Access Your Data On Search Console

The first step in loading Search Console data to any kind of data warehouse solution is to access them and start extracting them.

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

1. Search Console API
2. URL Testing Tools API

Of the two, we are interested in the first API, allowing us to access any data we are interested in.

As with every other Google product, you need to authorize yourself to get access to the API through the implementation of 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 an API like the one the 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 a big amount of data. Platforms like Google tend to generate a lot of data. Pulling big volumes out of an API might be difficult, especially when considering and respecting any rate limits that the API has.

Transform And Prepare Your Google Search Console Data

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

1. The limitations of the database that data will be loaded onto
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 for example, you want to push data into Google BigQuery, then 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 the data before loading 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 the expressivity of your queries and limit your analysts on what they can do directly out of the database.

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 the 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 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.

Transform And Prepare Your Search Console Data For Amazon Redshift

Redshift is built around industry-standard SQL with added functionality to manage very large data sets and high-performance analysis. So, in order to load any data into it, you will have to follow its data model which is a typical relational database model. The data you extract from your data source should be mapped into tables and columns where you can consider the table as a map to the resource you want to store and columns the attributes of that resource.

Also, each attribute should adhere to the data types that are supported by Redshift.

As data is probably coming in a representation like JSON that supports a much smaller range of data types you have to be really careful about what data you feed into Redshift and make sure that you have mapped your types into one of the datatypes that are supported by Redshift.

Designing a Schema for Redshift and mapping the data from your data source to it is a process that you should take seriously as it can both affect the performance of your cluster and the questions that you can answer. It’s always a good idea to have in your mind the best practices that Amazon has published regarding the design of a Redshift database. When you have concluded on the design of your database, you need to load your data on one of the data sources that are supported as input by Redshift, these are the following:

• Amazon S3
• Amazon DynamoDB
• Amazon Kinesis Firehose

Load Your Search Console Data Into Amazon Redshift

To upload data to Amazon S3, you will have to use the AWS REST API. As we see again, APIs play an important role in both the extraction and the loading of data into our data warehouse. The first task that you have to perform is to create a bucket, you do that by executing an HTTP PUT on the Amazon AWS REST API endpoints for S3.

You can do this by using a tool like CURL or Postman. Or use the libraries provided by Amazon for your favorite language. You can find more information by reading the API reference for the Bucket operations on Amazon AWS documentation.

After you have created your bucket you can start sending data to Amazon S3, using again the same AWS REST API but by using the endpoints for Object operations. As in the Bucket case you can either access the HTTP endpoints directly or use the library of your preference.

Amazon Redshift supports two methods for loading data into it. The first one is by invoking an INSERT command. You can connect to your Redshift instance with your client, using either a JDBC or ODBC connection and then you perform an INSERT command for your data.

The way you invoke the INSERT command is the same as you would do with any other SQL database, for more information you can check the INSERT examples page on the Redshift documentation.

Redshift is not designed for INSERT-like operations, on the contrary, the most efficient way of loading data into it is by doing bulk uploads using a COPY command.

You can perform a COPY command for data that lives as flat files on S3 or from an Amazon DynamoDB table. When you perform COPY commands, Redshift is able to read multiple files simultaneously and it automatically distributes the workload to the cluster nodes and performs the load in parallel.

The best way to load data from Search Console to Redshift

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

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