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.
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
- 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
- A Google BigQuery account.
- Basic understanding of SQL.
Introduction to Google BigQuery
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
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
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.
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:
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.
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.
EXTRACT(part FROM timestamp_expression[AT TIME ZONE timezone])
You can extract the following PARTS of date and time with this function:
- DAY OF WEEK
- DAY OF YEAR
- ISO WEEK
- ISO YEAR
The return data type is INT64 except when the return data type is DATE, DATETIME, and TIME.
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.
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]
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:
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.
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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BigQuery Timestamp to Date Functions
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).
|DAY||Day of month|
|SECOND||Seconds (including fractions)|
|TIMEZONE_HOUR||Time zone hour|
|TIMEZONE_MINUTE||Time zone minute|
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.
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