Jira is a project management tool by Atlassian for bug tracking, issue tracking, and agile project management. It enhances team collaboration and workflow efficiency.
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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.
Jira is an issue tracking software by Atlassian that assists developers in bug tracking and agile project management. With software support throughout the entire development process, from planning to tracking, to the final release, and reports based on real-time data to improve team performance, Jira is the go-to software development tool for agile teams.
Jira's API provides access to a wide range of data related to project management and issue tracking. The following are the categories of data that can be accessed through Jira's API:
1. Issues: This includes all the information related to the issues such as issue type, status, priority, description, comments, attachments, and more.
2. Projects: This includes information about the projects such as project name, description, project lead, and more.
3. Users: This includes information about the users such as user name, email address, and more.
4. Workflows: This includes information about the workflows such as workflow name, workflow steps, and more.
5. Custom fields: This includes information about the custom fields such as custom field name, type, and more.
6. Dashboards: This includes information about the dashboards such as dashboard name, description, and more.
7. Reports: This includes information about the reports such as report name, description, and more.
8. Agile boards: This includes information about the agile boards such as board name, board type, and more.
Overall, Jira's API provides access to a vast amount of data that can be used to improve project management and issue tracking.
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.
Jira is an issue tracking software by Atlassian that assists developers in bug tracking and agile project management. With software support throughout the entire development process, from planning to tracking, to the final release, and reports based on real-time data to improve team performance, Jira is the go-to software development tool for agile teams.
Jira's API provides access to a wide range of data related to project management and issue tracking. The following are the categories of data that can be accessed through Jira's API:
1. Issues: This includes all the information related to the issues such as issue type, status, priority, description, comments, attachments, and more.
2. Projects: This includes information about the projects such as project name, description, project lead, and more.
3. Users: This includes information about the users such as user name, email address, and more.
4. Workflows: This includes information about the workflows such as workflow name, workflow steps, and more.
5. Custom fields: This includes information about the custom fields such as custom field name, type, and more.
6. Dashboards: This includes information about the dashboards such as dashboard name, description, and more.
7. Reports: This includes information about the reports such as report name, description, and more.
8. Agile boards: This includes information about the agile boards such as board name, board type, and more.
Overall, Jira's API provides access to a vast amount of data that can be used to improve project management and issue tracking.
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.
Jira is an issue tracking software by Atlassian that assists developers in bug tracking and agile project management. With software support throughout the entire development process, from planning to tracking, to the final release, and reports based on real-time data to improve team performance, Jira is the go-to software development tool for agile teams.
Jira's API provides access to a wide range of data related to project management and issue tracking. The following are the categories of data that can be accessed through Jira's API:
1. Issues: This includes all the information related to the issues such as issue type, status, priority, description, comments, attachments, and more.
2. Projects: This includes information about the projects such as project name, description, project lead, and more.
3. Users: This includes information about the users such as user name, email address, and more.
4. Workflows: This includes information about the workflows such as workflow name, workflow steps, and more.
5. Custom fields: This includes information about the custom fields such as custom field name, type, and more.
6. Dashboards: This includes information about the dashboards such as dashboard name, description, and more.
7. Reports: This includes information about the reports such as report name, description, and more.
8. Agile boards: This includes information about the agile boards such as board name, board type, and more.
Overall, Jira's API provides access to a vast amount of data that can be used to improve project management and issue tracking.
1. First, navigate to the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.
2. Click on the "Add Source" button in the top right corner of the screen.
3. Select "Jira" from the list of available sources.
4. Enter a name for your Jira source connector and click "Next".
5. Enter your Jira credentials, including the Jira URL, email address, and API token.
6. Test the connection to ensure that the credentials are correct and the connection is successful.
7. Select the Jira projects and issue types that you want to replicate in Airbyte.
8. Choose the replication frequency and any other settings that you want to apply to your Jira source connector.
9. Click "Create Source" to save your Jira source connector and begin replicating data from Jira to Airbyte.
It is important to note that the specific steps for connecting your Jira source connector may vary depending on your specific use case and the version of Jira that you are using. For more detailed instructions and troubleshooting tips, refer to the Airbyte documentation or consult with a Jira expert.
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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