Cloud storages provide individuals and businesses with online platforms for the storage of data. Cloud storages provide their users with many benefits compared to On-Premise storage. Cloud users can access their data at any time and from any location. The Cloud is also very elastic, allowing users to scale their compute and storage resources based on their requirements. Due to this, many businesses are moving their data from On-Premise storage to the Cloud.
Google BigQuery is a Cloud Data Warehouse platform. It provides its users with an online platform where they can create Data Warehouses for data storage. Google BigQuery scales well to meet the changing storage and compute needs of its users. It also uses different mechanisms to provide security to the users’ data.
When using Google BigQuery, you will want to reduce the query costs. BigQuery has introduced a feature called BigQuery Cluster Tables to help you achieve this. BigQuery Clustered Tables automatically organize table data based on the values of one or more columns. Queries that use any of the clustering columns don’t scan unnecessary data. This improves the performance of queries run against the table. In this article, you will be exploring the steps to create Google BigQuery Cluster Tables in detail.
Table of Contents
- Introduction to Google BigQuery
- Introduction to Google Bigquery Cluster Tables
- Steps to Create a Google BigQuery Cluster Table
To successfully create a Google BigQuery Cluster Table, you need to meet the following requirements:
- A Google BigQuery Account.
- Basic understanding and working of Google BigQuery.
Introduction to Google BigQuery
Google BigQuery is a Cost-Effective, Serverless, and highly Scalable Multi-Cloud Data Warehouse designed and developed to offer business agility. BigQuery was developed by Google, hence, it uses the processing power of Google’s infrastructure. It comes with built-in Machine Learning capabilities that can help you to understand your data better.
You can use Google BigQuery in the following three main ways:
- Load and Export Data: With BigQuery, you can easily and quickly load your data into BigQuery. BigQuery will then process your data, after which you can export it for further analysis.
- Query and View Data: BigQuery allows you to run interactive queries. You can also run Batch queries and create Virtual tables from data.
- Manage Data: You can use BigQuery to list Jobs, Datasets, Projects, and Tables and update your datasets. BigQuery makes it feasible for you to delete and manage any data that you upload. BigQuery also allows you to create Dashboards and Reports that you can use to analyze your data and gain meaningful insights from it. It is also a powerful tool for real-time Data Analytics.
To explore more about Google BigQuery, visit here.
Introduction to Google Bigquery Cluster Tables
Google BigQuery has introduced a feature called Clustered Tables to help its users to get optimized performance from the Cloud platform when executing their queries. After creating a Clustered Table in BigQuery, its data is automatically organized based on the values of one or more columns in the schema of the table. The specified columns are used for colocating related data. When a BigQuery Cluster is created from multiple table columns, the order of the columns is very important as it determines the order in which the data will be sorted.
Clustered Tables improve the performance of particular types of queries such as queries that Aggregate data and queries that use Filter clauses. When you write data to a clustered table by a Load job or Query job, BigQuery uses the values of the Clustering columns to sort the data. The columns help BigQuery to organize the values into multiple blocks in the storage. When you run a query with a clause that filters data using the Clustering columns, BigQuery will use the sorted blocks to avoid scanning unnecessary data. This means that the queries will take a shorter time to return results.
In the next section, you will understand how to create BigQuery Cluster Tables for optimized query performance.
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Steps to Create a Google BigQuery Cluster Table
There are different ways through which you can create BigQuery Cluster Tables. In this section, you will learn how to create BigQuery Clustered Tables using the bq command-line tool.
Note: You can use up to four Clustering columns to create a Clustered Table.
The following steps can help you to create an empty Clustered Table and give it a Schema Definition:
Step 1: Open the BigQuery page on the Google Cloud Console.
Step 2: Expand your Project in the Explorer panel and select a Dataset.
Step 3: Expand the three vertical dots and select “Open”.
Step 4: Click the “Create table +” on the details panel.
Step 5: The Create table window will be opened. Click the “Create table from” drop-down menu and choose “Empty table”.
Step 6: Next, under the “Destination”, select the appropriate dataset under “Dataset Name” and enter the name of the table you are creating under “Table name”. Also, make sure that the “Table Type” is selected as “Native”.
Step 7: Enter the Schema Definition under “Schema”. Note that you must turn on the “Enable Text” toggle button to be able to enter the Schema name. The schema should also be entered as a JSON array. If you need to enter the schema manually, use the “+ Add Field” button.
Step 8: For the “Clustering Order”, enter the names of between one and four columns and separate their names using commas (,).
Step 9: Click the “Advanced Options” drop-down button. For Encryption activate the “Customer-Managed Key” radio button so as to use a Cloud Key Management Service Key. If you choose the “Google-Managed Key” option, BigQuery will encrypt your data at rest.
Step 10: Click the “Create Table” button.
Hurray! You have successfully created a Google BigQuery Cluster Table.
In this article, you learned about Google BigQuery and its services. Moreover, you understood about Google BigQuery Cluster Tables. In addition, you learned the steps to create BigQuery Cluster Tables via the bq console. Using the BigQuery Cluster technique you can input queries that use any of the clustering columns to filter data and this will take a shorter time to return your results.
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