How To Analyze CSV Data

If you have data in CSV format and want to analyze it, this short article provides a step-by-step guide on how to achieve such analytics.

Prerequisites

• GoodData account (if you do not have one, you can register your free trial)
• Installed dbt Core (you can find installation guide in dbt documentation)

Step 1: Add CSV to dbt seeds folder

If you use the open-source repository gooddata-csv-analytics, go to the import_csv folder, where you can place your CSV files.

Step 2: Edit profiles.yml

The profiles.yml must contain connection details for your data warehouse (i.e. PostgreSQL). You can find profiles.yml on the following path:

$ ~/.dbt/profiles.yml

You can open it with Visual Studio Code:

$ code ~/.dbt/profiles.yml

Now, you can edit your connection details and save them:

import_csv:
  outputs:

    prod:
      type: <postgres | snowflake | other>
      threads: 1
      host: <host>
      port: <host>
      user: <user>
      pass: <password>
      dbname: <database_name>
      schema: <database_schema>

  target: prod

Step 3: Run dbt seed

If you successfully configured the previous steps, you can run the following command in the import_csv folder:

$ dbt seed

If everything went smoothly, you should see the following result:

Step 4: Connect data source to GoodData

In your GoodData account, go to the Data tab and add a data source. You should use the same connection details that you used in Step 2.

Step 5: Analyze CSV data

Open the Analyze tab, where you can create metrics and visualizations based on your CSV data.

Conclusion

If you have any questions or require assistance, don't hesitate to reach us on the GoodData Slack channel. Willing to try GoodData? Explore the possibilities by signing up for our free trial today.