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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. Customer data: You can extract customer information such as name, address, contact details, and order history.
2. Product data: You can extract product information such as name, description, price, stock levels, and SKU.
3. Order data: You can extract order information such as order number, customer details, product details, order status, and payment information.
4. Inventory data: You can extract inventory information such as stock levels, product location, and product movement history.
5. Sales data: You can extract sales data such as revenue, profit, and sales trends.
6. Financial data: You can extract financial data such as invoices, payments, and expenses.
7. Shipping data: You can extract shipping information such as tracking numbers, carrier information, and delivery status.
8. Employee data: You can extract employee information such as name, contact details, and job title.
9. Supplier data: You can extract supplier information such as name, contact details, and order history.
10. CRM data: You can extract customer relationship management data such as leads, opportunities, and customer interactions.
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. Customer data: You can extract customer information such as name, address, contact details, and order history.
2. Product data: You can extract product information such as name, description, price, stock levels, and SKU.
3. Order data: You can extract order information such as order number, customer details, product details, order status, and payment information.
4. Inventory data: You can extract inventory information such as stock levels, product location, and product movement history.
5. Sales data: You can extract sales data such as revenue, profit, and sales trends.
6. Financial data: You can extract financial data such as invoices, payments, and expenses.
7. Shipping data: You can extract shipping information such as tracking numbers, carrier information, and delivery status.
8. Employee data: You can extract employee information such as name, contact details, and job title.
9. Supplier data: You can extract supplier information such as name, contact details, and order history.
10. CRM data: You can extract customer relationship management data such as leads, opportunities, and customer interactions.
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. Customer data: You can extract customer information such as name, address, contact details, and order history.
2. Product data: You can extract product information such as name, description, price, stock levels, and SKU.
3. Order data: You can extract order information such as order number, customer details, product details, order status, and payment information.
4. Inventory data: You can extract inventory information such as stock levels, product location, and product movement history.
5. Sales data: You can extract sales data such as revenue, profit, and sales trends.
6. Financial data: You can extract financial data such as invoices, payments, and expenses.
7. Shipping data: You can extract shipping information such as tracking numbers, carrier information, and delivery status.
8. Employee data: You can extract employee information such as name, contact details, and job title.
9. Supplier data: You can extract supplier information such as name, contact details, and order history.
10. CRM data: You can extract customer relationship management data such as leads, opportunities, and customer interactions.
1. First, you need to obtain the necessary credentials from your xentral ERP account. You will need the API key and the API secret key. These can be found in your xentral ERP account settings.
2. Once you have the credentials, open the Airbyte platform and navigate to the "Sources" tab.
3. Click on the "Add Source" button and select "xentral ERP" from the list of available sources.
4. In the "Configure xentral ERP" page, enter the API key and API secret key that you obtained from your xentral ERP account.
5. Next, enter the URL of your xentral ERP account in the "Base URL" field.
6. In the "Sync Schema" section, you can choose which tables you want to sync with Airbyte. You can select all tables or only specific ones.
7. Once you have configured the source, click on the "Check Connection" button to test the connection between Airbyte and xentral ERP.
8. If the connection is successful, click on the "Create Source" button to save the configuration.
9. You can now use the xentral ERP source connector to extract data from your xentral ERP account and load it into your destination data warehouse or database.
10. To schedule regular data syncs, you can create a new pipeline in Airbyte and select the xentral ERP source connector as the source.
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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