In today’s world organizations are constantly looking for data-driven insights. However, it can be difficult to Extract, Store, and Analyze that data as it rapidly grows in scale and scope. BigQuery, Google’s enterprise Data Warehouse, is designed to make large-scale Data Analytics accessible to everyone. When the size of your recorded data increases to Gigabytes, Terabytes, or even Petabytes, your organization needs a more efficient system like a Data Warehouse to manage the massive amount of data.

Google BigQuery offers no shortage of functionality to help you get the most out of Date and Time data, but it can be hard to know what to use and when. This article will introduce you to Google BigQuery and will list the various features that make it unique. Furthermore, this article will also describe the different types of BigQuery Date Functions along with their syntax and examples. Read along to learn more about BigQuery Date Functions!

Table of Contents

Prerequisites

  • Basic Understanding of SQL.

Introduction to Google BigQuery

BigQuery Date Functions - Google BigQuery Logo
Image Source

Google BigQuery is a fully managed Cloud-based Data Warehouse that is laid upon a Serverless architecture and provides a Big Data Analytics Web Service to process huge datasets over Petabytes of data. Google BigQuery is a Highly Scalable and Serverless Data Warehouse that houses an integrated Query Engine. The Query Engine is capable of executing SQL queries on datasets of mammoth scales in a span of just a few seconds. You can leverage this performance without having to manage any Infrastructure and without creating or rebuilding indexes.

Google leverages its existing Cloud architecture to successfully manage a Serverless design, and it also makes use of various data models that allow users to store dynamic data. It further provides support for Machine Learning (ML) operations by allowing users to take advantage of BigQuery ML functionality. BigQuery ML enables users to develop and train various Machine Learning Models by using built-in SQL capabilities to query data from the desired database. In the later section of this article, you will learn about different types of BigQuery Date Functions along with their syntax and usage.

Key Features of Google BigQuery

BigQuery Date Functions - Key Features of Google BigQuery
Image Source

Some of the key features of Google BigQuery are as follows:

  • Scalability: To provide consumers with true Scalability and consistent Performance, Google BigQuery leverages Massively Parallel Processing and a Highly Scalable Secure Storage Engine. The entire Infrastructure with over a thousand machines is managed by a complex software stack.
  • Serverless: The Google BigQuery Serverless model automatically distributes processing across a large number of machines running in parallel, so any company using Google BigQuery can focus on extracting insights from data rather than configuring and maintaining the Infrastructure/Server. 
  • Storage: Google BigQuery uses a Columnar architecture to store datasets of mammoth scales. Column-based Storage has several advantages, including better Memory Utilization and the ability to scan data faster than typical Row-based Storage.
  • Integrations: Google BigQuery as part of the Google Cloud Platform (GCP) supports seamless integration with all Google products and services. Google also offers a variety of Integrations with numerous third-party services, as well as the functionality to integrate with application APIs that are not directly supported by Google.

Simplify BigQuery ETL and Analysis with Hevo’s No-code Data Pipeline

A fully managed No-code Data Pipeline platform like Hevo Data helps you integrate and load data from 100+ different sources (including 40+ free sources) to a Data Warehouse such as Google BigQuery or Destination of your choice in real-time in an effortless manner. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources allows users to bring in data of different kinds in a smooth fashion without having to code a single line. 

Get Started with Hevo for free

Check out some of the cool features of Hevo:

  • Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
  • Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the Data Pipelines you set up. You need to edit the event object’s properties received in the transform method as a parameter to carry out the transformation. Hevo also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use.
  • Connectors: Hevo supports 100+ integrations to SaaS platforms, files, Databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Firebolt, Snowflake Data Warehouses; Amazon S3 Data Lakes; and MySQL, SQL Server, TokuDB, DynamoDB, PostgreSQL Databases to name a few.  
  • Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
  • 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
  • Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (including 40+ free sources) that can help you scale your data infrastructure as required.
  • 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
  • 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.
  • Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
Sign up here for a 14-day Free Trial!

8 BigQuery Date Functions: Syntax and Examples

BigQuery Date Functions are used to extract information from Date and Time data in Google BigQuery. Some of the most commonly used BigQuery Date Functions are discussed below:

1) Current Date Function

The Current Date Function is one of the most frequently used BigQuery Date Functions which returns the Current Date with respect to the specified timezone. This function can even be called without an argument. If no timezone is specified, UTC is selected as the default timezone but if the timezone parameter is evaluated to null then NULL is returned as the output.

SYNTAX

CURRENT_DATE([time_zone])

Following is an example of Current Date Function:

SELECT CURRENT_DATE() AS the_date;

+--------------+
| the_date     |
+--------------+
| 2021-09-13   |
+--------------+

2) Extract Function

BigQuery Date Functions - Extract Function
Image Source

Extract Function is one of the most important BigQuery Date Functions which is used to extract a specific part from the Date Expression. A complete Date Expression consists of Date, Month, and Year. Suppose you want to extract the month from the Date Expression then the Extract Function can be leveraged. In that case to get the value for the corresponding month. The return type for this Google BigQuery Date Function is INT64.

SYNTAX

EXTRACT(part FROM date_expression)

Following is an example of Extract Function:

SELECT EXTRACT(DAY FROM DATE '2021-09-13') AS the_day;

+---------+
| the_day |
+---------+
| 13      |
+---------+

3) Date_Add Function

BigQuery Date Functions - BigQuery Date Functions
Image Source

The Date_Add Function is one of the key BigQuery Date Functions which is used to add a specific interval to the date passed as an argument to the function. The return type for this function is a Date Expression.

SYNTAX

DATE_ADD(date_expression, INTERVAL int64_expression date_part)

Given below is an example of Date_Add Function:

SELECT DATE_ADD(DATE "2021-09-13", INTERVAL 5 DAY) AS five_days_later;

+--------------------+
| five_days_later    |
+--------------------+
| 2021-09-18         |
+--------------------+

4) Date_Sub Function

Similar to the Date_Add function, Date_Sub Function is used to subtract a specific time interval from the date passed as an argument to the function. The return type for this Google BigQuery Date Function is a Date Expression.

SYNTAX

DATE_SUB(date_expression, INTERVAL int64_expression date_part)

Given below is an example of Date_Sub Function:

SELECT DATE_SUB(DATE "2021-09-13", INTERVAL 5 DAY) AS five_days_ago;

+---------------+
| five_days_ago |
+---------------+
| 2021-09-08    |
+---------------+

5) Date_Diff Function

BigQuery Date Functions - Date_Diff Functions
Image Source

Date_Diff Function is used to calculate the time interval between the two dates specified. You can select the part of the date that you want to pass as an argument to the function. For example, if you want to calculate the gap between the two dates specified in days, you can pass the date as an argument to the function.

SYNTAX

DATE_DIFF(date_expression_a, date_expression_b, date_part)

Given below is an example of Date_Diff Function:

SELECT DATE_DIFF(DATE '2010-07-07', DATE '2008-12-25', DAY) AS days_diff;

+-----------+
| days_diff |
+-----------+
| 559       |
+-----------+

6) Date_From_Unix_Date Function

This function is used to interpret a new date since the number of days specified in the argument. The return type of this Google BigQuery Date function is a Date Expression.

SYNTAX

DATE_FROM_UNIX_DATE(int64_expression)

Given below is an example of Date_From_Unix_Date Function:

SELECT DATE_FROM_UNIX_DATE(14238) AS date_from_epoch;

+-----------------+
| date_from_epoch |
+-----------------+
| 2008-12-25      |
+-----------------+

7) Last Day Function

BigQuery Date Functions - Last Date Function
Image Source

The Last Day Function in Google BigQuery is used to find out the last date of the month. This function receives a date expression as the input and returns the last date of the corresponding month.

SYNTAX

SELECT LAST_DAY(‘DATE’, MONTH)

Given below is an example of Last Day Function:

SELECT LAST_DAY(DATETIME ‘2021–09–13’, MONTH) AS last_day

+-----------------+
| last_day |
+-----------------+
| 2021-09-30      |
+-----------------+

8) Parse Date Function

Parse Date Function is one of the most used BigQuery Date Functions which is used to convert a string representation of a date to a date object.

SYNTAX

PARSE_DATE(format_string, date_string)

Given below is an example of parsing of a string:

SELECT PARSE_DATE("%x", "09/13/21") AS parsed;

+------------+
| parsed     |
+------------+
| 2021-09-13 |
+------------+

If you want to learn more about BigQuery Date Functions, you can click here to check the official documentation.

Conclusion

In this article, you learned about Google BigQuery and the salient features that it offers. You also learned about Google BigQuery Date Functions and how you can use them to extract information associated with Date and Time data in Google BigQuery. With your Data Warehouse, Google BigQuery live and running, you’ll need to extract data from multiple platforms to carry out your analysis. However, integrating and analyzing your data from a diverse set of data sources can be challenging and this is where Hevo Data comes into the picture.

Visit our Website to Explore Hevo

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations such as Google BigQuery, with a few clicks. Hevo Data with its strong integration with 100+ sources (including 40+ free sources) allows you to not only export data from your desired data sources & load it to the destination of your choice, but also transform & enrich your data to make it analysis-ready so that you can focus on your key business needs and perform insightful analysis using BI tools.

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 Date Functions. Let us know in the comments section below!

mm
Former Research Analyst, Hevo Data

Rakesh is a Cloud Engineer with a passion for data, software architecture, and writing technical content. He has experience writing articles on various topics related to data integration and infrastructure.

No-code Data Pipeline for Google BigQuery

Get Started with Hevo