Understanding Tables and Views in BigQuery

Understanding Tables and Views in BigQuery

Introduction

When you're working with Google BigQuery, you'll come across two key terms quite often: tables and views. If you're new to SQL or just starting with BigQuery, understanding the difference between these two can be really helpful. In this article, we'll break down what tables and views are in BigQuery, and how they differ from each other.

What are Tables in BigQuery?

Tables in BigQuery are pretty much what they sound like – they're where your data is stored. Think of them like spreadsheets in Excel, but much more powerful. Each table has rows and columns, with each row representing a record and each column representing a specific attribute of that record. For example, in a table for customer data, each row could be a different customer, and each column could be information like name, email, and purchase history. In our course, we worked only with tables.

Key Characteristics of Tables:
  1. Storage: Tables physically store your data.
  2. Data Modification: You can directly add, remove, or change data in a table.
  3. Performance: Queries on large tables might take longer, as they deal with more data.
What are Views in BigQuery?

Views are a bit different. They don't actually store data. Instead, think of a view as a saved query. A view takes data from one or more tables and shows it to you in a specific way. It’s like having a custom lens to look at your tables. For example, you could have a view that only shows customers from a specific region or those who have spent over a certain amount.

Key Characteristics of Views:
  1. No Data Storage: Views do not store data themselves; they fetch data from tables.
  2. Dynamic: A view always shows current data. When the underlying table data changes, the view automatically updates.
  3. Security and Simplicity: Views can limit what data is visible, which is great for security and making complex data simpler.
Main Differences Between Tables and Views
  1. Data Storage: Tables store data physically; views do not.
  2. Data Modification: You can directly modify data in tables, but not in views.
  3. Performance: Tables may be slower for complex queries, while views can make these queries faster since they act like a pre-set filter.
  4. Use Cases: Tables are used for data storage and manipulation, whereas views are used for specific perspectives on the data, security, and simplifying complex queries.

 

Exploring the Different Types of Views in Google BigQuery

An important feature of BigQuery is its use of 'views', which are virtual tables representing specific queries. For Google BigQuery learners, understanding the various types of views available can be beneficial

1. Standard Views

Overview: Standard views in BigQuery are akin to saved SQL queries. They do not store data physically but display results from a defined query.

        CREATE VIEW
        project.dataset.standard_view AS
        SELECT
            column1,
            column2
        FROM
            project.dataset.table
        WHERE
            condition;

Key Features:

  • Execute underlying SQL queries against datasets.
  • Do not store data themselves.

Use Cases: Ideal for simplifying complex queries, organizing data logically, and setting up security measures by limiting data access.

2. Materialized Views

Overview: Materialized views are an advanced feature in BigQuery that cache the result of a query. This is a step up from standard views, as they store the query result temporarily.

        CREATE MATERIALIZED VIEW
        project.dataset.materialized_view AS
        SELECT
            column1,
            AVG(column2)
        FROM
            project.dataset.table
        GROUP BY
            column1;

Key Features:

  • Cache query results for faster execution.
  • Particularly beneficial for queries with intensive aggregations.

Use Cases: Best suited for queries that are run frequently, where quick access to results is necessary. They help in improving performance and reducing query costs.

3. Authorized Views

Overview: Authorized views in BigQuery are about access control rather than a different type of functionality. They enable sharing query results with specific users or groups without granting access to underlying data tables.

An authorized view is about controlling access to the data. The query for creating an authorized view is the same to a standard view, but you also need to set up access controls in BigQuery.

Key Features:

  • Control data access at the view level.
  • Maintain data security and governance.

Use Cases: Essential for scenarios where data privacy is paramount. They allow for selective data sharing, ensuring users only access data they are permitted to see.

4. Partitioned Views

Overview: Though not a distinct category in BigQuery, partitioned views can be created using standard views over partitioned tables. They enable querying across multiple partitioned tables as if they were a single table.

BigQuery does not directly support creating partitioned views, but you can create a standard view that queries partitioned tables.

        CREATE VIEW
        project.dataset.partitioned_view AS
        SELECT
            column1,
            column2
        FROM
            project.dataset.partitioned_table
        WHERE
            _PARTITIONTIME >= 'start_date' AND _PARTITIONTIME < 'end_date';

Key Features:

  • Combine multiple partitioned tables into a single view.
  • Useful for managing large datasets that are divided into partitions.

Use Cases: Useful when data is spread across several partitioned tables and needs to be queried collectively. They streamline querying sharded tables.