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Start by exporting the data you need from Harness. Depending on the specific data and the interface provided by Harness, you might need to use their built-in export functionality. Typically, this involves selecting the desired datasets or reports and exporting them in a CSV or JSON format. Ensure that the data is saved locally on your system for further processing.
Before importing data into BigQuery, ensure that it is clean and in a format compatible with BigQuery. Open the exported file(s) and confirm that the data types (e.g., strings, numbers, dates) are consistent and match the schema you plan to use in BigQuery. Clean any unnecessary characters or fields that aren't needed for analysis.
Log in to your Google Cloud Platform account and navigate to the BigQuery console. Create a new dataset within your project to store the incoming data. Click on "Create dataset," specify a unique dataset ID, and configure any other necessary settings, like data location and expiration.
Once your dataset is ready, define the schema for the new table that will store your Harness data. You can do this by creating a new table and specifying the column names and data types that match your prepared data. The schema should reflect the structure of your CSV or JSON file, ensuring each column in your file has a corresponding field in BigQuery.
Before importing data into BigQuery, upload your CSV or JSON file to Google Cloud Storage (GCS). Navigate to the GCS console, create a bucket if necessary, and use the "Upload files" option to transfer your data file from your local machine to your bucket.
Use the BigQuery console to load the data from GCS into your prepared table. Navigate to the BigQuery console, select your dataset, and choose the "Create table" option. Under "Source," select "Google Cloud Storage" and specify the path to your uploaded file. Ensure the file format is correct (CSV or JSON), and map the fields to your table's schema. Execute the load job to transfer data into BigQuery.
After the import process is complete, verify that the data is correctly loaded into BigQuery. Run a few queries to ensure that the data matches the original dataset from Harness and that no errors occurred during the import process. Check data types, field values, and record counts to confirm accuracy and completeness.
By following these steps, you can successfully move data from Harness to BigQuery 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?
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