How to load data from MySQL to Kafka
Learn how to use Airbyte to synchronize your MySQL data into Kafka within minutes.


Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
Building in-house pipelines
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes



Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.
Move Large Volumes, Fast
Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.
An Extensible Open-Source Standard
More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.
Full Control & Security
Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.
Fully Featured & Integrated
Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.
Enterprise Support with SLAs
Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.
What our users say

Raman Singh
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

Chase Zieman

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
How to Sync to Manually
Step 1: Set Up Your MySQL Database
1. Ensure that your MySQL database is properly configured and contains the data you want to move to Kafka.
2. Make a note of the database connection details, including the hostname, port, username, and password.
Step 2: Set Up Your Kafka Environment
1. Ensure that your Kafka cluster is up and running.
2. Create a Kafka topic where the data from MySQL will be sent. You can use the Kafka command-line tools for this:
```bash
kafka-topics.sh --create --topic your-topic-name --bootstrap-server your-kafka-broker:port --replication-factor 1 --partitions 1
```
3. Note down the Kafka broker details and the topic you just created.
Step 3: Create a Java Project
1. Create a new Java project in your favorite IDE.
2. Add the necessary dependencies to your `pom.xml` or `build.gradle` file. You'll need the MySQL JDBC driver and the Kafka client library:
```xml
mysql
mysql-connector-java
8.0.23
org.apache.kafka
kafka-clients
2.7.0
```
Step 4: Implement Data Extraction from MySQL
1. Write a Java class that connects to the MySQL database using JDBC.
2. Implement a method to query the data you want to send to Kafka. This could be a simple `SELECT` statement or something more complex depending on your needs.
3. Extract the data into a suitable format (e.g., a list of records).
Step 5: Implement Data Production to Kafka
1. Write a Java class that connects to the Kafka cluster using the Kafka producer API.
2. Create a Kafka producer with the appropriate configuration settings.
3. Implement a method to convert the data records from MySQL into Kafka messages.
4. Send the messages to the Kafka topic you created earlier.
Step 6: Combine MySQL Extraction and Kafka Production
1. In your main application logic, combine the data extraction and production steps.
2. Fetch the data from MySQL.
3. For each record, produce a message to the Kafka topic.
4. Ensure proper error handling and logging.
Step 7: Test Your Application
1. Run your application and monitor both the MySQL queries and the Kafka topic.
2. Verify that the data is being moved correctly from MySQL to Kafka.
3. Check for any data loss or errors during the process.
Step 8: Deployment and Maintenance
1. Once you're satisfied with the testing, deploy your application to a suitable environment where it can run continuously or as needed.
2. Implement monitoring and alerting to keep track of the application's health and the data transfer process.
3. Plan for regular maintenance, updates, and backups as necessary.
Step 9: Scaling and Optimization
1. If the volume of data is large or the load increases, consider optimizing your SQL queries and Kafka producer settings for better performance.
2. You may also need to scale your Kafka cluster by adding more brokers or increasing the number of partitions in your topic.