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Begin by logging into your Harness account. Navigate to the specific section or report from which you want to export data. Use the built-in data export functionality within Harness to download the required data, typically available in CSV format. Save the file to a location on your computer where you can easily access it later.
Open Google Sheets in your browser and create a new spreadsheet. Label the spreadsheet and its columns appropriately to match the structure of the data you will be importing from Harness. This ensures that your data is organized and ready for analysis once imported.
In your new Google Sheet, click on �File�� in the top menu, then select �Import.�� This will open a dialog box where you can choose the method and file for importing data into the sheet.
In the import dialog, select the �Upload�� tab. Click �Select a file from your device�� and browse to the location where you saved your exported CSV file from Harness. Select the file and click �Open�� to upload it to Google Sheets.
After selecting the file, Google Sheets will prompt you with import options. Choose �Replace spreadsheet�� if you want to replace the current contents or �Append to current sheet�� to add data below existing entries. Ensure the delimiter is set to �Comma�� since the file is in CSV format, and check the box for �Convert text to numbers and dates�� if applicable.
Review the data preview provided by Google Sheets to ensure that the columns and rows are aligned correctly. Make any necessary adjustments to your import settings. Once satisfied, click �Import Data�� to complete the process and populate your Google Sheet with the data from Harness.
After importing, verify that all data has been transferred accurately. Check for any discrepancies or formatting issues. Use Google Sheets' formatting tools to adjust column widths, apply number formats, and create headers if needed. This will ensure your data is easy to read and ready for further processing or analysis.
By following these steps, you can successfully move data from Harness to Google Sheets without relying on third-party connectors or integrations.
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.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
