BigQuery API'nin etkin olduğu bir Google Cloud projesine erişiminizin olması gerekir.
BigQuery Hızlı Başlangıç Kılavuzu'ndaki Başlamadan önce bölümünde
yeni bir Google Cloud projesi oluşturun veya
kullanabilirsiniz.
Bu veri kümesi, gerçek dünyadaki bir veri kümesinin emülasyonunu taklit eden karartılmış veriler içeriyor
gibi görünen URL'ler gerçek bir Google Analytics uygulamasından gelmiş gibi görünebilir. Belirli alanlar
<Other>, NULL ve '' dahil olmak üzere yer tutucu değerleri içerir. Kaynak:
veri kümesinin iç tutarlılığı biraz sınırlı olabilir.
Veri kümesi, şuna ait Google Analytics demo hesabı ile karşılaştırılamaz:
Google Merchandise Store'a gönderebilirsiniz.
Veri kümesini kullanma
Cloud Console, tabloları sorgulamak için kullanılabilecek bir arayüz sunar. URL parametrelerinin Google tarafından nasıl ele alınmasını istediğinizi belirtmek için
ga4_obfuscated_sample_ecommerce veri kümesine erişmek için BigQuery kullanıcı arayüzü.
Aşağıdaki sorguyu kopyalayıp Editor alanına yapıştırın. Bu sorgu,
veri kümesindeki benzersiz etkinlik, kullanıcı ve gün sayısını gösterir.
SELECT
COUNT(*) AS event_count,
COUNT(DISTINCT user_pseudo_id) AS user_count,
COUNT(DISTINCT event_date) AS day_count
FROM `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
Geçerli sorgular için veri miktarıyla birlikte bir onay işareti gösterilir.
belirtmelisiniz. Bu metrik, projenizin maliyetini belirlemenize
çalıştırmayı deneyin.
Çalıştır'ı tıklayın. Sorgu sonuçları sayfası, sorgu penceresinin altında görünür.
[null,null,["Son güncelleme tarihi: 2024-09-12 UTC."],[[["\u003cp\u003eThe \u003ccode\u003ega4_obfuscated_sample_ecommerce\u003c/code\u003e dataset provides obfuscated Google Analytics event export data for the Google Merchandise Store from November 1, 2020 to January 31, 2021.\u003c/p\u003e\n"],["\u003cp\u003eThis public dataset can be accessed and queried using BigQuery, allowing users to explore and analyze ecommerce website behavior.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset uses placeholder values for certain fields due to obfuscation, and its internal consistency may be limited.\u003c/p\u003e\n"],["\u003cp\u003eUsers can explore the dataset through the BigQuery UI, sample queries, and advanced analytical tools like Connected Sheets and Looker Studio.\u003c/p\u003e\n"],["\u003cp\u003eBefore using the dataset, ensure you have a Google Cloud project with BigQuery API enabled and review the limitations of the dataset.\u003c/p\u003e\n"]]],["The core content describes the `ga4_obfuscated_sample_ecommerce` dataset, a sample of Google Merchandise Store's obfuscated ecommerce data from November 2020 to January 2021. Access requires a Google Cloud project with BigQuery API enabled. Users can query the dataset using the BigQuery UI by composing and running queries in the editor. A sample query to count unique events, users, and days is provided. Users can then explore further by using advanced queries, schema, and other tools.\n"],null,["# BigQuery sample dataset for Google Analytics ecommerce web implementation\n\n[Google Merchandise Store](https://shop.googlemerchandisestore.com) is an online store that sells Google-branded\nmerchandise. The site uses Google Analytics's standard web [ecommerce\nimplementation](/tag-manager/ecommerce-ga4) along with [enhanced measurement](https://support.google.com/analytics/answer/9216061). The\n[`ga4_obfuscated_sample_ecommerce` dataset](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ga4_obfuscated_sample_ecommerce&t=events_20210131&page=table) available through the BigQuery\nPublic Datasets program contains a sample of obfuscated BigQuery event export\ndata for three months from 2020-11-01 to 2021-01-31.\n\nPre-requisite\n-------------\n\n- You need access to a Google Cloud project with BigQuery API enabled.\n Complete the *Before you begin* section in the [BigQuery Quickstart guide](https://cloud.google.com/bigquery/docs/quickstarts/quickstart-web-ui#before-you-begin) to\n create a new Google Cloud project or to enable the BigQuery API in an\n existing one.\n\n- You can use the [BigQuery Sandbox mode](https://cloud.google.com/bigquery/docs/sandbox) for free with certain limitations.\n The [Free usage tier](https://cloud.google.com/bigquery/pricing#free-tier) should be sufficient to explore this dataset and run the\n sample queries. You can optionally [Enable Billing](https://cloud.google.com/billing/docs/how-to/modify-project) to go beyond the Free\n usage tier.\n\nLimitations\n-----------\n\nThis dataset contains obfuscated data that emulates what a real world dataset\nwould look like from an actual Google Analytics implementation. Certain fields\nwill contain placeholder values including `\u003cOther\u003e`, `NULL`, and `''`. Due to\nobfuscation, internal consistency of the dataset might be somewhat limited.\n\nThe dataset can not be compared to the [Google Analytics Demo Account](https://support.google.com/analytics/answer/6367342) for\nGoogle Merchandise store as the data is different.\n\nUsing the dataset\n-----------------\n\n1. The Cloud Console provides an interface to query tables. You can use the\n [BigQuery UI](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ga4_obfuscated_sample_ecommerce&t=events_20210131&page=table) to access the `ga4_obfuscated_sample_ecommerce` dataset.\n\n2. If the **Editor** tab isn't visible, then click add_box **Compose new query**.\n\n3. Copy and paste the following query into the Editor field. This query will\n show to number of unique events, users, and days in the dataset.\n\n SELECT\n COUNT(*) AS event_count,\n COUNT(DISTINCT user_pseudo_id) AS user_count,\n COUNT(DISTINCT event_date) AS day_count\n FROM `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`\n\n4. For valid queries, a check mark will appear along with the amount of data\n that the query will process. This metric helps you determine the cost of\n running the query. \n\n \u003cbr /\u003e\n\n5. Click **Run** . The query results page will appear below the query window. \n\n \u003cbr /\u003e\n\n6. Try running some [sample queries](/analytics/bigquery/basic-queries).\n\nNext Steps\n----------\n\n- Learn more about the schema for [Google Analytics BigQuery event export\n schema](/analytics/bigquery/event-schema).\n\n- Run some of the [advanced queries](/analytics/bigquery/advanced-queries) on the dataset.\n\n- If you are not familiar with BigQuery, explore [BigQuery How-to Guides](https://cloud.google.com/bigquery/docs/how-to).\n\n- Use [Connected Sheets](https://cloud.google.com/bigquery/docs/connected-sheets) to analyze the dataset from Google Sheets\n spreadsheet.\n\n- [Visualize](https://cloud.google.com/bigquery/docs/visualize-looker-studio) the dataset using [Looker Studio](https://lookerstudio.google.com/)."]]
