How to load data from Google Search Console to Snowflake

Access Your Data on Google Search Console

The first step in loading your search Console data to any kind of data warehouse solution, is to access your data and start extracting it.

You access your data for the Google Search Console through the Search Console APIs. There are two APIs available there:

1. Search Console API

2. URL Testing Tools API

We are interested in the first API, which allows us to access any data we are interested in.

As with every other Google product, you need to authorize yourself to access the API by implementing the OAuth 2.0 protocol. The API is web-based following a REST-like architecture, but Google also offers some SDKs that you can use for some popular languages like Java and Python.

The things that you have to keep in mind when dealing with any API like the one the Google Search Console has, are:

1. Rate limits: Every API, has some rate limits that you have to respect.

2. Authentication: You authenticate on Google using an OAuth.

3. Paging and dealing with big amount of data: Platforms like Google tend to generate a lot of data. Pulling big volumes of data out of an API might be difficult, especially when you consider and respect any rate limits that the API has.

Transform And Prepare your Google Search Console Data for Snowflake

After you have accessed your data on Google Search Console, you will have to transform it based on two main factors:

1. The limitations of the database that is going to be used

2. The type of analysis that you plan to perform

Each system has specific limitations on the data types and data structures that it supports. If you want to push data into Google BigQuery, you can send nested data like JSON directly. But when you are dealing with tabular data stores, like PostgreSQL, this is not an option. Instead, you will have to flatten out your data before loading it into the database.

Also, you have to choose the right data types. Again, depending on the system you will send data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit your queries’ and limit your analysts on what they can do directly out of the database.

Google Search Console data is modeled around the concept of a report, just like Google Analytics but with a much more limited number of dimensions and metrics.

At the end you will need to map one report to a table on your database and make sure that all data is stored into it. Dimensions and metrics will become columns of the tables.

You need to take special care that the reports you will be getting from Google Search Console do not have primary keys given by Google to avoid duplicates.

For more information on how you can query your Search Analytics data, please see here.

Data in Snowflake is organized around tables with a well-defined set of columns with each one having a specific data type.

Snowflake supports a rich set of data types. It is worth mentioning that a number of semi-structured data types are also supported. With Snowflake, it is possible to directly load data in JSON, Avro, ORC, Parquet, or XML format. Hierarchical data is treated as a first-class citizen, similar to what Google BigQuery offers.

There is also one notable common data type that Snowflake does not support. LOB or large object data type is not supported. Instead, you should use a BINARY or VARCHAR type. But these types are not that useful for data warehouse use cases.

Of course, you will need to ensure that as data types from the Google Search Console API might change, you will adapt your database tables accordingly, there’s no such thing as automatic data type casting.

After you have a complete and well-defined data model or schema for Snowflake, you can move forward and start loading your data into the database.

Load Data From Google Search Console to Snowflake

Usually, data is loaded into Snowflake in a bulk way, using the COPY INTO command. Files containing data, usually in JSON format, are stored in a local file system or Amazon S3 buckets. A COPY INTO command is invoked on the Snowflake instance, and data is copied into a data warehouse.

The files can be pushed into Snowflake using the PUT command into a staging environment before the COPY command is invoked.

Another alternative is to upload every data directly into a service like Amazon S3 from where Snowflake can access data directly.

Updating Your Google Search Console Data on Snowflake

As you will be generating more data on Google Search Console, you will need to update your older data on Snowflake. This includes new records together with updates to older records that for any reason have been updated on Google Search Console.

You will need to periodically check Google Search Console for new data and repeat the process that has been described previously while updating your currently available data if needed. Updating an already existing row on a Snowflake table is achieved by creating UPDATE statements.

Another issue that you need to take care of is identifying and removing any duplicate records on your database. Either because Google Search Console does not have a mechanism to identify new and updated records or because of errors on your data pipelines, duplicate records might be introduced to your database.

In general, ensuring the quality of data that is inserted into your database is a big and difficult issue.

The best way to load data from Google Search Console to Snowflake

So far, we just scraped the surface of what you can do with Snowflake and how to load data into it. Things can get even more complicated if you want to integrate data coming from different sources.

Instead of writing, hosting, and maintaining a flexible data infrastructure, RudderStack can handle everything automatically for you.

RudderStack, with one click, integrates with sources or services, creates analytics-ready data, and syncs your Search Console to Snowflake right away.