Low Performance on Google Analytics source

Hi bartek_pepper 

The reason for the performance issues when pulling data from Google Analytics is that Power BI performs a live read every time you make a change in the Power Query Editor. This means each update or modification triggers a full data retrieval, which can be quite heavy, especially when dealing with over 200 MB of data.

Here are some alternatives to improve performance or consider different ways to store the data:

1. **Reduce the Amount of Data Retrieved**: It’s best to filter and define which fields you actually need and what the relevant date range is. This way, you can reduce the amount of data being downloaded each time, significantly cutting down refresh time.

2. **Incremental Refresh**: Set up incremental refresh to fetch only new or updated data instead of reloading everything from scratch on each refresh. After the initial load, Power BI will only pull in new data, which can greatly reduce refresh times.

3. **External ETL Process**: Consider using an ETL process with external tools (like Azure Data Factory, Google Cloud Dataflow, or other ETL services) to move the data from Google Analytics to a more efficient storage option:
- **Database (Azure SQL / SQL Server)**: Store the data in a database for faster querying and better control over refresh schedules.
- **Cloud Storage (Azure Blob / Google Cloud Storage)**: Save the data as files (e.g., CSV, Parquet) and connect Power BI to those files. This can improve performance when working with a data lake approach.
- **Big Data Processing Services (Azure Databricks / Google BigQuery)**: Manage and process large datasets more efficiently before feeding them into Power BI.

4. **Dataflows in Power BI**: Create a Dataflow that handles the extraction and transformation of the data, so it’s cached and the pull from Google Analytics is lighter, improving performance when making changes.

5. **Scheduled Refresh**: Avoid live connections to Google Analytics and schedule refreshes at more convenient times (e.g., off-peak hours) so data loads are less frequent, and performance during development is smoother.

These are some ways to help improve performance and better manage large datasets in Power BI.