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Begin by creating a new project in Google Cloud Platform (GCP). Navigate to the GCP Console, select the project dropdown, and click "New Project." Give your project a name and note the Project ID, as you will need it for later steps.
Within your newly created Google Cloud project, enable the BigQuery API. Go to the "APIs & Services" section, click on "Library," search for "BigQuery API," and click "Enable." This will allow your project to interact with BigQuery services.
Access the BigQuery console from the GCP Console. In the Explorer panel, click on your project and select “Create Dataset.” Provide a Dataset ID and configure any necessary dataset settings. This will serve as the storage location for your data from Google Search Console.
Log into Google Search Console and navigate to the property you want to export data from. Go to the "Performance" report, where you will see options for filtering and customizing the data view. Once you have the desired data displayed, click on the download button to save the data as a CSV file.
Open the downloaded CSV file and review its structure. Ensure that the data types (e.g., STRING, INTEGER) and column names align with what you plan to use in BigQuery. Modify the CSV if necessary to ensure compatibility, such as ensuring date formats are consistent.
In the GCP Console, navigate to "Cloud Storage" and create a new bucket. Once the bucket is set up, click "Upload Files" and select your prepared CSV file. This step is crucial as BigQuery needs to read data from a Google Cloud Storage bucket.
Return to the BigQuery console, click on your dataset, and select “Create Table.” Choose "Google Cloud Storage" as the source and provide the path to your CSV file in the bucket. Configure the schema manually or auto-detect from the CSV, and finalize by clicking "Create Table." This action will import your data into BigQuery, completing the transfer from Google Search Console.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Google Search Console is a Google service that helps site owners get the most out of their website. It offers ways for site owners to monitor, troubleshoot, and improve a site’s position on Google Search. It also provides reports and tools for measuring a site’s Search performance and traffic; learning what search queries lead to a site; optimizing website content; monitoring, testing, and tracking AMP pages; and much more, including the ability to test a site’s mobile usability.
Google Search Console's API provides access to a wide range of data related to a website's performance in Google search results. The following are the categories of data that can be accessed through the API:
1. Search Analytics: This category includes data related to search queries, impressions, clicks, and click-through rates.
2. Sitemaps: This category includes data related to the sitemap of a website, such as the number of URLs submitted, indexed, and any errors encountered.
3. Crawl Errors: This category includes data related to any crawl errors encountered by Google while crawling a website, such as 404 errors, server errors, and soft 404 errors.
4. Security Issues: This category includes data related to any security issues detected by Google, such as malware or phishing.
5. Indexing: This category includes data related to the indexing status of a website, such as the number of pages indexed and any indexing errors encountered.
6. Structured Data: This category includes data related to the structured data markup on a website, such as the number of pages with structured data and any errors encountered.
7. Mobile Usability: This category includes data related to the mobile usability of a website, such as the number of pages with mobile usability issues and any errors encountered.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
