

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
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


"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"


“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.”


“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”
- Open your Google Sheet.
- Cleanse the data: Make sure the data is in a consistent format that Snowflake can understand. This includes checking data types, date formats, and null values.
- Define headers: Ensure that the first row of your Google Sheet contains the column headers that you will use as field names in Snowflake.
- Export as CSV: Click on File > Download > Comma-separated values (.csv, current sheet). This will download the current sheet to your local machine as a CSV file.
- Log in to Snowflake: Use your credentials to log in to the Snowflake web interface.
- Create a Database and Schema (if not already existing):
CREATE DATABASE IF NOT EXISTS my_database;
USE DATABASE my_database;
CREATE SCHEMA IF NOT EXISTS my_schema;
USE SCHEMA my_schema; - Create a Table: Define a table in Snowflake that matches the structure of your Google Sheets data.
CREATE TABLE my_table (
column1_name column1_datatype,
column2_name column2_datatype,
…
);
- Create a File Format for CSV files (if not already existing):
CREATE FILE FORMAT my_csv_format
TYPE = 'CSV'
FIELD_DELIMITER = ','
SKIP_HEADER = 1
NULL_IF = ('NULL', 'null'); - Create a Stage to hold your CSV file:CREATE STAGE my_stageFILE_FORMAT = my_csv_format;
- Upload the CSV to the Stage:You can use Snowflake's web interface to manually upload the CSV file to the stage you created. Alternatively, you can use Snowflake's PUT command to upload the file from your local machine if you have the Snowflake CLI installed.
PUT file:///path/to/yourfile.csv @my_stage;
Copy the data from the stage to your Snowflake table:
COPY INTO my_table
FROM @my_stage/yourfile.csv
FILE_FORMAT = (FORMAT_NAME = my_csv_format)
ON_ERROR = 'CONTINUE';
Adjust the ON_ERROR parameter based on your preference for handling errors during the copy process.
- Check the loaded data:
- SELECT * FROM my_table;
- Review any errors that occurred during the data load process and adjust your data or table schema as necessary.
- Remove the CSV from the stage after the data load is successful:
REMOVE @my_stage/yourfile.csv;
- Drop the stage and file format if they will not be used again:
DROP STAGE my_stage;DROP FILE FORMAT my_csv_format;
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: