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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. Product information: VTEX's API allows you to extract data related to products, including their name, description, price, and availability.
2. Order information: You can also extract data related to orders, such as order number, customer information, shipping details, and payment information.
3. Inventory information: VTEX's API provides access to inventory data, including stock levels, product variants, and warehouse locations.
4. Customer information: You can extract data related to customers, such as their name, email address, shipping address, and order history.
5. Sales data: VTEX's API allows you to extract data related to sales, including revenue, order volume, and average order value.
6. Marketing data: You can also extract data related to marketing campaigns, such as click-through rates, conversion rates, and revenue generated.
7. Analytics data: VTEX's API provides access to analytics data, including website traffic, user behavior, and conversion rates.
8. Shipping information: You can extract data related to shipping, such as carrier information, tracking numbers, and delivery dates.
9. Payment information: VTEX's API allows you to extract data related to payments, including payment method, transaction ID, and payment status.
10. Reviews and ratings: You can also extract data related to product reviews and ratings, including the number of reviews, average rating, and customer feedback.
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. Product information: VTEX's API allows you to extract data related to products, including their name, description, price, and availability.
2. Order information: You can also extract data related to orders, such as order number, customer information, shipping details, and payment information.
3. Inventory information: VTEX's API provides access to inventory data, including stock levels, product variants, and warehouse locations.
4. Customer information: You can extract data related to customers, such as their name, email address, shipping address, and order history.
5. Sales data: VTEX's API allows you to extract data related to sales, including revenue, order volume, and average order value.
6. Marketing data: You can also extract data related to marketing campaigns, such as click-through rates, conversion rates, and revenue generated.
7. Analytics data: VTEX's API provides access to analytics data, including website traffic, user behavior, and conversion rates.
8. Shipping information: You can extract data related to shipping, such as carrier information, tracking numbers, and delivery dates.
9. Payment information: VTEX's API allows you to extract data related to payments, including payment method, transaction ID, and payment status.
10. Reviews and ratings: You can also extract data related to product reviews and ratings, including the number of reviews, average rating, and customer feedback.
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. Product information: VTEX's API allows you to extract data related to products, including their name, description, price, and availability.
2. Order information: You can also extract data related to orders, such as order number, customer information, shipping details, and payment information.
3. Inventory information: VTEX's API provides access to inventory data, including stock levels, product variants, and warehouse locations.
4. Customer information: You can extract data related to customers, such as their name, email address, shipping address, and order history.
5. Sales data: VTEX's API allows you to extract data related to sales, including revenue, order volume, and average order value.
6. Marketing data: You can also extract data related to marketing campaigns, such as click-through rates, conversion rates, and revenue generated.
7. Analytics data: VTEX's API provides access to analytics data, including website traffic, user behavior, and conversion rates.
8. Shipping information: You can extract data related to shipping, such as carrier information, tracking numbers, and delivery dates.
9. Payment information: VTEX's API allows you to extract data related to payments, including payment method, transaction ID, and payment status.
10. Reviews and ratings: You can also extract data related to product reviews and ratings, including the number of reviews, average rating, and customer feedback.
1. First, you need to obtain your VTEX API credentials, which include your account name, app key, and app token. You can find these credentials in your VTEX account settings.
2. Once you have your credentials, open the Airbyte dashboard and click on "Sources" in the left-hand menu.
3. Click on the "Create a new source" button and select "VTEX" from the list of available connectors.
4. In the VTEX source configuration page, enter your VTEX account name, app key, and app token in the corresponding fields.
5. Click on the "Test connection" button to verify that your credentials are correct and that Airbyte can connect to your VTEX account.
6. If the connection test is successful, click on the "Create source" button to save your VTEX source configuration.
7. You can now use your VTEX source connector to extract data from your VTEX account and replicate it to your destination data warehouse or database.
8. To set up a replication job, go to the Airbyte dashboard and click on "Destinations" in the left-hand menu.
9. Select your destination connector and follow the instructions to set up a new destination configuration.
10. Once you have your destination configured, go back to the VTEX source configuration page and click on the "Create new sync" button.
11. Select your destination configuration from the list of available destinations and follow the instructions to set up a new sync job.
12. You can now run your sync job to replicate data from your VTEX account to your 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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