The best way to perform an in-depth analysis of Google Analytics data with Python is to load Google Analytics data to a database or cloud data warehouse, and then connect Python to this database and analyze data. Skyvia can easily load Google Analytics reports' data to a database or a cloud data warehouse of your choice.
ELT process supposes simple copying cloud data to a data warehouse or a database as-is, leaving all the transformation tasks for the database server. This is often uses, for example, when loading data to cloud data warehouses with affordable and nearly unlimited computing power for transformations. In Skyvia, this task is solved with easy-to-configure Replication packages.
ETL process supposes that data structure in source and target is different, and data must be transformed before loading it into target database. For example, you may want to create a schema for OLAP or simply have target tables for data already created. In Skyvia, this is task solved with Import packages, having powerful mapping and transformation capabilities.
All you need to do is to specify parameters for connecting to Google Analytics and data warehouse and select metrics and dimensions.
Skyvia’s Replication Tool will painlessly ensure you always have the most current data from Google Analytics in your data warehouse.
You don't need to prepare the database — Skyvia creates a table, corresponding to your report, in the data warehouse automatically.
Skyvia offers powerful mapping features for data transformations. You can use complex expressions and formulas, lookups, etc.
You can import only new and updated records, and thus, keep your database for analysis always up-to-date.
Integrate Google Analytics and Python with minimal effort and in only a few clicks!