Summarize this article with:
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 querying the data you need from Teradata. Use Teradata SQL to export the desired tables or datasets to a CSV file or another flat file format. You can use Teradata's BTEQ utility or SQL Assistant for this task. Ensure that your export includes all necessary columns and rows, and consider any data type conversions that might be needed for compatibility with Firebolt.
Once you have the data in a flat file, inspect it for any inconsistencies or issues. Normalize the data format as needed, ensuring that date formats, numeric precision, and character encodings are consistent and compatible with Firebolt's requirements. This step is crucial to prevent data loading errors later.
Before uploading data, you need to define the schema in Firebolt that matches the structure of your Teradata data. Use Firebolt's SQL syntax to create tables with appropriate data types and constraints. Make sure the schema accurately reflects the structure of your exported data.
Firebolt requires data to be accessible through cloud storage services (such as Amazon S3) for ingestion. Transfer your prepared flat files to a cloud storage bucket that Firebolt can access. Ensure you have the necessary permissions set up for Firebolt to read from this storage location.
Use Firebolt's COPY command to load data from the cloud storage into your Firebolt tables. The COPY command allows you to specify file format and delimiter settings to match your data file structure. Monitor the loading process for any errors or warnings, and address any issues that arise.
After loading the data, run validation checks to ensure data integrity. Compare row counts and column values between Teradata and Firebolt to verify that the data has been transferred accurately. Use SQL queries to perform spot checks and summary comparisons.
Once data is loaded and validated, optimize the Firebolt tables for performance. This includes creating appropriate indexes and partitioning tables, if necessary, to enhance query performance. Use Firebolt's performance tuning tools and best practices to ensure efficient data retrieval.
Following these steps will help you successfully transfer data from Teradata to Firebolt without relying on external 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:
