Users can scale up or down both the Storage and Compute power on their own, depending on their needs. ![]() To process read-only data, Google BigQuery is built on Google’s Dremel engine. Since Google BigQuery is part of the Google Cloud Platform (GCP), it can take advantage of Google Cloud Functions and other Google products to help you save time and get better results. It enables businesses to evaluate their data more quickly and generate insights using normal SQL queries. Google BigQuery is a fully-managed Cloud Data Warehouse that lets you use SQL to manage terabytes of data. Introduction to Google BigQuery Image Source If you are not familiar with these concepts, it will be worthwhile to look at these helper articles: It is assumed that you have worked with Google BigQuery in the past and know how to create datasets and tables in Google BigQuery. In the end, you’ll also briefly touch upon the concept of Nested Structs. In this article, you will learn how to create BigQuery Structs, how to use them in queries, and how to perform operations on these Structs. If you have worked with JSON files in the past, or with dictionaries in Python, you will feel at home with structs in BigQuery. Google BigQuery defines a struct as follows:Ĭontainer of ordered fields each with a type (required) and field name (optional). BigQuery Structs allow the storage of k ey-value pair collections in your tables. ![]() One of the common ways of representing data collections is through key-value pairs. Often, the data you are dealing with in your analysis does not belong to the conventional data types like int, float, boolean, string, etc. Understanding Nested Structs in Google BigQuery. ![]() Performing Operations on Google BigQuery Structs.
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