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
"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."
Begin by thoroughly understanding the format and structure of the source data you intend to move. This involves identifying data types, data size, and any specific requirements such as encoding that might affect the transfer process.
Once you understand the data structure, prepare the data for export. If it's stored in a database, use SQL queries to export the data into a CSV or TXT file format. Ensure that the exported file is clean, meaning no corrupt rows or invalid entries that could disrupt the import process.
Gain access to your Teradata Vantage environment. This includes ensuring you have the necessary credentials, permissions, and network access to log in and perform data import operations. Familiarize yourself with the Teradata SQL Assistant or BTEQ (Basic Teradata Query) tool, as these will be used for data loading.
Before importing data, create the necessary tables in Teradata to hold the data. Use the `CREATE TABLE` statement to define the schema corresponding to your source data. Make sure the data types in Teradata match or are compatible with those of the source data to avoid type conversion errors.
Transfer the exported data files to a location accessible by Teradata. This could be a network shared drive or a directory on the Teradata server itself where you have write access. Use secure file transfer methods like SCP or SFTP if transferring files over the network.
Utilize Teradata's native tools such as FastLoad, MultiLoad, or TPT (Teradata Parallel Transporter) to import the data files into Teradata tables. These tools are specifically designed to handle large data transfers efficiently. For example, a FastLoad script can be used to import CSV files directly into Teradata tables.
After loading the data, perform checks to ensure the data integrity in Teradata matches the source data. This can be done by running queries to count rows and verify sample records. If there are discrepancies, investigate and reload the necessary data. Finally, clean up any temporary files or tables used during the import process to maintain a tidy environment.
By following these steps, you can efficiently move data to Teradata Vantage 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.
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:
