Summarize
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
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
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
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."
Begin by ensuring that your Kafka 9 setup is operational. Verify the Kafka broker is running and accessible, and that you have access to the necessary topics from which you wish to move data. Use tools like `kafka-topics.sh` to list and describe topics, ensuring you have the correct configurations and access rights to read the data.
Write a custom Kafka consumer in a language of your choice (such as Java, Python, or Node.js) that can connect to your Kafka cluster. Utilize the Kafka client libraries to subscribe to the desired topic(s) and consume the messages. Ensure the consumer can handle data serialization formats like Avro or JSON, if applicable.
Implement logic within your consumer to process each message as it is consumed. Depending on your data structure and requirements, you may need to transform or clean the data. This step ensures that the data is in the correct format and shape for Typesense. Convert all fields to a JSON format that matches the schema of your Typesense collection.
Install and run a Typesense server. Ensure it is properly configured and accessible from your network. You need to create a collection in Typesense that matches the schema of the transformed data. Use the Typesense API to define the collection and its fields, ensuring they match your data structure.
Write a script or module, again in a language of your choice, that connects to your Typesense server. This script will use the Typesense client library to index documents. The script should take the JSON data from your Kafka consumer, and for each data batch, make API requests to Typesense to index the documents.
For efficiency, implement batching logic in your Kafka consumer and Typesense ingestion script. Instead of sending one document at a time, collect a batch of documents and send them in a single request to Typesense. This reduces overhead and improves performance. Monitor the batch size to balance network and processing efficiency with memory constraints.
Continuously monitor the data pipeline for errors and performance issues. Implement logging and error handling in both your Kafka consumer and Typesense ingestion script. Ensure that the pipeline can handle outages or network issues by implementing retry logic. Regularly review the data integrity and performance of both Kafka and Typesense to ensure that the data is correctly indexed and accessible.
By following these steps, you can effectively move data from Kafka 9 to Typesense without relying on third-party connectors or integrations, ensuring a custom and optimized data flow tailored to your needs.
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:
