Google BigQuery Scheduled Querying with SQL: 5 Easy Steps

on Data Warehouse, Google BigQuery, SQL • December 28th, 2021 • Write for Hevo

BigQuery Scheduled Query FI

BigQuery was launched in 2010 and it has become a popular Data Warehousing solution since then. It is a scalable Data Warehouse solution that helps businesses with varying data storage needs to store their data. With BigQuery, users can analyze up to terabytes of data in seconds. This has made BigQuery one of the most preferred Data Warehousing solutions. 

When using BigQuery, you may need to schedule some queries to be executed at a specified time with minimal human intervention. Although BigQuery hasn’t been supporting this feature for a long time, Google released the BigQuery BETA version with support for query scheduling. The feature doesn’t require you to write any code, only standard SQL, which makes it a good feature for data analysts in need of organizing their query flow within the same interface. 

In this article, you will get to know about BigQuery scheduled query in detail. 

Table of Contents

Prerequisites

This is what you need for this article:

  • A Google BigQuery Account. 

Understanding Google BigQuery 

BigQuery Scheduled Query: logo
Image Source

Google BigQuery is a serverless Data Warehouse with a built-in query engine that is highly scalable. Because it was created by Google, it makes use of Google’s infrastructure’s processing power. SQL queries on terabytes of data can be run in seconds, and petabytes in minutes, thanks to the Query Engine. This performance is provided by BigQuery without the requirement to maintain infrastructure or rebuild or construct indexes.

BigQuery’s scalability and speed make it ideal for processing large datasets. It also has built-in Machine Learning capabilities that can assist you in better understanding your data.

You can do the following with BigQuery:

  • With a scalable and secure platform that includes Machine Learning capabilities, democratise insights.
  • A multi-Cloud and adaptable analytics solution can help you make better business decisions based on data.
  • With little operating overhead, adapt to data of any scale, from bytes to petabytes.
  • Analyze data at a large scale.
  • Run large-scale Analytics.  

You can also use BigQuery to create Dashboards and reports to examine your data and acquire useful insights. It’s also a useful tool for performing real-time Data Analysis.

Simplify your Data Analysis with Hevo’s No-code Data Pipeline

Hevo Data, a No-code Data Pipeline helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. It supports 100+ data sources and loads the data onto the desired Data Warehouse, enriches the data, and transforms it into an analysis-ready form without writing a single line of code.

Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different Business Intelligence (BI) tools as well.

Get Started with Hevo for Free

Check out why Hevo is the Best:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Sign up here for a 14-Day Free Trial!

BigQuery Scheduled Query

You can create a BigQuery scheduled query and even have the query run recurrently. The scheduled queries should be written in standard SQL using DDL (Data Definition Language) or DML (Data Manipulation Language) statements. The query results can be organized by date and time by parameterizing both the query string and the destination table. 

Once you create or update the schedule for a BigQuery scheduled query, the scheduled time for the query will be converted from your local time to UTC. 

To create a BigQuery scheduled query, you should have the following IAM permissions:

  • bigquery.transfers.update or both bigquery.transfers.get (for creating the transfer) and bigquery.jobs.create.
  • bigquery.datasets.update (on your target dataset)
  • bigquery.jobs.create (for running the scheduled query)

For you to modify a BigQuery scheduled query, you should be the creator of the schedule and have the IAM permissions given below:

  • bigquery.jobs.create
  • bigquery.transfers.update

The roles/bigquery.admin IAM role, which is predefined, comes with permissions that can help you to schedule and modify queries. 

If you need to create or modify a BigQuery scheduled query that is run by a service account, you must be granted access to the service account. 

The following steps will help you to set up a BigQuery scheduled query:

Step 1: Creating New BigQuery Scheduled Query 

Open the BigQuery Account on the Cloud console. Run the query that you need to schedule. Once you get the desired results, click “Schedule” and then choose “Create new scheduled query”. 

BigQuery Scheduled Query: Step 1
Image Source

Step 2: Configuring Details for BigQuery Scheduled Query

The scheduled query options will be opened in the New scheduled query pane. Enter the following details on the pane:

  • Enter the name of the query in the “Name for the scheduled query” field. Ensure that you give the query a name that you can easily identify in case you need to alter the query. 
  • (Optional) By default, the query will run daily. However, you can change this. To change the frequency with which the scheduled query is executed, modify the “Repeats” option from “Daily” to what you desire. To set a custom frequency, choose “Custom” and enter a Cron-like time in the “Custom schedule” field. The shortest period allowed is 15 minutes. 
  • To modify the start time, choose the “Select start time” option. Enter the start time and date of choice and save it. 
  • To modify the end time, choose “Select end time” and enter the end date and time of your choice, then save it. 
  • To create a query with no schedule, select “On Demand” from the “Repeats” option. 
BigQuery Scheduled Query: Step 2
Image Source

Step 3: Defining the Destination

If you are creating a standard SQL SELECT query, provide the following details about the destination dataset:

  • Choose the right destination dataset for the “Dataset name”. 
  • Enter the name of the destination table in the “Table name”. This option will not be shown for a DDL or DML query. 
  • For “Destination table write preference”, select either “WRITE_APPEND” to append data to the table or “WRITE_TRUNCATE” to overwrite the destination table. This option will also not be shown for a DDL or DML query. 
BigQuery Scheduled Query: Step 3
Image Source

Step 4: Advanced Options

  • If you have customer-managed encryption keys, choose the “Customer-managed key” option under Advanced options. You will be presented with a list of encryption keys to choose a key from. 
  • If there are many service accounts linked to your Google cloud project, associate a service account to the scheduled query instead of using user credentials. Click the “Scheduled query credential” dropdown button to see the available service accounts. 
BigQuery Scheduled Query: Step 4
Image Source

Step 5: Additional Configurations

There are also additional configurations.

  • Check the “Send email notifications” box to receive emails of transfer run failures. 
  • For DDL and DML queries, select the “Processing location” or region. 
  • (Optional) For “Pub/Sub topic”, enter the name of your Pub/Sub topic. 
BigQuery Scheduled Query: Step 5
Image Source

Once done, click the “Schedule” button.  And that is how to create a BigQuery scheduled query. 

Conclusion

This write-up has exposed you to the various offerings on Google BigQuery to help you improve your overall database design and experience when trying to make the most out of your data. It defined what Data Analytics is all about and mentioned some BigQuery Scheduled Query that will ensure the best for you when refining your data set. In case you want to export data from a source of your choice into your desired Database/destination like Google BigQuery, then Hevo Data is the right choice for you! 

Visit our Website to Explore Hevo

Hevo Data provides its users with a simpler platform for integrating data from 100+ sources for Analysis. It is a No-code Data Pipeline that can help you combine data from multiple sources. You can use it to transfer data from multiple data sources into your Data Warehouses such as Google BigQuery, Database, or a destination of your choice. It provides you with a consistent and reliable solution to managing data in real-time, ensuring that you always have Analysis-ready data in your desired destination.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!

Share your experience of learning about BigQuery Scheduled Query! Let us know in the comments section below!

No Code Data Pipeline For Your Google BigQuery Data Warehouse