


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 exporting the required data from PostgreSQL. Use the `COPY` command to export data from a PostgreSQL table to a CSV file. This command outputs data into a plain text format, which is easily transferable.
```sql
COPY your_table TO '/path/to/your_data.csv' WITH (FORMAT CSV, HEADER);
```
Ensure that the CSV file is properly formatted. Check for any special characters, delimiters, or newline issues that might cause problems during the import process into Teradata. It's important that the data types in the CSV align with those expected in Teradata.
Move the CSV file to the Teradata server environment. This can be done using secure file transfer methods like SCP or SFTP if the systems are on separate machines. Ensure that the file permissions are set correctly to allow reading by the user that will perform the import.
Before importing the data, create the target table in Teradata with a structure that matches the CSV file. Use the `CREATE TABLE` statement, specifying appropriate data types for each column. This ensures that data is correctly interpreted upon import.
```sql
CREATE TABLE your_teradata_table (
column1 INTEGER,
column2 VARCHAR(255),
...
);
```
Use Teradata’s `TPT` (Teradata Parallel Transporter) or `FastLoad` utility to import the CSV data into the Teradata table. These utilities are designed for efficient bulk loading of data.
For example, using `FastLoad`:
```bash
fastload < my_fastload_script.txt
```
Ensure your script file is correctly configured to specify the CSV file location, table name, and the mapping of CSV columns to table columns.
After loading the data, validate that all records have been imported correctly. Perform counts and checks on key data points to ensure data integrity. Compare row counts between the PostgreSQL source and the Teradata target table.
```sql
SELECT COUNT() FROM your_teradata_table;
```
Once the data is confirmed to be imported correctly, apply any necessary indexes or optimization techniques on the Teradata table to improve query performance. This might include creating primary indexes or collecting statistics.
```sql
COLLECT STATISTICS ON your_teradata_table COLUMN (column_name);
```
By following these steps, you can effectively transfer data from PostgreSQL to Teradata without relying on third-party connectors, leveraging the capabilities of each database system.
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 object-relational database management system, PostgreSQL is able to handle a wide range of workloads, supports multiple standards, and is cross-platform, running on numerous operating systems including Microsoft Windows, Solaris, Linux, and FreeBSD. It is highly extensible, and supports more than 12 procedural languages, Spatial data support, Gin and GIST Indexes, and more. Many webs, mobile, and analytics applications use PostgreSQL as the primary data warehouse or data store.
PostgreSQL gives access to a wide range of data types, including:
1. Numeric data types: This includes integers, floating-point numbers, and decimal numbers.
2. Character data types: This includes strings, text, and character arrays.
3. Date and time data types: This includes dates, times, and timestamps.
4. Boolean data types: This includes true/false values.
5. Network address data types: This includes IP addresses and MAC addresses.
6. Geometric data types: This includes points, lines, and polygons.
7. Array data types: This includes arrays of any of the above data types.
8. JSON and JSONB data types: This includes JSON objects and arrays.
9. XML data types: This includes XML documents.
10. Composite data types: This includes user-defined data types that can contain multiple fields of different data types.
Overall, PostgreSQL's API provides access to a wide range of data types, making it a versatile and powerful tool for data management and analysis.
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: