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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 Server is a project management tool developed by Atlassian that helps teams to plan, track, and manage their work. It is a powerful tool that allows teams to collaborate and stay organized throughout the entire project lifecycle. Jira Server is designed to be flexible and customizable, allowing teams to tailor it to their specific needs. With Jira Server, teams can create and manage tasks, assign them to team members, and track their progress. It also provides a range of reporting and analytics features that help teams to identify bottlenecks and improve their workflows. Jira Server also integrates with a range of other tools, such as Confluence, Bitbucket, and Slack, making it a central hub for team collaboration. Jira Server is particularly useful for software development teams, as it provides a range of features specifically designed for agile development methodologies, such as Scrum and Kanban. It also supports continuous integration and delivery, allowing teams to automate their development processes and deliver high-quality software faster. Overall, Jira Server is a powerful and flexible tool that helps teams to stay organized, collaborate effectively, and deliver high-quality work.
1. Issue data: You can extract information about issues such as their status, priority, assignee, reporter, description, comments, attachments, and more.
2. Project data: You can extract information about projects such as their name, key, description, lead, and more.
3. User data: You can extract information about users such as their name, email, role, and more.
4. Workflow data: You can extract information about workflows such as their name, status, and transitions.
5. Version data: You can extract information about versions such as their name, release date, and status.
6. Component data: You can extract information about components such as their name, description, and lead.
7. Custom field data: You can extract information about custom fields such as their name, type, and value.
8. Board data: You can extract information about boards such as their name, type, and filter.
9. Sprint data: You can extract information about sprints such as their name, start and end dates, and status.
10. Dashboard data: You can extract information about dashboards such as their name, owner, and gadgets.
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 Server is a project management tool developed by Atlassian that helps teams to plan, track, and manage their work. It is a powerful tool that allows teams to collaborate and stay organized throughout the entire project lifecycle. Jira Server is designed to be flexible and customizable, allowing teams to tailor it to their specific needs. With Jira Server, teams can create and manage tasks, assign them to team members, and track their progress. It also provides a range of reporting and analytics features that help teams to identify bottlenecks and improve their workflows. Jira Server also integrates with a range of other tools, such as Confluence, Bitbucket, and Slack, making it a central hub for team collaboration. Jira Server is particularly useful for software development teams, as it provides a range of features specifically designed for agile development methodologies, such as Scrum and Kanban. It also supports continuous integration and delivery, allowing teams to automate their development processes and deliver high-quality software faster. Overall, Jira Server is a powerful and flexible tool that helps teams to stay organized, collaborate effectively, and deliver high-quality work.
1. Issue data: You can extract information about issues such as their status, priority, assignee, reporter, description, comments, attachments, and more.
2. Project data: You can extract information about projects such as their name, key, description, lead, and more.
3. User data: You can extract information about users such as their name, email, role, and more.
4. Workflow data: You can extract information about workflows such as their name, status, and transitions.
5. Version data: You can extract information about versions such as their name, release date, and status.
6. Component data: You can extract information about components such as their name, description, and lead.
7. Custom field data: You can extract information about custom fields such as their name, type, and value.
8. Board data: You can extract information about boards such as their name, type, and filter.
9. Sprint data: You can extract information about sprints such as their name, start and end dates, and status.
10. Dashboard data: You can extract information about dashboards such as their name, owner, and gadgets.
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 Server is a project management tool developed by Atlassian that helps teams to plan, track, and manage their work. It is a powerful tool that allows teams to collaborate and stay organized throughout the entire project lifecycle. Jira Server is designed to be flexible and customizable, allowing teams to tailor it to their specific needs. With Jira Server, teams can create and manage tasks, assign them to team members, and track their progress. It also provides a range of reporting and analytics features that help teams to identify bottlenecks and improve their workflows. Jira Server also integrates with a range of other tools, such as Confluence, Bitbucket, and Slack, making it a central hub for team collaboration. Jira Server is particularly useful for software development teams, as it provides a range of features specifically designed for agile development methodologies, such as Scrum and Kanban. It also supports continuous integration and delivery, allowing teams to automate their development processes and deliver high-quality software faster. Overall, Jira Server is a powerful and flexible tool that helps teams to stay organized, collaborate effectively, and deliver high-quality work.
1. Issue data: You can extract information about issues such as their status, priority, assignee, reporter, description, comments, attachments, and more.
2. Project data: You can extract information about projects such as their name, key, description, lead, and more.
3. User data: You can extract information about users such as their name, email, role, and more.
4. Workflow data: You can extract information about workflows such as their name, status, and transitions.
5. Version data: You can extract information about versions such as their name, release date, and status.
6. Component data: You can extract information about components such as their name, description, and lead.
7. Custom field data: You can extract information about custom fields such as their name, type, and value.
8. Board data: You can extract information about boards such as their name, type, and filter.
9. Sprint data: You can extract information about sprints such as their name, start and end dates, and status.
10. Dashboard data: You can extract information about dashboards such as their name, owner, and gadgets.
1. First, navigate to the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.
2. Click on the "Jira Server" source connector and then click "Create new connection".
3. Enter a name for your connection and click "Next".
4. Enter the URL for your Jira Server instance and click "Next".
5. Enter your Jira Server username and password and click "Next".
6. Select the projects you want to sync and click "Next".
7. Choose the sync mode you want to use (either full refresh or incremental) and click "Next".
8. Review your connection settings and click "Create".
9. Once your connection is created, you can run a sync to start pulling data from Jira Server into Airbyte.
10. You can also schedule regular syncs to keep your data up-to-date.
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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