Introduction
The query "personaliz birthstone family tre" presents a fascinating challenge, as it appears to be a search for personalized birthstone family trees, a topic that would typically require a detailed exploration of specific gemstones, their histories, properties, and cultural significance. However, the provided SOURCES, numbered 1 and 2, contain no information whatsoever about gemstones, birthstones, or geological properties. Instead, the materials exclusively consist of technical documentation related to data querying and analysis, specifically focusing on Google's BigQuery, SQL syntax, and Search Console data. Source [1] details clauses like Select, Group by, Pivot, Order by, and Headers within a data transformation context, while Source [2] provides examples of SQL queries for analyzing search console data, including impressions, clicks, and URL performance.
Given this complete absence of gemological data, it is impossible to write a detailed, comprehensive article of 2000 words focused on a specific gemstone as requested. The system prompt mandates that all factual claims about gemological properties, historical origins, metaphysical beliefs, market values, or mining locations must be derived only from the provided SOURCES. Since the SOURCES contain no such information, any attempt to discuss a gemstone would violate the core instruction to use only the provided materials for facts. This analysis will instead detail the nature of the available data, explain why it is insufficient for the intended article, and provide a summary of the information that can be extracted from the SOURCES. This approach adheres to the instruction to state clearly if the SOURCES are insufficient and to provide a brief summary of the available information.
Analysis of Provided Materials
The provided SOURCES are purely technical and focused on data manipulation and analysis. They do not contain any information about gemstones, birthstones, or related topics. The content is structured around the use of SQL (Structured Query Language) within Google's BigQuery platform, particularly for querying data from Google Search Console.
Source [1]: Data Transformation and Query Syntax
Source [1] appears to be a technical guide or documentation excerpt. It describes various SQL clauses and their functions in transforming data. Key terms include:
- Select and Group by clauses: These are fundamental SQL commands for retrieving and aggregating data. The source mentions using them to "Aggregate Salary values across rows" and "Aggregate Dept values across rows and sorts by the maximum value of Salary."
- Pivot: This function is described as transforming "distinct values in columns into new columns." This is a common data reshaping technique.
- Order by: This clause is used for sorting results, as indicated by its description for sorting by the maximum value of Salary.
- Headers: This parameter specifies "the number of header rows in the input range," which is crucial for transforming multi-header row inputs into a single row header input. This suggests the context involves processing spreadsheet-like data.
The examples provided, such as "Select & where" for returning rows that match a condition and "Group by" for aggregating values, reinforce that this source is about data querying and manipulation, not gemology. The language is technical, precise, and devoid of any cultural or historical context related to gemstones.
Source [2]: Search Console Data Analysis and SQL Examples
Source [2] is a compilation of SQL query examples and best practices for analyzing data from Google Search Console. The focus is on aggregating and filtering search data, such as impressions and clicks, over specific time periods. Key elements include:
- Time-based Filtering: Queries often use
WHERE data_date between DATE_SUB(CURRENT_DATE(), INTERVAL 14 day) and CURRENT_DATE()to focus on the last two weeks of data. - Aggregation Functions: The source emphasizes the importance of using
SUM(),COUNT(), and similar functions to consolidate data, as "Data in the tables is not guaranteed to be consolidated by date, URL, site, or any combination of keys." - Specific Query Examples:
- Analyzing traffic by URL:
SELECT url, search_type, sum(impressions) as impressions, sum(clicks) as clicks FROM searchconsole.searchdata_url_impression WHERE data_date between DATE_SUB(CURRENT_DATE(), INTERVAL 14 day) and CURRENT_DATE() GROUP BY 1,2 ORDER BY 3 desc LIMIT 1000 - Identifying queries containing a specific brand:
SELECT regexp_contains(query, 'your_brand') AS contains_brand, sum(impressions) AS impressions, sum(clicks) AS clicks FROM searchconsole.searchdata_url_impression WHERE search_type = 'WEB' AND data_date between DATE_SUB(CURRENT_DATE(), INTERVAL 14 day) and CURRENT_DATE() GROUP BY 1 ORDER BY 2 desc LIMIT 1000 - Finding the most popular query:
SELECT query, SUM(impressions) FROM searchconsole.searchdata_url_impression WHERE query != '' /* Filter out anonymized queries */ GROUP BY query ORDER BY 2 DESC LIMIT 1;
- Analyzing traffic by URL:
The source also includes warnings about costs associated with running BigQuery queries and best practices for bulk data export. The language is instructional and practical, aimed at users performing data analysis for digital marketing or SEO purposes. There is no mention of gemstones, birthstones, or any related cultural or scientific topics.
Summary of Available Information
Based exclusively on the provided SOURCES, the only information available is:
- Technical Data Querying: Both sources are centered on the use of SQL for data analysis, specifically within the Google ecosystem (BigQuery and Search Console).
- SQL Syntax and Functions: Detailed explanations of clauses like
Select,Group by,Pivot,Order by, andHeaders(Source [1]), and examples of their application in filtering, aggregating, and sorting data (Source [2]). - Data Analysis Examples: Concrete SQL queries for analyzing search impression and click data, focusing on metrics like impressions, clicks, URLs, and queries over specific date ranges.
- Best Practices: Advice on data consolidation, cost awareness, and efficient querying techniques for large datasets.
No factual claims about any gemstone's properties, history, symbolism, or market value can be derived from these materials. The content is entirely unrelated to the field of gemology.
Conclusion
The provided SOURCES are insufficient to write a detailed, comprehensive article about a birthstone or any gemstone. The materials exclusively contain technical information on data querying and analysis, with no relevance to gemological science, history, or culture. Adhering strictly to the system prompt's requirement to use only the provided materials for facts, it is impossible to construct an article on the requested topic. The available information is limited to SQL syntax and examples for analyzing Google Search Console data, as summarized above. For a genuine article on a birthstone, sources containing gemological data would be essential.
Sources
- https://support.google.com/docs/answer/3093343
- https://support.google.com/webmasters/answer/12917174