BigQuery Delete Table & Drop Table Commands Simplified 101

on BigQuery Delete Table, Data Warehouse, Google BigQuery, Tutorials • January 7th, 2022 • Write for Hevo

Venturing into Data Science and deciding on a tool to use to solve a given problem can be challenging at times especially when you have a wide array of choices. In this age of data transformation where organizations are constantly seeking out ways to improve the day to day handling of data being produced and looking for methods to minimize the cost of having these operations, it has become imperative to handle such data transformations in the Cloud as it is a lot easier to manage and is also cost-efficient.

Data Warehousing architectures have rapidly changed over the years and most of the notable service providers are now Cloud-based. Therefore, companies are increasingly on the move to align with such offerings on the Cloud as it provides them with a lower upfront cost, enhances scalability, and performance as opposed to traditional On-premise Data Warehousing systems. Google BigQuery is among one of the well-known and widely accepted Cloud-based Data Warehouse Applications.

In this article, you will gain information about undergoing BigQuery Delete Table. You will also gain a holistic understanding of Google BigQuery, its key features, managing BigQuery Tables and the different methods of deleting BigQuery Tables. Read along to find out in-depth information about undergoing BigQuery Delete Table.

Table of Contents

What is Google BigQuery?

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Google BigQuery is a Cloud-based Data Warehouse that provides a Big Data Analytic Web Service for processing petabytes of data. It is intended for analyzing data on a large scale. It consists of two distinct components: Storage and Query Processing. It employs the Dremel Query Engine to process queries and is built on the Colossus File System for storage. These two components are decoupled and can be scaled independently and on-demand.

Google BigQuery is fully managed by Cloud service providers. We don’t need to deploy any resources, such as discs or virtual machines. It is designed to process read-only data. Dremel and Google BigQuery use Columnar Storage for quick data scanning, as well as a tree architecture for executing queries using ANSI SQL and aggregating results across massive computer clusters. Furthermore, owing to its short deployment cycle and on-demand pricing, Google BigQuery is serverless and designed to be extremely scalable.

For further information about Google Bigquery, follow the Official Documentation.

Key Features of Google BigQuery

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  • Performance: Partitioning is supported by BigQuery, which improves query performance. The data may be readily queried using SQL or Open Database Connectivity (ODBC)
  • Scalability: Being quite elastic, BigQuery separates computation and storage, allowing customers to scale processing and memory resources according to their needs. The tool has significant vertical and horizontal scalability and runs real-time queries on petabytes of data in a very short period.
  • Security: When a third-party authorization exists, users can utilize OAuth as a standard approach to get the cluster. By default, all data is encrypted and in transit. Cloud Identity and Access Management (IAM) allows for fine-tuning administration.
  • Usability: Google BigQuery is a highly user-friendly platform that requires a basic understanding of SQL commands, ETL tools, etc.
  • Data Types: It supports JSON and XML file formats.
  • Data Loading: It employs the conventional ELT/ETL Batch Data Loading techniques by employing standard SQL dialect, as well as Data Streaming to load data row by row using Streaming APIs.
  • Integrations: In addition to operational databases, the system supports integration with a wide range of data integration tools, business intelligence (BI), and artificial intelligence (AI) solutions. It also works with Google Workspace and Cloud Platform.
  • Data Recovery: Data backup and disaster recovery are among the services provided by Google BigQuery. Users can query point-in-time snapshots of data changes from the last seven days.
  • Pricing Models: The Google BigQuery platform is available in both on-demand and flat-rate subscription models. Although data storage and querying will be charged, exporting, loading, and copying data is free. It has separated computational resources from storage resources. You are only charged when you run queries. The quantity of data processed during searches is billed.

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Managing Tables in BigQuery

One can manage your BigQuery tables in the following ways:

  • Update table properties:
    • Expiration time
    • Description
    • Schema definition
    • Labels
  • Rename a table
  • Copy a table
  • Delete a table
  • Restore a deleted table

Methods to undergo BigQuery Delete Table

You can delete a BigQuery table in the following ways:

  • BigQuery Delete Table: Using the Cloud Console.
  • BigQuery Delete Table: Using a Data Definition Language (DDL) DROP TABLE statement.
  • BigQuery Delete Table: Using the bq command-line tool bq rm command.
  • BigQuery Delete Table: Calling the tables.delete API method.
  • BigQuery Delete Table: Using the client libraries.

Currently, you can only delete one table at a time.

When you remove a table, all of its data is likewise deleted. Define the default table expiration for the dataset or set the expiration time when you create the table to automatically destroy tables after a specific period of time.

Required Permissions

To delete a table, you need the following IAM permissions:

  • bigquery.tables.delete
  • bigquery.tables.get

Each of the predefined IAM roles listed below includes the permissions required to delete a BigQuery table:

  • roles/bigquery.dataEditor
  • roles/bigquery.dataOwner
  • roles/bigquery.admin

You can also delete tables from datasets that you create if you have the bigquery.datasets.create permission.

For further information about IAM roles and permissions in BigQuery, you can visit here.

The following ways to showcase BigQuery Delete Table methods in this article are as follows:

A) Cloud Console

The steps followed to undergo BigQuery delete table are as follows:

  • Step 1: Go to the Explorer panel. Expand your project and dataset. Now, choose the table.
  • Step 2: Click the “Delete table” option in the details panel.
  • Step 3: In the popup, type “delete“.
  • Step 4: Click the “Delete” button to confirm.


Using standard SQL query syntax, DDL statements enable you to delete tables.

To delete an existing table using a DDL statement in the Cloud Console, you can follow the following steps:

  • Step 1: Click the “Compose new query” button.
  • Step 2: Fill in the Query editor field with your DDL statement. 
 DROP TABLE mydataset.mytable
  • Step 3: Click the “Run” button.

