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Abstract

[1].You can use the following blueprints to try it out:<div id="af0c"><pre>LOAD DATA INTO mydataset.table(x INT64, y STRING) FROM FILES( skip_leading_rows=1, format='CSV', uris = ['gs://bucket/path/your_file.csv'] )</pre></div>The full documentation and all parameters you can use within the function you can find at the official Google site [1]. This new function with the already existing export function makes sense, because you can now read data from external sources via SQL, since Google is focusing on this area with Google BigLake and Analytics Hub.Google BigLake is designed to allow companies to unify their Data Warehouses and Data Lakes without having to worry about the underlying storage format or system. You can easily access Google internal storage as well as cross-platform data such as Microsoft Azure or AWS within BigQuery using SQL, with

Options

out having to cache it. Read more about it <a href="https://readmedium.com/google-launches-new-data-lakehouse-engine-big-lake-4648d6be458e">here</a>.<figure id="1d8e"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/0*I-dL1L8hgz3FjFKA.png"><figcaption>Google BigLake — Source: <a href="https://www.nextplatform.com/2022/04/06/google-biglake-stretches-bigquery-across-all-data/">THENEXTPLATFORM</a></figcaption></figure>If you using GCP and BigQuery more often like me, you may also be interested in these articles:<ul><li><a href="https://readmedium.com/378a65fdc9c1">BigQuery now supporting Query Queues</a></li><li><a href="https://readmedium.com/8ef3df2af71d">3 Big Updates in Google Data Studio</a></li><li><a href="https://readmedium.com/817191ddc636">Google improves Data Security in it’s Data Warehouse BigQuery</a></li><li><a href="https://readmedium.com/c0363d303632">3 Big Announcements from Google</a></li></ul><h2 id="5d3a">Sources and Further Readings</h2>[1] Google, <a href="https://cloud.google.com/bigquery/docs/reference/standard-sql/other-statements#load_data_statement">Load Data Statement</a> (2022)</article></body>

Using the Load Data Statement in Google BigQuery

How to use the new Function in BigQuery SQL

If you’re like me and often work with Google Cloud and BigQuery, then you’re probably always eager to see new updates that will help you in your daily life, one of them is this new LOAD DATAfeature.

While Google already had the EXPORT DATA statement implemented which allows to export the results of a query to an external storage location Google now also published (in preview) the LOAD DATA statement. It loads data from one or more files into a table and can create a new table, append data into an existing table, or overwrite an existing table [1].

You can use the following blueprints to try it out:

LOAD DATA INTO mydataset.table(x INT64, y STRING)
  FROM FILES(
    skip_leading_rows=1,
    format='CSV',
    uris = ['gs://bucket/path/your_file.csv']
  )

The full documentation and all parameters you can use within the function you can find at the official Google site [1]. This new function with the already existing export function makes sense, because you can now read data from external sources via SQL, since Google is focusing on this area with Google BigLake and Analytics Hub.

Google BigLake is designed to allow companies to unify their Data Warehouses and Data Lakes without having to worry about the underlying storage format or system. You can easily access Google internal storage as well as cross-platform data such as Microsoft Azure or AWS within BigQuery using SQL, without having to cache it. Read more about it here.

Google BigLake — Source: THENEXTPLATFORM

If you using GCP and BigQuery more often like me, you may also be interested in these articles:

Sources and Further Readings

[1] Google, Load Data Statement (2022)