka Connectors.
Google BigQuery Sink Connector
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As you already know, BigQuery is Google’s fully-managed, serverless Data Warehouse used by organizations all over the world for scalable analysis of huge amounts of data. Google BigQuery Sink Connector is used to stream data into BigQuery Tables. The Sink Connector automatically creates BigQuery Tables while streaming data from Kafka Topics. The connector is highly scalable as it contains an internal thread pool capable of streaming records in parallel.
JMS Source Connector
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The JMS Source Connector is capable of moving messages from any JMS-compliant broker into a Kafka Topic. It supports all traditional JMS (Java Message Service) Brokers, such as IBM MQ, ActiveMQ, Solace Appliance, etc. It makes use of JNDI (Java Naming and Directory Interface) to connect to the JMS broker. It then collects messages from the specified topic or queue and writes them into the specified Kafka Topic.
Elasticsearch Service Sink Connector
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Elasticsearch is the world’s leading open-source search and analytics solution. The Kafka Connect Elasticsearch Service Sink Connector is capable of moving data from a Kafka to Elasticsearch. It writes data from Kafka Topic to an Elasticsearch Index. All data have the same type in Elasticsearch allowing independent evolution of schemas for data from different Kafka Topics.
Amazon S3 Sink Connector
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As the name suggests, Amazon S3 Sink Connector exports data from Kafka Topics to Amazon S3 Objects in either Avro, JSON, or Bytes formats. In addition to Schema Records, this Kafka Connector is also capable of exporting plain JSON Records without schema in text files.
HDFS 2 Sink Connector
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The HDFS 2 Sink Connector is capable of exporting data from any Kafka Topic to HDFS 2.x files in a variety of formats. The connector also integrates with Hive to make data readily available for querying with HiveQL. It supports exporting data to HDFS in Avro and Parquet format. In addition to that, you can also write other formats to HDFS by extending the Format class.
MySQL Source (Debezium) Connector
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The Debezium MySQL Source Connector can read a snapshot of the existing data and record all of the row-level changes in the Databases on a MySQL Server or Cluster. However, Debezium MySQL Source Connector is capable of running only one task.
Replicator
Replicator allows you to easily replicate Topics from one Kafka Cluster to another. Replicator is also capable of creating topics as needed, preserving the topic configuration in the Source Cluster. Although it replicates data within Kafka, it is still implemented as a connector.
Conclusion
Most developers consider Kafka Connect to be the natural choice for moving data in and out of Kafka. Without the need for additional resources, you can use Kafka Connectors to share data between Kafka Topics and other external Data Systems. In this tutorial, you were introduced to Kafka Connect and discussed some of the popular Kafka Connectors. However, in businesses, extracting complex data from a diverse set of Data Sources can be a challenging task and this is where Hevo saves the day!
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Share your experience of working with Kafka Connectors in the comments section below.
Raj is a skilled data analyst with a strong passion for data analysis and architecture, having a flair for writing technical content as well. With extensive experience in handling marketing data, Raj has adeptly navigated abstract business problems to derive actionable insights that drive significant results.