Introduction
In the fast-paced business environment, organizations produce a huge amount of data, simply storing and organizing which is not enough and serves no purpose. It, therefore, becomes important for businesses to not just collect and store the data but also to analyze it in order to derive useful business insights, but there comes a challenge of collecting, maintaining, and analyzing these exponentially growing data using the outdated data warehouse technologies and here comes in role Google BigQuery, one of the best database analytics tools which allows you to trawl through huge amounts of data and find the right data for analysis.
In this article, we will have a look at What is Google BigQuery? We will have an overview of the BigQuery Create Table command and the methods for table creation in BigQuery. We will also explore Firebase BigQuery.
What is Google BigQuery?
Google BigQuery is a cost-effective enterprise data warehouse solution and is part of Google Cloud’s comprehensive data analytics platform designed for business agility. It helps businesses to manage and analyze the data with the help of inbuilt features like Machine Learning, Business Intelligence, and Geospatial Analysis. Google BigQuery’s serverless architecture allows high-scale operations and execution of SQL queries over large datasets. It is an enterprise-ready cloud-native data warehouse that covers the whole analytics ecosystem including ingestion, processing, and storage of data followed by advanced analytics and collaboration.
Google BigQuery Tables Overview
Google BigQuery Tables stores records that are organized in rows and each of the records is composed of columns which are also known as fields. Every table has a schema that describes the table details such as the column name, data type, and various other information. BigQuery tables are of three different types: –
- Native Tables: Native tables are those tables where the data is stored in native BigQuery Storage.
- External Tables: External tables are the tables backed by the storage which is external to BigQuery.
- Views: Views are virtual tables created by using SQL queries.
BigQuery Create Table Process
In order to begin with the BigQuery Create Table process, you are required to create a dataset that contains both tables and views, also there are some guidelines that need to be followed while naming Google BigQuery Tables such as the name of the table should be unique in the dataset and the length of the table name can be 1024 characters long which can have a lower case, upper case letter, number, and underscore. Now we will be looking at some of the methods which you can use to create tables in BigQuery.
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- BigQuery Create Table Using YAML Definition File
In order to create a BigQuery table using the YAML definition file, you can follow the below-given steps: –
- Create a YAML file, and upload it into the Google Cloud Shell. A sample YAML file is given below: –
resources:
– name: TestTableYAML
type: bigquery.v2.table
properties:
datasetId: test_dataset
tableReference:
projectId: testgcpproject
tableId: TestTableYAML
description: TestTableYAML
labels:
apigateway: not-applicable
build_date: 2458647
bus_contact: lahu
businessregion: us
cloudzone: google-us
company: lahu
costcenter: finance
country: india
dataclassification: dc2
department: product-development
eol_date: not-applicable
group: application-delivery
lifecycle: development
organization: lahu
prj_code: data_analytics
productversion: 1
project: gcp-cloud-data-lake
resourcename: location-table
service: gcp-cloud-datawarehouse
sla: silver
status: development
support_contact: lahu
tech_contact: lahu
tier: bigquery
schema:
fields:
– mode: nullable
name: ID
type: string
– mode: nullable
name: UserName
type: string
– mode: nullable
name: FullName
type: string
– mode: nullable
name: UserGroupID
type: string
– mode: nullable
name: create_date_time
type: datetime
– mode: nullable
name: update_date_time
type: datetime
- Now execute the below-given command in order to create a table in Google BigQuery: –
gcloud deployment-manager deployments create testtableyaml –config TestTableYAML.yaml
- BigQuery Create Table Command Using WebUI
Given below are the steps which will guide you on how to create a Google BigQuery table using WebUI: –
- Ensure that the Project and Dataset that will serve as the destination already exist.
- In the second step write a Query in the normal syntax and click on the Show Options button before executing it.
- Now the Destination Table section will appear. In that section, select the Table and then choose the Project, Dataset, and specify the Table Name to use as your destination.
- Click on Run Query and after execution, the results will be appended to the table that you have specified in the previous steps.
- BigQuery Create Table Using bq mk Command
The bq command-line tool is based on Python Language and can be used to implement BigQuery Create Table Command. You can follow the below steps to create a table using the bq mk command.
- First of all, create a dataset, if required, using the following command
bq mk test_dataset
- Now after creating the dataset, create a table in the “test_dataset” dataset using the following command.
bq mk –table –expiration 36000 –description “test table” bigquery_project_id:test_dataset.test_table sr_no:INT64,name:STRING,DOB:DATE
- BigQuery Create Table Command Using API
In order to create a Google BigQuery Table using the, you will be required to send a JSON-formatted configuration string to the API of your choice. We will be using “jobs. insert” API call in order to insert a new table in the database. Here, we need to specify some of the critical fields as a part of our configuration such as query, destinationTable, projectId, datasetId, tableId, createDisposition, writeDisposition, and WRITE_APPEND.
You can refer to the below image to see the API options provided by Google BigQuery.
Firebase BigQuery
Firebase, a NoSQL database program stores data in JSON-like documents. It can be linked with BigQuery which allows you to do various functions such as: –
- Analyzing the raw data with our own queries using the BigQuery SQL.
- Exporting the data to use with our own tools.
In order to link Firebase to BigQuery you can follow the given steps: –
- First of all, sign in to Firebase.
- Now click on the Settings icon, then select Project Settings.
- Now on the Project Settings page, click the Integrations tab.
- Now finally on the BigQuery card, click Link.
Conclusion
In this article, you have learned about What is Google BigQuery and its use cases for businesses, how it solves the problem of data analysis, and helps businesses make better decisions by providing useful insights. You have also learned about the BigQuery Create Table process, various different methods to create a table, and also about Firebase and how to link Firebase to BigQuery.
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