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"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"
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Sync with Airbyte
1. First, navigate to the Harness dashboard and select the "Connectors" tab from the left-hand menu.
2. Click on the "Add Connector" button and select "Airbyte" from the list of available connectors.
3. In the "Connection Settings" section, enter the URL for your Airbyte instance.
4. Next, enter the API key for your Airbyte instance in the "API Key" field.
5. In the "Source Settings" section, select the source connector you want to connect to Harness.
6. Enter the necessary credentials for the selected source connector, such as the username and password.
7. Click the "Test Connection" button to ensure that the connection is successful.
8. If the connection is successful, click the "Save" button to save the connector configuration.
9. You can now use the connector in your Harness workflows to extract data from the source connector and load it into your destination system.
1. First, navigate to the connectors page on Airbyte and select the Starburst Galaxy destination connector.
2. Next, enter the required credentials for your Starburst Galaxy account, including the host, port, database name, username, and password.
3. Once you have entered your credentials, click on the "Test Connection" button to ensure that the connection is successful.
4. If the connection is successful, you can then configure the settings for your destination connector, including the table name, schema, and any additional options.
5. After configuring your settings, you can then run a sync to transfer data from your source connector to your Starburst Galaxy destination.
6. You can monitor the progress of your sync and view any errors or warnings that may occur during the transfer process.
7. Once the sync is complete, you can then view your data in your Starburst Galaxy database and use it for analysis or other purposes.
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.
The harness is the industry’s first Software Delivery stage to use AI to facilitate your DevOps processes - CI, CD & GitOps, Feature Flags, Cloud Costs, and much more. Our AI takes your distribution pipelines to the next level. You can automate yellow verifications, prioritize what tests to run, condition the impact of changes, automate cloud costs, and much more. Lead your delivery pipelines with familiar developer knowledge-YAML, Git Commits. Remove all unnecessary toil and speed up developer productivity.
Harness's API provides access to a wide range of data related to software delivery and deployment. The following are the categories of data that can be accessed through Harness's API:
1. Applications: Information related to the applications being deployed, including their names, versions, and deployment status.
2. Environments: Details about the environments where the applications are being deployed, such as their names, types, and configurations.
3. Pipelines: Information about the pipelines used for software delivery, including their names, stages, and execution status.
4. Workflows: Details about the workflows used for software deployment, such as their names, steps, and execution status.
5. Artifacts: Information about the artifacts used in the software delivery process, including their names, versions, and locations.
6. Metrics: Data related to the performance of the software delivery process, such as deployment frequency, lead time, and mean time to recovery.
7. Logs: Details about the logs generated during the software delivery process, including their content, timestamps, and severity levels.
8. Notifications: Information about the notifications sent during the software delivery process, such as their types, recipients, and content.
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.
The harness is the industry’s first Software Delivery stage to use AI to facilitate your DevOps processes - CI, CD & GitOps, Feature Flags, Cloud Costs, and much more. Our AI takes your distribution pipelines to the next level. You can automate yellow verifications, prioritize what tests to run, condition the impact of changes, automate cloud costs, and much more. Lead your delivery pipelines with familiar developer knowledge-YAML, Git Commits. Remove all unnecessary toil and speed up developer productivity.
Starburst Data is a data access and analytics company that offers a cloud-native, SQL-based query engine called Presto. Their mission is to enable organizations to access and analyze data across various sources efficiently and at scale. Starburst Data provides an enterprise-grade platform that leverages the power of Presto to query data residing in different databases, data lakes, and cloud storage systems, eliminating data silos and accelerating insights. With a focus on performance, security, and ease of use, Starburst Data empowers businesses to unlock the value of their data, enabling faster decision-making and advanced analytics capabilities.
1. First, navigate to the Harness dashboard and select the "Connectors" tab from the left-hand menu.
2. Click on the "Add Connector" button and select "Airbyte" from the list of available connectors.
3. In the "Connection Settings" section, enter the URL for your Airbyte instance.
4. Next, enter the API key for your Airbyte instance in the "API Key" field.
5. In the "Source Settings" section, select the source connector you want to connect to Harness.
6. Enter the necessary credentials for the selected source connector, such as the username and password.
7. Click the "Test Connection" button to ensure that the connection is successful.
8. If the connection is successful, click the "Save" button to save the connector configuration.
9. You can now use the connector in your Harness workflows to extract data from the source connector and load it into your destination system.
1. First, navigate to the connectors page on Airbyte and select the Starburst Galaxy destination connector.
2. Next, enter the required credentials for your Starburst Galaxy account, including the host, port, database name, username, and password.
3. Once you have entered your credentials, click on the "Test Connection" button to ensure that the connection is successful.
4. If the connection is successful, you can then configure the settings for your destination connector, including the table name, schema, and any additional options.
5. After configuring your settings, you can then run a sync to transfer data from your source connector to your Starburst Galaxy destination.
6. You can monitor the progress of your sync and view any errors or warnings that may occur during the transfer process.
7. Once the sync is complete, you can then view your data in your Starburst Galaxy database and use it for analysis or other purposes.
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
Harness's API provides access to a wide range of data related to software delivery and deployment. The following are the categories of data that can be accessed through Harness's API:
1. Applications: Information related to the applications being deployed, including their names, versions, and deployment status.
2. Environments: Details about the environments where the applications are being deployed, such as their names, types, and configurations.
3. Pipelines: Information about the pipelines used for software delivery, including their names, stages, and execution status.
4. Workflows: Details about the workflows used for software deployment, such as their names, steps, and execution status.
5. Artifacts: Information about the artifacts used in the software delivery process, including their names, versions, and locations.
6. Metrics: Data related to the performance of the software delivery process, such as deployment frequency, lead time, and mean time to recovery.
7. Logs: Details about the logs generated during the software delivery process, including their content, timestamps, and severity levels.
8. Notifications: Information about the notifications sent during the software delivery process, such as their types, recipients, and content.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: