Top companies trust Airbyte to centralize their Data
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. Search results: Splunk's API allows you to extract search results from the Splunk platform. This includes data from logs, metrics, and other sources.
2. Metrics data: You can extract metrics data from Splunk's API, including data on system performance, network traffic, and other key metrics.
3. Event data: Splunk's API allows you to extract event data, including data on user activity, system events, and other important events.
4. Alert data: You can extract alert data from Splunk's API, including data on alerts triggered by specific events or conditions.
5. Configuration data: Splunk's API allows you to extract configuration data, including data on system settings, user permissions, and other configuration details.
6. User data: You can extract user data from Splunk's API, including data on user activity, login history, and other user-related information.
7. Dashboard data: Splunk's API allows you to extract data from dashboards, including data on visualizations, charts, and other dashboard elements.
8. Report data: You can extract report data from Splunk's API, including data on report results, report configurations, and other report-related information.
9. Index data: Splunk's API allows you to extract index data, including data on index settings, index performance, and other index-related information.
10. Search job data: You can extract search job data from Splunk's API, including data on search job status, search job results, and other search job-related information.
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. Search results: Splunk's API allows you to extract search results from the Splunk platform. This includes data from logs, metrics, and other sources.
2. Metrics data: You can extract metrics data from Splunk's API, including data on system performance, network traffic, and other key metrics.
3. Event data: Splunk's API allows you to extract event data, including data on user activity, system events, and other important events.
4. Alert data: You can extract alert data from Splunk's API, including data on alerts triggered by specific events or conditions.
5. Configuration data: Splunk's API allows you to extract configuration data, including data on system settings, user permissions, and other configuration details.
6. User data: You can extract user data from Splunk's API, including data on user activity, login history, and other user-related information.
7. Dashboard data: Splunk's API allows you to extract data from dashboards, including data on visualizations, charts, and other dashboard elements.
8. Report data: You can extract report data from Splunk's API, including data on report results, report configurations, and other report-related information.
9. Index data: Splunk's API allows you to extract index data, including data on index settings, index performance, and other index-related information.
10. Search job data: You can extract search job data from Splunk's API, including data on search job status, search job results, and other search job-related information.
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. Search results: Splunk's API allows you to extract search results from the Splunk platform. This includes data from logs, metrics, and other sources.
2. Metrics data: You can extract metrics data from Splunk's API, including data on system performance, network traffic, and other key metrics.
3. Event data: Splunk's API allows you to extract event data, including data on user activity, system events, and other important events.
4. Alert data: You can extract alert data from Splunk's API, including data on alerts triggered by specific events or conditions.
5. Configuration data: Splunk's API allows you to extract configuration data, including data on system settings, user permissions, and other configuration details.
6. User data: You can extract user data from Splunk's API, including data on user activity, login history, and other user-related information.
7. Dashboard data: Splunk's API allows you to extract data from dashboards, including data on visualizations, charts, and other dashboard elements.
8. Report data: You can extract report data from Splunk's API, including data on report results, report configurations, and other report-related information.
9. Index data: Splunk's API allows you to extract index data, including data on index settings, index performance, and other index-related information.
10. Search job data: You can extract search job data from Splunk's API, including data on search job status, search job results, and other search job-related information.
1. First, navigate to the Airbyte dashboard and click on "Sources" in the left-hand menu.
2. Click on the "Create Source" button in the top right corner of the screen.
3. Select "Splunk" from the list of available sources.
4. Enter a name for your Splunk source and click "Next".
5. Enter the hostname or IP address of your Splunk instance, along with the port number and protocol (HTTP or HTTPS).
6. Enter your Splunk username and password.
7. If you have a custom search query you'd like to use, enter it in the "Search Query" field. Otherwise, leave it blank.
8. Choose the data you'd like to replicate from Splunk by selecting the appropriate index, source type, and time range.
9. Click "Test Connection" to ensure that your credentials are correct and that Airbyte can connect to your Splunk instance.
10. If the connection is successful, click "Create Source" to save your Splunk source in Airbyte.
11. You can now use this source to replicate data from Splunk to your destination of choice.
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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