Trusted by data-driven companies
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.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- 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
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"
“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.”
“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”
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.
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.
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
<!-- For Maven -->
<dependencies>
<!-- MySQL Driver -->
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>8.0.23</version>
</dependency>
<!-- Kafka Client -->
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
<version>2.7.0</version>
</dependency>
</dependencies>
```
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).
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.
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.
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.
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.
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.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
MySQL is an SQL (Structured Query Language)-based open-source database management system. An application with many uses, it offers a variety of products, from free MySQL downloads of the most recent iteration to support packages with full service support at the enterprise level. The MySQL server, while most often used as a web database, also supports e-commerce and data warehousing applications and more.
MySQL provides access to a wide range of data types, including:
1. Numeric data types: These include integers, decimals, and floating-point numbers.
2. String data types: These include character strings, binary strings, and text strings.
3. Date and time data types: These include date, time, datetime, and timestamp.
4. Boolean data types: These include true/false or yes/no values.
5. Spatial data types: These include points, lines, polygons, and other geometric shapes.
6. Large object data types: These include binary large objects (BLOBs) and character large objects (CLOBs).
7. Collection data types: These include arrays, sets, and maps.
8. User-defined data types: These are custom data types created by the user.
Overall, MySQL's API provides access to a wide range of data types, making it a versatile tool for managing and manipulating data in a variety of applications.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
