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This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
Set up a source connector to extract data from in Airbyte
Choose from one of 400 sources where you want to import data from. This can be any API tool, cloud data warehouse, database, data lake, files, among other source types. You can even build your own source connector in minutes with our no-code no-code connector builder.
Configure the connection in Airbyte
The Airbyte Open Data Movement Platform
The only open solution empowering data teams to meet growing business demands in the new AI era.
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Move large volumes, fast.
Change Data Capture.
Security from source to destination.
We support the CDC methods your company needs
Log-based CDC
Timestamp-based CDC
Airbyte Open Source
Airbyte Cloud
Airbyte Enterprise
Why choose Airbyte as the backbone of your data infrastructure?
Keep your data engineering costs in check
Get Airbyte hosted where you need it to be
- Airbyte Cloud: Have it hosted by us, with all the security you need (SOC2, ISO, GDPR, HIPAA Conduit).
- Airbyte Enterprise: Have it hosted within your own infrastructure, so your data and secrets never leave it.
White-glove enterprise-level support
Including for your Airbyte Open Source instance with our premium support.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of sources, including API tools, cloud data warehouses, lakes, databases, and files, or even custom sources you can build.
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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.
1. Spreadsheet data: You can extract all the data present in a Zoho Sheet, including the values, formulas, and formatting.
2. Cell data: You can extract data from specific cells in a sheet, including the value, formula, and formatting.
3. Sheet metadata: You can extract metadata about a sheet, such as the sheet name, sheet ID, and the number of rows and columns.
4. User data: You can extract data about the users who have access to a sheet, including their email addresses and permissions.
5. Cell comments: You can extract comments added to specific cells in a sheet.
6. Sheet revisions: You can extract the revision history of a sheet, including the date and time of each revision and the user who made the changes.
7. Sheet settings: You can extract the settings of a sheet, such as the sheet protection, data validation rules, and conditional formatting rules.
8. Sheet views: You can extract the different views of a sheet, including the default view, filtered views, and sorted views.
9. Sheet charts: You can extract the charts present in a sheet, including the chart type, data range, and formatting.
10. Sheet formulas: You can extract the formulas present in a sheet, including the formula type, cell references, and function arguments.
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 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.
1. Spreadsheet data: You can extract all the data present in a Zoho Sheet, including the values, formulas, and formatting.
2. Cell data: You can extract data from specific cells in a sheet, including the value, formula, and formatting.
3. Sheet metadata: You can extract metadata about a sheet, such as the sheet name, sheet ID, and the number of rows and columns.
4. User data: You can extract data about the users who have access to a sheet, including their email addresses and permissions.
5. Cell comments: You can extract comments added to specific cells in a sheet.
6. Sheet revisions: You can extract the revision history of a sheet, including the date and time of each revision and the user who made the changes.
7. Sheet settings: You can extract the settings of a sheet, such as the sheet protection, data validation rules, and conditional formatting rules.
8. Sheet views: You can extract the different views of a sheet, including the default view, filtered views, and sorted views.
9. Sheet charts: You can extract the charts present in a sheet, including the chart type, data range, and formatting.
10. Sheet formulas: You can extract the formulas present in a sheet, including the formula type, cell references, and function arguments.
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 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.
1. Spreadsheet data: You can extract all the data present in a Zoho Sheet, including the values, formulas, and formatting.
2. Cell data: You can extract data from specific cells in a sheet, including the value, formula, and formatting.
3. Sheet metadata: You can extract metadata about a sheet, such as the sheet name, sheet ID, and the number of rows and columns.
4. User data: You can extract data about the users who have access to a sheet, including their email addresses and permissions.
5. Cell comments: You can extract comments added to specific cells in a sheet.
6. Sheet revisions: You can extract the revision history of a sheet, including the date and time of each revision and the user who made the changes.
7. Sheet settings: You can extract the settings of a sheet, such as the sheet protection, data validation rules, and conditional formatting rules.
8. Sheet views: You can extract the different views of a sheet, including the default view, filtered views, and sorted views.
9. Sheet charts: You can extract the charts present in a sheet, including the chart type, data range, and formatting.
10. Sheet formulas: You can extract the formulas present in a sheet, including the formula type, cell references, and function arguments.
1. Open your Zoho Sheets account and navigate to the "Developer Console" section.
2. Click on "Add Client ID" and select "Create New Client ID."
3. Choose "Web Application" as the application type and enter a name for your application.
4. In the "Authorized Redirect URIs" section, add the following URL: https://localhost:8000/api/v1/oauth/callback.
5. Click "Create" and note down the "Client ID" and "Client Secret" values.
6. Open the Airbyte UI and navigate to the "Sources" tab.
7. Click on "Create New Source" and select "Zoho Sheets" from the list of available connectors.
8. Enter a name for your source and paste the "Client ID" and "Client Secret" values from step 5 into the respective fields.
9. Click "Test Connection" to ensure that the credentials are valid.
10. Select the Zoho Sheets spreadsheet you want to connect to and click "Save."
11. You can now use the Zoho Sheets source connector to extract data from your spreadsheet and replicate it to your desired destination.
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.
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