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 MySQL database is properly configured and accessible. Verify that the MySQL server is running, and you have the necessary permissions to export data. Identify the tables and data you need to transfer, and check for any specific data types or structures that might require special handling.
Use the `mysqldump` utility to export data from MySQL. This utility allows you to create a dump file of your database or specific tables in SQL format. Execute a command like:
```bash
mysqldump -u [username] -p [database_name] [table_name(s)] > data_dump.sql
```
Replace `[username]`, `[database_name]`, and `[table_name(s)]` with your actual database and table names. This creates a file (`data_dump.sql`) containing SQL statements for your data.
Review the SQL dump file for compatibility with Teradata SQL syntax. MySQL and Teradata have different SQL dialects, so you may need to adjust data types, remove unsupported functions, and modify any MySQL-specific syntax. Focus on ensuring that data type conversions are correctly handled.
Ensure your Teradata Vantage system is ready to receive data. Confirm that you have access to the database and appropriate permissions to create tables and load data. Create the necessary tables in Teradata that match the structure of your MySQL tables, considering any adjustments made in the previous step.
Use the `bteq` utility or Teradata's SQL Assistant to load the transformed SQL data into Teradata. First, transfer the `data_dump.sql` to the system where Teradata is accessible. Then, execute the SQL file using:
```bash
bteq < data_dump.sql
```
Ensure that your connection details and credentials are configured correctly in the `bteq` environment for successful execution.
After loading data into Teradata, perform a thorough check to confirm data integrity. Compare row counts and data values between MySQL and Teradata databases. Use queries to verify that all data has been transferred correctly and no corruption has occurred during the process.
Once data is successfully transferred and verified, optimize the data in Teradata for performance. Analyze query patterns and create indexes where necessary to speed up data retrieval. Consider using Teradata's statistics collection features to improve query execution plans and overall system performance.
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: