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."
Start by exporting the data from ClickHouse. You can use the ClickHouse command-line interface (CLI) to perform this task. Execute a SQL query to select the data you want to export and save the output to a file in a format like CSV or TSV. For example:
```bash
clickhouse-client --query="SELECT * FROM your_table" --format=CSV > data.csv
```
Ensure that the exported data file is formatted correctly and ready for transfer. Check for any special characters or formatting issues that might cause problems during the import process. If necessary, clean the data using a text editor or a scripting language like Python.
Transfer the exported data file to the environment where Starburst Galaxy can access it. You can do this using secure methods like SCP (Secure Copy Protocol) or SFTP (SSH File Transfer Protocol) to ensure the data is transferred safely. For example:
```bash
scp data.csv user@starburst-server:/desired/directory/
```
Log in to your Starburst Galaxy environment and set up a table schema that matches the data structure of the ClickHouse data. Use the Starburst SQL interface to define the table with the appropriate columns and data types.
Use the Starburst SQL interface to load the data from the transferred file into the Starburst Galaxy table. Execute a SQL command to import the data from the file. For example:
```sql
COPY your_table FROM '/desired/directory/data.csv' WITH (FORMAT CSV);
```
After loading the data, perform validation checks to ensure that all records were imported correctly. Run SQL queries to compare record counts and data integrity between the original ClickHouse dataset and the imported Starburst Galaxy dataset.
Once the data is validated, optimize the table for query performance in Starburst Galaxy. Create any necessary indexes and analyze the table if Starburst Galaxy supports it to improve query execution times. Optimize the data layout as needed to ensure efficient data retrieval.
By following these steps, you can successfully move data from ClickHouse to Starburst Galaxy without relying on third-party connectors or integrations.
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.
An open-source database management system for online analytical processing (OLAP), ClickHouse takes the innovative approach of using a column-based database. It is easy to use right out of the box and is touted as being hardware efficient, extremely reliable, linearly scalable, and “blazing fast”—between 100-1,000x faster than traditional databases that write rows of data to the disk—allowing analytical data reports to be generated in real-time.
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:
