How to load data from K6 Cloud to Databricks Lakehouse
Learn how to use Airbyte to synchronize your K6 Cloud data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Export Data from k6 Cloud
First, export your test result data from k6 Cloud. You can do this by using the k6 Cloud API to download the JSON or CSV data files. Use the `GET /v1/runs/{run_id}/metrics` endpoint to fetch the metrics data for a specific test run.
Step 2: Save Data Locally
Once the data is exported from k6 Cloud, save it to your local file system. Ensure to organize the files in a structured manner, such as having separate directories for different test runs or dates.
Step 3: Prepare Databricks Environment
Log into your Databricks account and navigate to your Lakehouse environment. Ensure you have the necessary permissions to create tables and upload data. Set up a suitable cluster that can process the data files you intend to import.
Step 4: Upload Data to Databricks File System (DBFS)
Use the Databricks CLI or the web interface to upload your JSON or CSV files to the Databricks File System (DBFS). You can do this by running the command `dbfs cp dbfs:/` for each file or directory you wish to upload.
Step 5: Create a Table Schema in Databricks
In Databricks, create a table schema that matches the structure of your k6 Cloud data. Use the Databricks SQL interface or a notebook with Spark SQL to define the schema. For example, if your data is in JSON format, you can use `CREATE TABLE` statements specifying the appropriate data types for each column.
Step 6: Load Data into Databricks Table
Use Spark SQL to load the uploaded data into your Databricks table. You can read the data files using Spark’s `DataFrame` API. For example, use `spark.read.json("dbfs:/")` for JSON files or `spark.read.csv("dbfs:/")` for CSV files, and then write the data into the table using `DataFrame.write.insertInto("")`.
Step 7: Verify Data Integrity
Once the data is loaded, run queries to verify that the data has been transferred correctly and is complete. Check for consistency with your original k6 Cloud data by comparing sample metrics or counts. Use SQL queries to inspect the data and ensure that all necessary transformations and migrations were successful.
By following these steps, you can effectively move data from k6 Cloud to a Databricks Lakehouse environment without the use of third-party connectors or integrations.