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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
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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: The Atlassian Marketplace connector's API provides data on the products available on the Atlassian Marketplace, including their names, descriptions, and pricing.
2. User information: The API can also extract data on users who have installed or purchased products from the Atlassian Marketplace, including their names, email addresses, and user IDs.
3. Product usage data: The API can provide information on how often a product is being used, how many users are using it, and how much data is being processed.
4. Reviews and ratings: The API can extract data on the reviews and ratings of products on the Atlassian Marketplace, including the number of reviews, average rating, and individual review comments.
5. Sales data: The API can provide information on the sales of products on the Atlassian Marketplace, including the number of sales, revenue generated, and payment details.
6. Licensing information: The API can extract data on the licensing of products on the Atlassian Marketplace, including the type of license, expiration date, and renewal options.
7. Integration data: The API can provide information on the integration of products with other Atlassian tools, including compatibility, installation instructions, and configuration options.
8. Support data: The API can extract data on the support provided for products on the Atlassian Marketplace, including support options, response times, and customer satisfaction ratings.
9. Developer information: The API can provide data on the developers who have created products on the Atlassian Marketplace, including their names, contact information, and development history.
10. Marketplace trends: The API can extract data on the trends and patterns in the Atlassian Marketplace, including the most popular products, emerging technologies, and user behavior.
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: The Atlassian Marketplace connector's API provides data on the products available on the Atlassian Marketplace, including their names, descriptions, and pricing.
2. User information: The API can also extract data on users who have installed or purchased products from the Atlassian Marketplace, including their names, email addresses, and user IDs.
3. Product usage data: The API can provide information on how often a product is being used, how many users are using it, and how much data is being processed.
4. Reviews and ratings: The API can extract data on the reviews and ratings of products on the Atlassian Marketplace, including the number of reviews, average rating, and individual review comments.
5. Sales data: The API can provide information on the sales of products on the Atlassian Marketplace, including the number of sales, revenue generated, and payment details.
6. Licensing information: The API can extract data on the licensing of products on the Atlassian Marketplace, including the type of license, expiration date, and renewal options.
7. Integration data: The API can provide information on the integration of products with other Atlassian tools, including compatibility, installation instructions, and configuration options.
8. Support data: The API can extract data on the support provided for products on the Atlassian Marketplace, including support options, response times, and customer satisfaction ratings.
9. Developer information: The API can provide data on the developers who have created products on the Atlassian Marketplace, including their names, contact information, and development history.
10. Marketplace trends: The API can extract data on the trends and patterns in the Atlassian Marketplace, including the most popular products, emerging technologies, and user behavior.
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: The Atlassian Marketplace connector's API provides data on the products available on the Atlassian Marketplace, including their names, descriptions, and pricing.
2. User information: The API can also extract data on users who have installed or purchased products from the Atlassian Marketplace, including their names, email addresses, and user IDs.
3. Product usage data: The API can provide information on how often a product is being used, how many users are using it, and how much data is being processed.
4. Reviews and ratings: The API can extract data on the reviews and ratings of products on the Atlassian Marketplace, including the number of reviews, average rating, and individual review comments.
5. Sales data: The API can provide information on the sales of products on the Atlassian Marketplace, including the number of sales, revenue generated, and payment details.
6. Licensing information: The API can extract data on the licensing of products on the Atlassian Marketplace, including the type of license, expiration date, and renewal options.
7. Integration data: The API can provide information on the integration of products with other Atlassian tools, including compatibility, installation instructions, and configuration options.
8. Support data: The API can extract data on the support provided for products on the Atlassian Marketplace, including support options, response times, and customer satisfaction ratings.
9. Developer information: The API can provide data on the developers who have created products on the Atlassian Marketplace, including their names, contact information, and development history.
10. Marketplace trends: The API can extract data on the trends and patterns in the Atlassian Marketplace, including the most popular products, emerging technologies, and user behavior.
1. Open the Atlassian Marketplace connector in Airbyte.
2. Click on the "Configuration" tab.
3. Under "Source Configuration", click on "Add New".
4. Enter a name for the connector. 5. Enter the following credentials:
- "Base URL": the URL of your Atlassian Marketplace account.
- "Username": your Atlassian Marketplace username.
- "Password": your Atlassian Marketplace password.
6. Click on "Test Connection" to ensure that the credentials are correct.
7. Once the connection is successful, click on "Save".
8. Click on the "Sync" tab.
9. Click on "Create New Sync".
10. Select the Atlassian Marketplace connector as the source connector.
11. Select the destination connector for your data.
12. Configure the sync settings as desired.
13. Click on "Create Sync" to start syncing your data.
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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