TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

1. set up Elasticsearch as a source connector (using Auth, or usually an API key)
2. set up Kafka as a destination connector
3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Elasticsearch

Elasticsearch is a distributed search and analytics engine for all types of data. Elasticsearch is the central component of the ELK Stack (Elasticsearch, Logstash, and Kibana).

What is Kafka

A communication solutions agency, Kafka is a cloud-based / on-prem distributed system offering social media services, public relations, and events. For event streaming, three main functionalities are available: the ability to (1) subscribe to (read) and publish (write) streams of events, (2) store streams of events indefinitely, durably, and reliably, and (3) process streams of events in either real-time or retrospectively. Kafka offers these capabilities in a secure, highly scalable, and elastic manner.

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Prerequisites

1. A Elasticsearch account to transfer your customer data automatically from.
2. A Kafka account.
3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Elasticsearch and Kafka, for seamless data migration.

When using Airbyte to move data from Elasticsearch to Kafka, it extracts data from Elasticsearch using the source connector, converts it into a format Kafka can ingest using the provided schema, and then loads it into Kafka via the destination connector. This allows businesses to leverage their Elasticsearch data for advanced analytics and insights within Kafka, simplifying the ETL process and saving significant time and resources.

Methods to Move Data From Elasticsearch to Kafka

• Method 1: Connecting Elasticsearch to Kafka using Airbyte.
• Method 2: Connecting Elasticsearch to Kafka manually.

Method 1: Connecting Elasticsearch to Kafka using Airbyte

Step 1: Set up Elasticsearch as a source connector

1. Open the Airbyte UI and navigate to the "Sources" tab.
2. Click on the "Create Connection" button and select "Elasticsearch" as the source.
3. Enter the required information such as the name of the connection and the Elasticsearch URL.
4. Provide the Elasticsearch credentials such as the username and password.
5. Specify the index or indices that you want to replicate.
6. Choose the replication mode, either full or incremental.
7. Set the replication schedule according to your needs.
8. Test the connection to ensure that the Elasticsearch source connector is working correctly.
9. Save the connection and start the replication process.

It is important to note that the Elasticsearch source connector on Airbyte.com requires a valid Elasticsearch URL and credentials to establish a connection. The connector also allows you to specify the index or indices that you want to replicate and choose the replication mode and schedule. Once the connection is established, Airbyte will replicate the data from Elasticsearch to your destination of choice.

Step 2: Set up Kafka as a destination connector

1. First, you need to have an Apache Kafka destination connector installed on your system. If you don't have it, you can download it from the Apache Kafka website.  
2. Once you have the Apache Kafka destination connector installed, you need to create a new connection in Airbyte. To do this, go to the Connections tab and click on the "New Connection" button.  3. In the "New Connection" window, select "Apache Kafka" as the destination connector and enter the required connection details, such as the Kafka broker URL, topic name, and authentication credentials.  
4. After entering the connection details, click on the "Test Connection" button to ensure that the connection is working properly.  
5. If the connection test is successful, click on the "Save" button to save the connection.  
6. Once the connection is saved, you can create a new pipeline in Airbyte and select the Apache Kafka destination connector as the destination for your data.  
7. In the pipeline configuration, select the connection you created in step 3 as the destination connection.  
8. Configure the pipeline to map the source data to the appropriate Kafka topic and fields.  
9. Once the pipeline is configured, you can run it to start sending data to your Apache Kafka destination.

Step 3: Set up a connection to sync your Elasticsearch data to Kafka

Once you've successfully connected Elasticsearch as a data source and Kafka as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select Elasticsearch from the dropdown list of your configured sources.
3. Select your destination: Choose Kafka from the dropdown list of your configured destinations.
4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
5. Select the data to sync: Choose the specific Elasticsearch objects you want to import data from towards Kafka. You can sync all data or select specific tables and fields.
6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Elasticsearch to Kafka according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Kafka data warehouse is always up-to-date with your Elasticsearch data.

Method 2: Connecting Elasticsearch to Kafka manually

Moving data from Elasticsearch to Kafka without using third-party connectors or integrations requires you to write custom code to extract data from Elasticsearch and publish it to a Kafka topic. Below is a step-by-step guide for developers to accomplish this task:

Prerequisites:

1. Java Development Kit (JDK) installed.

2. Apache Kafka and Zookeeper installed and running.

3. Elasticsearch installed and running.

4. Access to the Elasticsearch indices from which you want to export data.

5. Kafka topic created to which you want to publish data.

Step 1: Set Up Project Environment

Create a new Java project using your preferred IDE or build tool (e.g., Maven or Gradle).

Step 2: Add Dependencies

Add the necessary dependencies to your project's build file for Kafka and Elasticsearch clients.

