Google BigQuery Temporary Tables: 3 Comprehensive Aspects

More and more organizations are looking to unlock business insights from the data. However, it can be difficult to ingest, store, and analyze that data as it rapidly grows in scale and scope. Google’s enterprise Data Warehouse, BigQuery, has been designed to make large-scale Data Analytics accessible to everyone. When the size of recorded data grows to gigabytes, terabytes, or even petabytes, an enterprise surely needs a more efficient system like a Data Warehouse, to manage their vast amount of data.

This article will introduce you to Google BigQuery and will list the various features that make it so popular. It will also help you understand BigQuery Temporary Tables and will explain the methods to create them. Furthermore, the article will also describe the steps required to delete BigQuery Temporary Tables. Read along to learn more about this tool!

Table of Contents

• Introduction to Google BigQuery
• Key Features of Google BigQuery
• Understanding Google BigQuery Temporary Tables
• Methods to Create BigQuery Temporary Tables
  • Creating BigQuery Temporary Tables Method 1: Using the TEMP Parameter
  • Creating BigQuery Temporary Tables Method 2: Using the TEMPORARY Parameter
• Deleting BigQuery Temporary Tables
• Conclusion

Introduction to Google BigQuery

Google BigQuery is a highly scalable Data Warehouse and is well known for storing and querying data rapidly. It is a Cloud-based serverless Data Warehouse that allows ETL users to process data using multiple SQL queries. Google BigQuery supports real-time bulk data loading and uses the columnar format to store data.

Google BigQuery has the capability to handle massive amounts of data e.g., logs from outlets of retail chains down to the SQL level or IoT data from millions of utility meters, telecom usage, and vehicles across the globe. Powered by Google, BigQuery is certainly a Platform as a Service (PaaS) offering a fully managed Data Warehouse in a serverless architecture. Moreover, it is popular because it enables organizations to focus on Data Analytics instead of managing the infrastructure.

To learn more about Google BigQuery, visit here.

Key Features of Google BigQuery

The following features make Google BigQuery a popular choice in today’s market:

• Being a serverless architecture, Google BigQuery operates on the Cloud platform, thus facilitating the scalability of Data Analytics automatically. 
• Google BigQuery allows the users to capture the best of the decision-making insights by forming and implementing Machine Learning algorithms using SQL. It offers real-time Data Analytics based upon high-speed streaming insertion API. The user just needs to incorporate the real-time data and Google BigQuery can analyze it instantaneously. 
• By design, Google BigQuery helps one avoid the data silo problem owing to the existence of individual teams in an organization, having their independent Data Marts as it offers cross-team communication concerning any of the Databases.  
• Owing to the integration of the subject tool with Google Cloud’s native identity and access management frameworks, the user can take control of the permissions and relevant access criteria for specific individuals, teams, or ventures thus enabling the safety and security of classified data to keep the classified all while still empowering the cross-team communications. 
• Working with data in Google BigQuery involves three primary parts – storage, ingestion, and querying. Being a fully managed provision, one doesn’t need to set up or install anything and even doesn’t need the database administrator. One can simply log in to the Google Cloud project from a browser and get started. 
• Data in BigQuery is stored in a structured table, which means one can use standard SQL for easy querying and Data Analysis. It is perfect for Big Data because Google BigQuery manages all the storage and scaling operations automatically for the client. 
• Of course, storing the data alone doesn’t matter if one can’t get into Google BigQuery in the first place. There are a lot many ways to do that as Google BigQuery is integrated with many of the Data Analytics platforms. Once the data is in Google BigQuery, one can use SQL, having worked with ANSI-compliant Relational Databases in the past. 
• Google BigQuery also supports the data transfer service via which users can get data from multiple sources on a scheduled basis e.g., Google Marketing Platform, Google Ads, YouTube, Partner SaaS applications to Google BigQuery, Teradata, and Amazon S3. 
• Additionally, the user can share access with other users, so that they can also derive insights from the relevant datasets. Google BigQuery provides users the flexibility to bypass the ingestion and storage steps, by analyzing Google BigQuery Public Datasets that are third-party Datasets that have been made public for anyone to query against. 
• Considering the essential nature of data for any organization, Google BigQuery offers automatic backup and restore options. It also keeps track of the performed changes on a 7-days basis so that comparison with previous versions can be done if necessary and recall any changes accordingly.

Understanding Google BigQuery Temporary Tables

Insights are extracted from the Google BigQuery Data Sets using SQL queries. It provides users with the flexibility to save the search results in BigQuery Temporary Tables. The essence of BigQuery Temporary Tables is that they cache the query results, can be named randomly and stored in a special dataset. Once the user is done querying, the BigQuery Temporary Tables can be retained for the next 24 hours and afterward get deleted automatically. The subject tables are primarily used to save the intermediate results, thus avoiding the hassle to save or maintain them in a given dataset. The visibility of all the BigQuery Temporary Tables can be achieved by the following sequence:

• Navigate to the BigQuery console.
• Click Query history, and select the query via which a BigQuery Temporary Table has been created. 
• Move to the Destination Table row and click the Temporary Table option.

