![]() ![]() Standard SQL in BigQuery – UNION DISTINCTĪs expected, we do have only two distinct values in the column Lead_type, so that’s what we see in the results. We’re using the UNION DISTINCT query for the “ Lead_type” field values. To illustrate the use of Standard SQL, let’s demonstrate another example using the tables we’ve built. What makes it special is the use of two additional keywords, DISTINCT and ALL. When we talk about Standard SQL, we’re talking about the BigQuery Standard SQL we used at the beginning of the article as an example. ![]() Also, note that adding an extra space at the end of the first table name (here, for example, after “ …aug_2021” could prevent the error that is otherwise raised “ Not found: Dataset my-project-name:domain_public was not found in location ”) UNION using Standard SQL Tip: Keep an eye on the formatting changes between Legacy SQL and Standard SQL. According to the rules, its syntax is: SELECT Let’s re-run the query using the Legacy SQL syntax. Once you have selected which tables you want to work with, you have to list them, separated by a comma, and then hit “ Run”. Here we’ll test the syntax and see what it returns.Īs the name indicates, the Comma-Delimited Unions syntax uses commas to separate tables, and it’s shorter than the Standard SQL syntax. We’re creating two example tables with data for our clients for two different months: August 2021 and August 2022. Try creating them in Google Sheets and then uploading them to GCC in a. Pro Tip: Creating tables manually could result in an error. Let’s head over to Google Cloud Console (GCC) and try the Bigquery Sandbox to test the syntax. SELECT *column name* FROM `*respective table*` Its syntax is clear and logical: SELECT *column name* FROM `*respective table*` When your aim is to return all results when joining two tables into a vertical column, then UNION ALL is perfect for the job. What does their syntax look like when used practically? Let’s find out. Returns only the specified records from tables and queries Here’s an overview of the main differences to help you get a clearer picture regarding their use: UNION ALL Let’s see in what aspects the UNION ALL and UNION DISTINCT queries differ from each other in terms of speed and performance. Differences Between BigQuery UNION ALL and UNION DISTINCT Their names are self-explanatory: UNION ALL implies that there’s an inclusion of all values, and UNION DISTINCT implies that there’s an exclusion of non-unique values. Depending on your needs, you can select the query that will produce the best results.īoth queries can save you time and get you the data results you want without having to input multiple queries. BigQuery UNION ALL and UNION DISTINCTĪs discussed above, the BigQuery UNION can be utilised in two ways: as UNION DISTINCT and UNION ALL. Let’s further explore how this query can save the day. To save the time it takes to query each table, using the UNION operator, you can retrieve results using one query. By adding either ALL or DISTINCT keywords in the query, users can decide to either keep the duplicate values in the final results or have them removed.įor instance, you might have information for each client in your client database organised into tables by industry, and you’d prefer to have it all in one unified list. However, BigQuery UNION offers two options for processing the duplicate data. At its core, the fundamental functionality of this query function is to bring together the data from multiple result sets and unify them in a vertical manner. ![]() Google BigQuery UNION Overviewįor those who have worked with SQL queries before, the BigQuery UNION type should be familiar. Today we’ll explore how you can create UNION queries using both the Standard SQL syntax and the Legacy SQL syntax. When using BigQuery, you can do this by writing UNION queries. Unifying the query results into one dataset is a task that comes up pretty commonly while working with Google BigQuery and databases in general. The web service is accessible via Google Cloud Platform and offers many features that make working with data and analysing it much easier. The data warehouse provided by Google is equipped with a built-in query engine that allows anyone to quickly run queries without worrying about maintaining the warehouse infrastructure. Even if you have terabytes of data, you can query it in just minutes. Google BigQuery allows its users to process and analyze vast amounts of data in seconds. ![]()
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