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BIGQUERY TIMESTAMP TO DATE FUNCTIONS SIMPLIFIED 101

Osheen Jain • September 30th, 2021



When a business grows, data also grows to massive volumes in a short time. In
the past decade, companies have been using data to obtain insights for improving
their Products, Business Operations, and Marketing. With most companies
depending on Big Data, Data Analysis has become the focal point of all major
digital transformations. However, the traditional Data Warehouse system can no
longer keep up with the growing demands.

Table of Contents

 * Prerequisites
 * Introduction to Google BigQuery
   * Key Features of Google BigQuery
 * Introduction to SQL
 * Timestamp Functions in Standard SQL
   * 1) CURRENT_TIMESTAMP
   * 2) EXTRACT
   * 3) STRING
   * 4) TIMESTAMP
 * Date Functions in Standard SQL
   * 1) CURRENT_DATE
   * 2) EXTRACT
 * Simplify BigQuery ETL and Analysis with Hevo’s No-code Data Pipeline
 * BigQuery Timestamp to Date Functions
 * Convert Timestamp to Date Data Type in Google BigQuery
   * Converting using CAST
   * Converting from STRING using PARSE
   * Converting with Extract()
 * Conclusion

BigQuery by Google is an entirely serverless Data Warehouse with high
availability and Petabyte scalability. BigQuery is based on Dremel; Google’s
distributed system for querying large datasets and storing data in a columnar
format. It uses tree architecture to parallelize requests in many machines. Each
SQL query scans the entire table, producing results and insights in just seconds
(even for tables with millions of rows).

In this article, you will learn about BigQuery Timestamp to Date Functions. You
will also get a holistic understanding of Google BigQuery, its key features,
data types in BigQuery, and SQL Date & Time functions. Read along to find out
how to convert Timestamp to Date using BigQuery Timestamp to Date Functions.


TABLE OF CONTENTS

 * Prerequisites
 * Introduction to Google BigQuery
 * Key Features of Google BigQuery
 * Introduction to SQL
 * Timestamp Functions in Standard SQL
 * Date Functions in Standard SQL
 * BigQuery Timestamp to Date Functions
 * Convert Timestamp to Date Data Type in Google BigQuery
 * Conclusion


PREREQUISITES

 * A Google BigQuery account.
 * Basic understanding of SQL.


INTRODUCTION TO GOOGLE BIGQUERY


Image Source

Google BigQuery, launched in 2011, is a Cloud Data Warehouse. It is capable of
analyzing Terabytes of data within seconds. If you have experience with writing
SQL Queries, you can quickly start querying data on the BigQuery platform. You
will need a GCP console or the classic web UI to access Google BigQuery. You can
do it by calling BigQuery Rest API using various Client Libraries such as Java,
.Net, or Python or using a Command-Line tool. You can use third-party tools for
visualizing the data or loading the data to interact with Google BigQuery. 

Unlike the Row-based storage structure used in Relational Databases, Google
BigQuery uses a Columnar Storage Structure or Column-based Storage. It ensures
the use of fewer resources to achieve faster query processing. Columnar Storage
is how BigQuery handles large datasets and delivers excellent speed. Storing
data in columns is efficient for analytical purposes because it needs a faster
data reading speed.


KEY FEATURES OF GOOGLE BIGQUERY


Image Source

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

 * Integrations: BigQuery is a part of Google’s Cloud Platform which means
   you’ll have access to Google products, including Google Analytics and Google
   Ads.
 * Serverless: You don’t need any attachments to use the BigQuery Cloud service.
   In addition, employees will always have secure access to data irrespective of
   their location. 
 * Data Processing Speed: BigQuery enables real-time analysis on Structured and
   Semi-Structured Data. Companies can use SQL queries with ease and at any
   scale.
 * Data Security: Your company’s data in BigQuery is protected according to
   Google’s standards.
 * Cost: Every user receives up to 1 TB of requests and 10 GB for storage for
   free per month. In addition, you’ll also receive $300 for 90 days to pay for
   services on the Google platform. 
 * BigQuery ML: Experts can build prediction models on Structured and
   Semi-Structured Data using existing SQL tools and skills.

You can follow the Official Documentation for further information about Google
BigQuery.


INTRODUCTION TO SQL


Image Source

Structured Query Language or SQL is used to manage relational databases and
perform various operations on their data. It is a standardized Programming
Language that was initially created in the 1970s. SQL is frequently used by
Database Administrators, Data Analysts looking to set up and run analytical
queries, and developers writing data integration scripts. With SQL, you can
modify (Add, Update, and Delete) index structures and database tables and
retrieve subsets of information from within a database. Commonly used SQL
statements include Add, Insert, Update, Select, Delete, Create, Alter, and
Truncate.

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When working in SQL, it’s common to use dates and times data types and
functions. Using these data types, you can calculate trends in the data, changes
over time, and perform interval arithmetic. It helps companies to better
understand the impacts of the underlying business problem. Later in this
article, you will learn about BigQuery Timestamp to Date Functions and how you
can convert data types using BigQuery Timestamp to Date Functions.


TIMESTAMP FUNCTIONS IN STANDARD SQL

Following are some of the most commonly used Timestamp Functions in Standard
SQL:


1) CURRENT_TIMESTAMP

This function produces a continuous, non-ambiguous timestamp that has exactly 60
seconds per minute. Also, the Timestamp Function does not repeat values over the
leap second. 

SYNTAX

CURRENT_TIMESTAMP()


2) EXTRACT

You can return a specific part from the supplied Timestamp expression with this
command. It supports an optional timezone parameter if you don’t want to use the
default time zone. 