An important point to keep in mind about the sharing of BigQuery Temporary Tables is that they can neither be shared nor are they visible if the user employs any standard list or the given table manipulation frameworks. 

Methods to Create BigQuery Temporary Tables

BigQuery Temporary Tables provide a lot of functionality and can be created by the following 2 methods:

• Creating BigQuery Temporary TablesMethod 1: Using the TEMP Parameter
• Creating BigQuery Temporary Tables Method 2: Using the TEMPORARY Parameter

Creating BigQuery Temporary Tables Method 1: Using the TEMP Parameter

You can use the TEMP parameter to set up a new Temporary Table as follows:

• The Syntax for TEMP Parameter
• Use Case for TEMP Parameter
• Example of Using the TEMP Parameter

The Syntax for TEMP Parameter

The following syntax can be used for creating BigQuery Temporary Tables:

Use Case for TEMP Parameter

TEMP parameter is primarily a user-defined function (UDF) that can create temporary tables in the Google BigQuery. It gives users the flexibility to make customize tables for data visualization, as per the analytics requirements. More so, the use-case of TEMP is in the local temporary tables i.e., only visible to the current session.

Example of Using the TEMP Parameter

A self-explanatory example of creating BigQuery Temporary Tables using the TEMP parameter is described as follows where a Temporary Table is also being used at a sub-query level. 

-- Find the top 100 names from the year 2017.
CREATE TEMP TABLE top_names(name STRING)
AS
 SELECT name
 FROM `bigquery-public-data`.usa_names.usa_1910_current
 WHERE year = 2017
 ORDER BY number DESC LIMIT 100
;
-- Which names appear as words in Shakespeare's plays?
SELECT
 name AS shakespeare_name
FROM top_names
WHERE name IN (
 SELECT word
 FROM `bigquery-public-data`.samples.shakespeare
);

Creating BigQuery Temporary Tables Method 2: Using the TEMPORARY Parameter

You can use the TEMPORARY parameter to set up a new Temporary Table as follows:

• The Syntax for TEMPORARY Parameter
• Use Case for TEMPORARY Parameter
• Example of Using the TEMPORARY Parameter

The Syntax for TEMPORARY Parameter

The syntax for the TEMPORARY parameter is the same as that of TEMP and is given as follows:

Use Case for TEMPORARY Parameter

Usage of TEMP and TEMPORARY parameters are the same, except for the fact that TEMPORARY is used for the creation of global Temporary Tables; these tables are visible to all the associated connections. Essentially, if a global Temporary Table is created in one session, it can still be used in the other sessions. 

Deleting BigQuery Temporary Tables

Removing or deleting the BigQuery Temporary Tables is the mean by which users can keep the Database clean and clutter-free. There are certain methods available for achieving the subject target, but the most often used framework is the Drop Command in syntax.

Using the DROP Command to Delete Temp Tables

You can use the DRO command to delete a Temporary Table as follows:

• The Syntax of DROP Command
• Use Case of the DROP Command
• Example of the DROP Command

The Syntax of DROP Command

The syntax for using the Drop command in Google BigQuery is very simple and is given as follows:

The notion of IF EXISTS is critical as sometimes, the given table doesn’t exist in the dataset and the system gives the following error. 

Use Case of the DROP Command

DROP function is primarily used to delete the entire Temporary Table and not some specific part of it. It is a critical part of Data Defined Language (DDL) in Google BigQuery. During data querying and visualization, BigQuery Temporary Tables are used to extract valuable insights; the redundant tables can be removed by using the DROP function. To delete specific rows or columns of the table, the DELETE command is used. 

Example of the DROP Command

The following query will delete the table named “mytable” located in the dataset “mydataset”.

Key Considerations for Temporary Tables in Google BigQuery

Although temporary tables play a key role in managing and processing large datasets, some key considerations can be followed to ensure the optimal performance in Google BigQuery:

• To write or save the results to a temporary table, a host of permissions must be granted to the user, which might include:
• Creation of new tables: 

bigquery.tables.create

• Filling of data into new tables, overwrite the associated data, or append the same : 

bigquery.tables.updateData

• Running a query task:

bigquery.jobs.create

• BigQuery Temporary Tables are generally considered slow to query tables as they might entail a lot many records positioned in the database. Additionally, BigQuery Temporary Tables are primarily a view logic i.e., containing directions to the data and thus consuming more time. 
• BigQuery Temporary Tables are short-lived i.e., they are session-based and information stored in them gets deleted once a session is aborted/closed, or completed. 
• While creating a BigQuery Temporary Table, it is important not to employ a project or dataset qualifier in the table name. The table is already provisioned to be created in a special dataset. 

Conclusion

Google BigQuery platform enables organizations to realize the real strength of Data Analytics while offering a variety of features to extract meaningful insights for optimal decision making. The BigQuery Temporary Tables are one such feature that provides users the autonomy to visualize the requisite data, without adding much burden on the storage side. The feature offers a lot of flexibility in terms of creating the data-filled layout, updating the information, and most importantly configuring the policies for automatic deletion of the table records. All in all, Temporary Tables in BigQuery further complement the process of information extraction, thus enabling the users to derive key insights which result in efficient and effective managerial decisions. 

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