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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. Order information: You can extract data related to orders such as order number, order date, order status, shipping address, billing address, and order items.
2. Shipping information: You can extract data related to shipping such as shipping carrier, shipping method, tracking number, shipping cost, and shipping label.
3. Customer information: You can extract data related to customers such as customer name, email address, phone number, and shipping address.
4. Product information: You can extract data related to products such as product name, SKU, weight, and dimensions.
5. Inventory information: You can extract data related to inventory such as stock levels, backorders, and product availability.
6. Returns information: You can extract data related to returns such as return reason, return status, and return shipping information.
7. Reports: You can extract data related to reports such as sales reports, shipping reports, and inventory reports.
8. User information: You can extract data related to users such as user name, email address, and user role.
9. Store information: You can extract data related to stores such as store name, store URL, and store logo.
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. Order information: You can extract data related to orders such as order number, order date, order status, shipping address, billing address, and order items.
2. Shipping information: You can extract data related to shipping such as shipping carrier, shipping method, tracking number, shipping cost, and shipping label.
3. Customer information: You can extract data related to customers such as customer name, email address, phone number, and shipping address.
4. Product information: You can extract data related to products such as product name, SKU, weight, and dimensions.
5. Inventory information: You can extract data related to inventory such as stock levels, backorders, and product availability.
6. Returns information: You can extract data related to returns such as return reason, return status, and return shipping information.
7. Reports: You can extract data related to reports such as sales reports, shipping reports, and inventory reports.
8. User information: You can extract data related to users such as user name, email address, and user role.
9. Store information: You can extract data related to stores such as store name, store URL, and store logo.
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. Order information: You can extract data related to orders such as order number, order date, order status, shipping address, billing address, and order items.
2. Shipping information: You can extract data related to shipping such as shipping carrier, shipping method, tracking number, shipping cost, and shipping label.
3. Customer information: You can extract data related to customers such as customer name, email address, phone number, and shipping address.
4. Product information: You can extract data related to products such as product name, SKU, weight, and dimensions.
5. Inventory information: You can extract data related to inventory such as stock levels, backorders, and product availability.
6. Returns information: You can extract data related to returns such as return reason, return status, and return shipping information.
7. Reports: You can extract data related to reports such as sales reports, shipping reports, and inventory reports.
8. User information: You can extract data related to users such as user name, email address, and user role.
9. Store information: You can extract data related to stores such as store name, store URL, and store logo.
1. First, navigate to the Shipstation source connector page on Airbyte.com.
2. Click on the "Add Source" button to begin the process of adding your Shipstation credentials.
3. Enter your Shipstation API Key and API Secret Key in the appropriate fields.
4. Click on the "Test" button to ensure that your credentials are correct and that Airbyte can connect to your Shipstation account.
5. Once the test is successful, click on the "Save" button to save your credentials.
6. You will then be prompted to select the data you want to replicate from Shipstation. You can choose to replicate orders, shipments, or both.
7. After selecting the data you want to replicate, click on the "Next" button to proceed.
8. You will then be prompted to select the destination where you want to replicate your Shipstation data. You can choose from a variety of destinations, including databases, data warehouses, and cloud storage services.
9. After selecting your destination, click on the "Create Connection" button to complete the process of connecting your Shipstation source connector on Airbyte.com.
10. Your Shipstation data will now be replicated to your chosen destination on a regular basis, according to the schedule you set up in Airbyte.
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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