SYNTAX

EXTRACT(part FROM timestamp_expression[AT TIME ZONE timezone])

You can extract the following PARTS of date and time with this function:

 * MICROSECOND
 * MILLISECOND
 * SECOND
 * MINUTE
 * HOUR
 * DAY OF WEEK
 * DAY
 * DAY OF YEAR
 * WEEK
 * WEEK(<WEEKDAY>)
 * ISO WEEK
 * MONTH
 * QUARTER
 * YEAR
 * ISO YEAR
 * DATETIME
 * TIME

The return data type is INT64 except when the return data type is DATE,
DATETIME, and TIME.


3) STRING

You can use this function to convert a Timestamp expression to a String data
type. You can specify a different timezone if you don’t want the default one. 

STRING(timestamp_expression[, timezone])


4) TIMESTAMP

There are three parameters in this function.

 * The first parameter converts a String expression to a Timestamp data type,
   and you can specify a time zone. string_expression[, timezone]
 * The second parameter converts a Date object to a Timestamp data type.
   date_expression[, timezone]
 * The third parameter converts a DateTime object to a Timestamp data type.
   datetime_expression[, timezone]

SYNTAX

TIMESTAMP(string_expression[, timezone])
TIMESTAMP(date_expression[, timezone])
TIMESTAMP(datetime_expression[, timezone])



DATE FUNCTIONS IN STANDARD SQL

There are the following DATE functions in standard SQL that BigQuery supports:


1) CURRENT_DATE

You can use this function to find the Current Date in the specific or default
timezone. If you don’t want to use the default time zone, use the time_zone
parameter.

SYNTAX

CURRENT_DATE([time_zone])


2) EXTRACT

You can use this function to return a specific date part. The parts can be
DAYOFWEEK, DAY, DAYOFYEAR, WEEK, WEEK(<WEEKDAY>), MONTH, QUARTER, YEAR, ISO
YEAR, and ISOWEEK. 

EXTRACT(part FROM date_expression)

You can also refer to this article to gain a deeper understanding of BogQuery
Date Functions.


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set up in just a few minutes allowing the users to load data without having to
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   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
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BIGQUERY TIMESTAMP TO DATE FUNCTIONS


Image Source

There are four BigQuery Timestamp to Date Functions. They are as follows:

 * Date type: Denotes a calendar date, and the following information is
   included: Year, Month, and Date: YYYY-MM-DD. This data type doesn’t include
   any information on time zone (e.g., 2021-07-15).
 * Time type: Shows time similar to a digital watch; it is not date-dependent.
   The format is: HH:MM: SS (e.g., 17:35:14)
 * Datetime type: Includes both the calendar time and date. However, it does not
   keep track of time zones. The format is YYYY-MM-DD HH:MM: SS (e.g.,
   2021-07-15 17:35:14).
 * Timestamp type: Includes all three: Date, Time, and Time Zone information. By
   default, the timezone is set to UTC. You can also specify a time zone. The
   format is: YYYY-MM-DD [Timezone] HH:MM:SS (e.g. 2021-07-15 17:35:14 UTC).

You can perform the following functions on the above date and time function
groups:

 * Find current DateTime
 * Change format
 * Add and subtract your date/time
 * Subtract date/time
 * Group date/time
 * Extract specific parts
 * Calculate the difference between two dates/times


CONVERT TIMESTAMP TO DATE DATA TYPE IN GOOGLE BIGQUERY

Now that you have a basic idea of BigQuery Timestamp to Date Functions. you are
ready to learn about how to convert Timestamp to Date data type in Google
BigQuery. In general, you can stick with TIMESTAMPs if you want to work with
time zones. However, DATETIME is the most flexible datatypes since you can take
advantage of both date and time functionality.


CONVERTING USING CAST

The CAST function in SQL converts one data type to another. You can convert your
STRING to one of the date data types. To do this, ensure that your STRING is in
the following formats:

 * DATE: YYYY-MM-DD
 * TIME: HH:MM: SS
 * DATETIME: YYYY-MM-DD HH:MM: SS
 * TIMESTAMP: YYYY-MM-DD HH:MM: SS [timezone]


CONVERTING FROM STRING USING PARSE

To use one of the PARSE functions, you can format STRING in different ways;
you’ll just tell the function how it should read it. There is a PARSE function
for each Date/Time Data type:

 * DATE: PARSE_DATE(format_string, date_string)
 * DATETIME: PARSE_DATETIME(format_string, datetime_string)
 * TIMESTAMP: PARSE_TIMESTAMP(format_string, timestamp_string[, timezone])
 * TIME: PARSE_TIME(format_string, time_string)


CONVERTING WITH EXTRACT()

The EXTRACT() function in SQL provides access to temporal data types—Date,
Timestamp, Interval, and time. You can pull a specific date/time format out of
timestamp with this command. For example, you can extract just the month from
the date 2021-07-15. The output will only be July (07).

Extract fieldMeaningYEARYearMONTHMonthDAYDay of monthHOUR24
hourMINUTEMinuteSECONDSeconds (including fractions)TIMEZONE_HOURTime zone
hourTIMEZONE_MINUTETime zone minute


CONCLUSION

Queries take seconds to get executed with Google BigQuery. Analysts throughout
the companies can then take advantage of this efficiency to make decisions based
on faster insights, build visualization reports on aggregate data, and forecast
trends based on historical data more accurately. This article introduced you to
Google BigQuery Timestamp to Date Functions and how to convert Timestamp to Date
using the BigQuery Timestamp to Date Functions.

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.

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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.



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