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Begin by exporting the data you need from k6 Cloud. You can achieve this by using the k6 Cloud API to fetch test results. Use the API endpoint to list and retrieve test results in JSON or CSV format. This will be your source data that you will transfer to Starburst Galaxy.
Once you have the data exported, prepare it for transfer. Ensure that the data is cleaned and formatted correctly. If necessary, convert the JSON or CSV data into a format that Starburst Galaxy can ingest, such as Parquet or ORC files, to optimize performance.
Since direct transfers between k6 Cloud and Starburst Galaxy are not possible without third-party tools, store the prepared data in a secure, accessible location. This could be an S3 bucket or a similar cloud storage service supported by Starburst Galaxy. Ensure that the storage location is configured with the appropriate access controls.
Configure the necessary credentials for both the storage service and Starburst Galaxy. Ensure that Starburst Galaxy has the necessary permissions to access the storage location. Use AWS IAM roles, Google Cloud IAM, or similar services to manage these permissions securely.
Use Starburst Galaxy's built-in capabilities to connect to the cloud storage and ingest the data. Utilize Starburst's SQL-based queries to create external tables pointing to the data files in the cloud storage. This step involves writing SQL queries within Starburst Galaxy to map the data structure from the files into tables.
After loading data into Starburst Galaxy, verify that the data has been transferred correctly. Run SQL queries to check for consistency and accuracy in the dataset. Compare key metrics and values against the original data exported from k6 Cloud to ensure no data loss or corruption occurred during the transfer.
Once the data is verified, optimize it for performance within Starburst Galaxy. Create indexes, partitions, or materialized views as necessary to enhance query performance. You can now use the data for further analytics or integration with other datasets within Starburst Galaxy to gain insights or drive decisions.
By following these steps, you can effectively move data from k6 Cloud 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.
k6 Cloud is a commercial SaaS product that we designed to be the perfect companion to k6 OSS. It brings ease of use, team management, and continuous testing capabilities to your performance testing projects. k6 Cloud Docs assist you to learn and use k6 Cloud features and functionality. The k6 Cloud is a fully-managed load testing service that complements k6 to accelerate your performance testing. k6 is an open-source load testing tool and cloud service for developers, DevOps, QA, and SRE teams.
K6 Cloud's API provides access to various types of data related to performance testing and monitoring. The following are the categories of data that can be accessed through the API:
1. Test Results: This category includes data related to the results of performance tests, such as response times, error rates, and throughput.
2. Metrics: This category includes data related to various performance metrics, such as CPU usage, memory usage, and network traffic.
3. User Behavior: This category includes data related to user behavior during performance tests, such as the number of users, their actions, and their locations.
4. Environment: This category includes data related to the environment in which the performance tests are conducted, such as the hardware and software configurations.
5. Alerts: This category includes data related to alerts generated during performance tests, such as threshold breaches and error notifications.
6. Reports: This category includes data related to performance test reports, such as summary reports, detailed reports, and trend analysis reports.
Overall, K6 Cloud's API provides a comprehensive set of data that can be used to analyze and optimize the performance of web applications and services.
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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