For Maven, add the following to your `pom.xml`:

```xml

<dependencies>

    <!-- Kafka Client -->

    <dependency>

        <groupId>org.apache.kafka</groupId>

        <artifactId>kafka-clients</artifactId>

        <version>YOUR_KAFKA_VERSION</version>

    </dependency>

    <!-- Elasticsearch Client -->

    <dependency>

        <groupId>org.elasticsearch.client</groupId>

        <artifactId>elasticsearch-rest-high-level-client</artifactId>

        <version>YOUR_ELASTICSEARCH_VERSION</version>

    </dependency>

</dependencies>

```

Step 3: Configure Elasticsearch Client

Create an instance of the Elasticsearch high-level REST client to interact with your Elasticsearch cluster.

```java

import org.elasticsearch.client.RestClient;

import org.elasticsearch.client.RestHighLevelClient;

‍

public class ElasticsearchConnector {

    public RestHighLevelClient createClient() {

        return new RestHighLevelClient(

                RestClient.builder(new HttpHost("localhost", 9200, "http"))

        );

    }

}

```

Step 4: Configure Kafka Producer

Set up a Kafka producer to send messages to your Kafka topic.

```java

import org.apache.kafka.clients.producer.KafkaProducer;

import org.apache.kafka.clients.producer.ProducerConfig;

import org.apache.kafka.clients.producer.ProducerRecord;

import org.apache.kafka.common.serialization.StringSerializer;

‍

import java.util.Properties;

‍

public class KafkaDataPublisher {

    public KafkaProducer<String, String> createProducer() {

        Properties props = new Properties();

        props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");

        props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());

        props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());

        return new KafkaProducer<>(props);

    }

}

```

Step 5: Extract Data from Elasticsearch

Write a method to extract data from Elasticsearch using the search API. You can specify the index and query as needed.

```java

import org.elasticsearch.action.search.SearchRequest;

import org.elasticsearch.action.search.SearchResponse;

import org.elasticsearch.client.RequestOptions;

import org.elasticsearch.index.query.QueryBuilders;

import org.elasticsearch.search.SearchHit;

import org.elasticsearch.search.builder.SearchSourceBuilder;

‍

import java.util.ArrayList;

import java.util.List;

‍

public class ElasticsearchDataExtractor {

    private final RestHighLevelClient client;

‍

    public ElasticsearchDataExtractor(RestHighLevelClient client) {

        this.client = client;

    }

‍

    public List<String> fetchData() throws IOException {

        SearchRequest searchRequest = new SearchRequest("your_index");

        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();

        searchSourceBuilder.query(QueryBuilders.matchAllQuery());

        searchRequest.source(searchSourceBuilder);

‍

        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);

        List<String> results = new ArrayList<>();

        for (SearchHit hit : searchResponse.getHits().getHits()) {

            results.add(hit.getSourceAsString());

        }

        return results;

    }

}

```

Step 6: Publish Data to Kafka

Iterate over the extracted data and publish each record to the Kafka topic.

```java

public class DataMover {

    public static void main(String[] args) throws IOException {

        // Create Elasticsearch client

        ElasticsearchConnector esConnector = new ElasticsearchConnector();

        RestHighLevelClient esClient = esConnector.createClient();

‍

        // Create Kafka producer

        KafkaDataPublisher kafkaPublisher = new KafkaDataPublisher();

        KafkaProducer<String, String> producer = kafkaPublisher.createProducer();

‍

        // Extract data from Elasticsearch

        ElasticsearchDataExtractor extractor = new ElasticsearchDataExtractor(esClient);

        List<String> data = extractor.fetchData();

‍

        // Publish data to Kafka

        for (String record : data) {

            producer.send(new ProducerRecord<>("your_topic", record));

        }

‍

        // Close resources

        producer.close();

        esClient.close();

    }

}

```

Step 7: Run and Monitor

Compile and run your Java application. Monitor both Elasticsearch and Kafka to ensure data is being moved correctly.

Step 8: Exception Handling and Logging

Add proper exception handling and logging to your application to manage any errors or issues that arise during the data transfer process.

Step 9: Scaling and Optimization

Depending on the volume of data, you may need to optimize your queries, batch size, and producer settings. You might also consider parallelizing the data extraction and publishing process to handle larger datasets more efficiently.

Step 10: Clean-Up

After successfully moving the data, perform any necessary clean-up, such as closing connections and disposing of resources.

This guide provides a basic framework for moving data from Elasticsearch to Kafka without third-party connectors. Depending on your specific requirements and environment, you may need to adjust configurations, error handling, and performance tuning to suit your use case.

Use Cases to transfer your Elasticsearch data to Kafka

Integrating data from Elasticsearch to Kafka provides several benefits. Here are a few use cases:

1. Advanced Analytics: Kafka’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Elasticsearch data, extracting insights that wouldn't be possible within Elasticsearch alone.
2. Data Consolidation: If you're using multiple other sources along with Elasticsearch, syncing to Kafka allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
3. Historical Data Analysis: Elasticsearch has limits on historical data. Syncing data to Kafka allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: Kafka provides robust data security features. Syncing Elasticsearch data to Kafka ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: Kafka can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Elasticsearch data.
6. Data Science and Machine Learning: By having Elasticsearch data in Kafka, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While Elasticsearch provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Kafka, providing more advanced business intelligence options. If you have a Elasticsearch table that needs to be converted to a Kafka table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Elasticsearch account as an Airbyte data source connector.
2. Configure Kafka as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Elasticsearch to Kafka after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!