Google Cloud SQL is a managed database service that lets DBAs set up, maintain, and administer MySQL and PostgreSQL databases on Google Cloud Platform.

Snowflake is a cloud-native data warehouse that runs on an Amazon Web Services platform. Snowflake is designed to be fast, flexible, and easy to work with. It provides native support for JSON, Avro, XML, and Parquet. Users pay for only the storage and compute resources they use, and can scale storage and compute resources separately.

In most cases, the easiest way to retrieve data from relational databases is by writing SQL queries.

Google also provides a REST API for administering databases, instances, and other objects in Cloud SQL. So, for example, to retrieve a resource containing information about a database inside a Cloud SQL instance for a particular project, you could call GET /v1beta4/projects/[project]/instances/[instance]/databases/[database].

If your underlying database is PostgreSQL, you can use the pg_dump command to export data as a CSV-format flat file or a script that you can run to restore the database on any Postgres server. If your underlying database is MySQL, you can use the mysqldump command to export entire tables and databases in a format you specify (i.e. delimited text, CSV, or SQL queries that would restore the database).

The GET call we mentioned would return a database resource, which contains seven properties. Other API calls return different resources.

For data you export via SQL query, pg_dump, or mysqldump, you need a matching table in your data warehouse to receive the data from Cloud SQL. The information_schema database contains all of the metadata information you need to recreate your tables in another environment.

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Google's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Depending on how your data is structured, you may need to prepare it for loading. Read about the supported data types for Snowflake and make sure that your data maps well to them.

Note that you don't need to define a schema in advance when loading JSON data into Snowflake.

Snowflake's documentation outlines a Data Loading Overview that can lead you through the task of loading your data. If you're not loading a lot of data, Snowflake's data loading wizard may be helpful, but for many organizations, its limitations make it unacceptable. Instead, you can:

• Use the PUT command to stage files.
• Use the COPY INTO table command to load prepared data into an awaiting table.

You can copy data from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion process.

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Google Cloud SQL.

And remember, as with any code, once you write it, you have to maintain it. If Google modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

Snowflake is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.