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Begin by understanding the data structure, format, and requirements in both Harness and Starburst Galaxy. Identify the types of data you need to move and any specific schema or formatting requirements for Starburst Galaxy. Ensure that you have the necessary permissions and access to export data from Harness and import data into Starburst Galaxy.
Utilize Harness's built-in data export functionalities. This may involve using their API or built-in tools to export the data in a format such as CSV, JSON, or any other supported format. Make sure to export the data in a manner that preserves its integrity and structure.
Once the data is exported, review and preprocess it. This may include cleaning up the data, converting it into a compatible format for Starburst Galaxy, and ensuring that any necessary transformations (such as data type conversions) are applied. This step is crucial to ensure seamless integration into Starburst Galaxy.
Choose a secure method to transfer data from your local environment (or wherever the data is stored post-export) to the Starburst Galaxy environment. This might involve using secure file transfer protocols such as SCP, SFTP, or HTTPS to ensure data integrity and confidentiality during the transfer process.
Use Starburst Galaxy's import capabilities to load the data you transferred. This may involve accessing Starburst Galaxy's SQL interface or command-line tools to create tables and ingest the data. Make sure to adhere to any import guidelines provided by Starburst Galaxy to handle large datasets efficiently.
After importing the data, conduct thorough checks to verify that all data has been transferred accurately and completely. Compare the data in Starburst Galaxy with the original dataset in Harness to ensure consistency. Run queries to check for any discrepancies or missing records.
Finally, optimize the performance of your database in Starburst Galaxy by creating indexes or optimizing queries as needed. Document the entire data migration process, including any challenges faced and solutions implemented, for future reference and potential audits. This will be useful for repeat processes or troubleshooting issues later on.
By following these steps, you can efficiently and securely move data from Harness to Starburst Galaxy 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:
