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
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
“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.”
"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."
Ensure that both Kafka clusters (the source and the destination) are up and running. Verify that each cluster has the necessary topics created. You may need to create topics in the destination cluster that mirror those in the source cluster. This can typically be done using the `kafka-topics.sh` script.
Create a Kafka consumer on the source cluster that subscribes to the topic(s) you want to replicate. This consumer will read data from the source cluster. Use the `kafka-console-consumer.sh` script to test your setup. Customize your consumer using the necessary configuration parameters like `bootstrap.servers`, `group.id`, and `auto.offset.reset`.
Implement logic within your consumer application to process and transform records if needed. This can be done using a custom application written in Java, Python, or any supported Kafka client language. The goal is to prepare the records for publishing to the destination cluster.
Create a Kafka producer that will send the processed records to the destination Kafka cluster. Use the `kafka-console-producer.sh` script to ensure you can connect to the destination cluster initially. Customize your producer with configuration parameters such as `bootstrap.servers` and `acks` to guarantee message delivery.
Integrate the consumer and producer logic within your application. This bridge will read records from the source cluster, process them if necessary, and then publish them to the destination cluster. Ensure that you handle exceptions and retries to maintain reliable data transfer.
Since you are not using any third-party tools, you need to manually manage consumer offsets. Ensure that your application correctly commits offsets after records are successfully produced to the destination cluster. This can be done programmatically through the Kafka consumer API.
Once the application is verified and running correctly, automate the deployment and execution of your application. Use cron jobs, systemd services, or any other scheduling tool to ensure the process runs continuously or at scheduled intervals. Monitor the process logs and Kafka metrics to ensure ongoing data movement without issues.
By following these steps, you will have effectively moved data from one Kafka cluster to another using native Kafka clients and your custom application logic.
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.
Apache Kafka is an open-source distributed event streaming platform that is used to handle real-time data feeds. It is designed to handle high volumes of data and provide real-time processing and analysis of data streams. Kafka is used by many companies for various purposes such as data integration, real-time analytics, and messaging. It is highly scalable and fault-tolerant, making it a popular choice for large-scale data processing. Kafka provides a publish-subscribe model where producers publish data to topics, and consumers subscribe to those topics to receive the data. It also provides features such as data retention, replication, and partitioning to ensure data reliability and availability.
Kafka's API gives access to various types of data, including:
1. Event data: Kafka is primarily used for streaming event data, such as user actions, sensor readings, and log data.
2. Metadata: Kafka provides metadata about the topics, partitions, and brokers in a cluster.
3. Consumer offsets: Kafka tracks the offset of each message consumed by a consumer, allowing for reliable message delivery.
4. Producer metrics: Kafka provides metrics on the performance of producers, such as message send rate and error rate.
5. Consumer metrics: Kafka provides metrics on the performance of consumers, such as message consumption rate and lag.
6. Log data: Kafka stores log data for a configurable amount of time, allowing for historical analysis and debugging.
7. Administrative data: Kafka provides APIs for managing topics, partitions, and consumer groups.
Overall, Kafka's API gives access to a wide range of data related to event streaming, metadata, performance metrics, and administrative tasks.
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:
