{"id":25259,"date":"2023-02-21T00:00:00","date_gmt":"2023-02-21T00:00:00","guid":{"rendered":"https:\/\/alienroad.com\/google-bilgi-bankasi\/bulk-data-export-a-new-and-powerful-way-to-access-your-search-console-data\/"},"modified":"2023-02-21T00:00:00","modified_gmt":"2023-02-21T00:00:00","slug":"bulk-data-export-a-new-and-powerful-way-to-access-your-search-console-data","status":"publish","type":"ar_kb","link":"https:\/\/alienroad.com\/google-bilgi-bankasi\/bulk-data-export-a-new-and-powerful-way-to-access-your-search-console-data\/","title":{"rendered":"Bulk data export: a new and powerful way to access your Search Console data"},"content":{"rendered":"

Tuesday, February 21, 2023<\/p>\n

\nToday, we’re announcing bulk
\n data export<\/a>, a new feature that allows you to export data from Search Console to
\n Google BigQuery<\/a> on an ongoing basis.
\n (Note that the rollout will take approximately one week, so you may not have access right away.<\/em>)\n<\/p>\n

\n You can configure an export in Search Console to get a daily data dump into your BigQuery project.
\n The data includes all your performance data, apart from anonymized queries, which are filtered out
\n for privacy reasons; in other words, the bulk data export is not affected by the
\n daily data row limit<\/a>.
\n This means you can explore your data to its maximum potential, joining it with other sources of data
\n and using advanced analysis and visualization techniques.\n<\/p>\n

\nThis data export could be particularly helpful for large websites with tens of thousands of pages, or those receiving
\n traffic from tens of thousands of queries a day (or both!). Small and medium sites already have access to all their
\n data through the user interface, the Looker Studio connector<\/a>
\n (formerly known as Data Studio) or the Search Analytics API<\/a>.\n<\/p>\n

Setting up a new bulk data export<\/h2>\n

\nTo configure a new report, you’ll need to prepare your BigQuery account to receive the data and set up
\n your details in the Search Console settings. Check the Help Center for a
\n step-by-step guide<\/a>,
\n but in general, the process is divided into two stages:\n<\/p>\n

    \n
  1. Prepare your Cloud project<\/strong> (inside Google Cloud Console<\/a>):
    \n this includes enabling the BigQuery API for your project and giving permission to your Search Console service account.<\/li>\n
  2. Set export destination<\/strong> (inside Search Console<\/a>):
    \n this includes providing your Google Cloud project ID, and choosing a dataset location. Note that only property owners can set up a bulk data export.<\/li>\n<\/ol>\n

    \n\"Search\n<\/p>\n

    \nOnce you submit the information to Search Console, it’ll simulate an export. If the export succeeds, we’ll inform all property owners via email and your
    \n ongoing exports will start within 48 hours. If the export simulation fails, you’ll receive an immediate alert on the issue detected; here’s a list of
    \n     possible export errors<\/a>.\n<\/p>\n

    Data available on bulk data exports<\/h2>\n

    \nOnce the bulk data export is set up successfully, you can log in to your BigQuery account and start querying the data.\n<\/p>\n

    \nYou can find detailed table guidelines and references<\/a> in the help center;
    \n also check the explanation on the difference between     aggregating
    \n data by property vs by page<\/a>, as it’ll help you understand the data better. Here is a quick description of the three tables that will be available to you:\n<\/p>\n