C) bq rm Command

To delete a table, you can use the bq rm command with the –table flag (or the -t shortcut). When deleting a table using the bq command-line tool, you must confirm the action. To skip confirmation, you can use the –force flag (or -f shortcut).

If the table is in a dataset in a project other than your default project, you can add the project ID to the dataset name, as shown below:

project id:dataset

bq rm 
<project_id value>:<dataset name>.<table name>

In the above syntax given, you can replace the following:

  • project_id: your project ID.
  • dataset: the name of the dataset that contains the table.
  • table: the name of the table that you’re deleting.


Enter the following command to delete the mytable table from the mydataset dataset. mydataset is a dataset in your default project.

bq rm -t mydataset.mytable

Enter the following command to delete the mytable table from the mydataset dataset. The dataset mydataset is in the project myotherproject, not your default project.

bq rm -t myotherproject:mydataset.mytable

Enter the following command to delete the mytable table from the mydataset dataset. mydataset is a dataset in your default project. To avoid confirmation, the command use the -f shortcut.

bq rm -f -t mydataset.mytable

D) API Method

You can call the tables.delete API method. And even specify the table name to delete using the tableId parameter.

E) Client Library: C#

To undergo BigQuery delete table in C# you can refer the following code snippet.

using Google.Cloud.BigQuery.V2;
using System;

public class BigQueryDeleteTable
    public void DeleteTable(
        string projectId = "your-project-id",
        string datasetId = "your_dataset_id",
        string tableId = "your_table_id"
        BigQueryClient client = BigQueryClient.Create(projectId);
        client.DeleteTable(datasetId, tableId);
        Console.WriteLine($"Table {tableId} deleted.");

F) Client Library: Go

To undergo BigQuery delete table in Go you can refer the following code snippet.

import (


// deleteTable demonstrates deletion of a BigQuery table.
func deleteTable(projectID, datasetID, tableID string) error {
        // projectID := "my-project-id"
        // datasetID := "mydataset"
        // tableID := "mytable"
        ctx := context.Background()
        client, err := bigquery.NewClient(ctx, projectID)
        if err != nil {
                return fmt.Errorf("bigquery.NewClient: %v", err)
        defer client.Close()

        table := client.Dataset(datasetID).Table(tableID)
        if err := table.Delete(ctx); err != nil {
                return err
        return nil

G) Client Library: Java

To undergo BigQuery delete table in Java you can refer the following code snippet.


public class DeleteTable {

  public static void runDeleteTable() {
    // TODO(developer): Replace these variables before running the sample.
    String datasetName = "MY_DATASET_NAME";
    String tableName = "MY_TABLE_NAME";
    deleteTable(datasetName, tableName);

  public static void deleteTable(String datasetName, String tableName) {
    try {
      // Initialize client that will be used to send requests. This client only needs to be created
      // once, and can be reused for multiple requests.
      BigQuery bigquery = BigQueryOptions.getDefaultInstance().getService();
      boolean success = bigquery.delete(TableId.of(datasetName, tableName));
      if (success) {
        System.out.println("Table deleted successfully");
      } else {
        System.out.println("Table was not found");
    } catch (BigQueryException e) {
      System.out.println("Table was not deleted. n" + e.toString());

H) Client Lirary: Node.js

To undergo BigQuery delete table in Node.js you can refer the following code snippet.

// Import the Google Cloud client library
const {BigQuery} = require('@google-cloud/bigquery');
const bigquery = new BigQuery();

async function deleteTable() {
  // Deletes "my_table" from "my_dataset".

   * TODO(developer): Uncomment the following lines before running the sample.
  // const datasetId = "my_dataset";
  // const tableId = "my_table";

  // Delete the table
  await bigquery

  console.log(`Table ${tableId} deleted.`);

I) Client Library: PHP

To undergo BigQuery delete table in PHP you can refer the following code snippet.

 use GoogleCloudBigQueryBigQueryClient;

/** Uncomment and populate these variables in your code */
// $projectId = 'The Google project ID';
// $datasetId = 'The BigQuery dataset ID';
// $tableId = 'The BigQuery table ID';

$bigQuery = new BigQueryClient([
    'projectId' => $projectId,
$dataset = $bigQuery->dataset($datasetId);
$table = $dataset->table($tableId);
printf('Deleted table %s.%s' . PHP_EOL, $datasetId, $tableId);

J) Client Library: Python

To undergo BigQuery delete table in Python you can refer the following code snippet.

from import bigquery

# Construct a BigQuery client object.
client = bigquery.Client()

# TODO(developer): Set table_id to the ID of the table to fetch.
# table_id = 'your-project.your_dataset.your_table'

# If the table does not exist, delete_table raises
# google.api_core.exceptions.NotFound unless not_found_ok is True.
client.delete_table(table_id, not_found_ok=True)  # Make an API request.
print("Deleted table '{}'.".format(table_id))

K) Client Library: Ruby

To undergo BigQuery delete table in Ruby you can refer the following code snippet.

require "google/cloud/bigquery"

def delete_table dataset_id = "my_dataset_id", table_id = "my_table_id"
  bigquery =
  dataset  = bigquery.dataset dataset_id
  table    = dataset.table table_id


  puts "Table #{table_id} deleted."

For further information on BigQuery Delete tables, you can visit here.


In this article, you have learned about BigQuery Delete Tables. This article also provided information on Google BigQuery, its key features, managing BigQuery Tables, and the different methods of deleting BigQuery Tables in detail. For further information on BigQuery JSON Extract, BigQuery Create View Command, BigQuery Partition Tables, you can visit the former links.

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