Redshift Datepart Function 101: Syntax and Usage Simplified

Sanchit Agarwal • Last Modified: December 29th, 2022

Redshift Datepart Feature Image

With the emergence of Cloud Data Warehouses, enterprises are gradually moving towards Cloud storage leaving behind their On-premise Storage systems. This shift is observed owing to the high computing power and on-demand scalability offered by them. Amazon Web Services is one such Cloud Computing platform that offers Amazon Redshift as their Cloud Data Warehouse product. Users can seamlessly transfer their data from various SaaS applications and other databases to Amazon Redshift. Owing to its amazing speed and performance, you can run multiple Queries using SQL and generate insightful reports from your BI Tools in real-time.

One of the important data points required for generating meaningful reports is the date and time at which an event occurred. You can use the Redshift Datepart command to query data based on a specific date and time.

In this article, you will learn how to work with Redshift Datepart in detail with the help of a few easy-to-understand examples.  

Table of Contents

Introduction to Redshift

Amazon Redshift
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Amazon Redshift is a brilliant Cloud-based Data Warehouse run by Amazon Web Services that allows companies to store petabytes of data in scalable storage units known as “Clusters” which can be queried in parallel. Inside these Clusters are the computing resources called Nodes. You can scale your Cluster within minutes and analyse your data with great speed.

Based on PostgreSQL 8, you can efficiently carry out multiple complex queries at a time and gain real-time data insights for decision-making and predictive analysis. Using SQL you can easily query large volumes of Structured and Semi-Structured data and save your results back to S3 Data Lake. Also, various departments of a firm can benefit from Redshift as each team can own individual nodes and access them easily without experiencing any waiting time or delays. 

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Download the Cheatsheet on How to Set Up High-performance ETL to Redshift
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Key Features of Redshift

Since its inception in 2012, continuous improvement and development is observed by the constant effort of developers at Amazon to provide the following intuitive features:

  • AWS Ecosystem: For users already familiar with other Amazon products, Redshift allows them to transfer your data back to the S3 data lake for further analysis using tools like Amazon Athena, Amazon SageMaker, etc. With easy migration within AWS services, you get end-to-end data management solutions with little to no friction at all.
  • Unbeatable Performance: Amazon Redshift enhances the speed for varying workloads using its Machine Learning Capabilities. Technologies such as R3 instances and AQUA (Advanced Query Accelerator) provide superior performance for resource-intensive Workloads. For the repeated queries you get better performance as Redshift reads the saved results from a prior run.
  • Flexible Pricing: Amazon offers this Redshift on an hour, year, and even on a query basis. It is highly cost-effective as you can choose an optimal number of Nodes on the basis of your workloads and pay exactly for what you need. With Free Concurrent Scaling credits you earn each day, you can comfortably scale-up using these points and predict the next month’s cost, no matter the oscillating workloads.
  • Scalability: With fully managed storage, you can scale with a few clicks or simple API calls. You also get storage savings with the in-built compression encoding for numeric and date/time data types. It also allows concurrent queries against petabytes of data which doesn’t need any loading and transformation.
  • Manageable: From the beginning, you get simple and clear instructions to set up and operate Redshift quickly and efficiently. It automatically optimises the Clusters for the fluctuating workloads.   
  • Completely Secure: Redshift provides SSL Security for data in transit and AES (Advanced Encryption Standard) 256-bit encryption for data at rest. This allows you to safely share data across Redshift Clusters.

To know more about these amazing features, you can visit the Official Amazon Redshift Features page.    

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Redshift Datepart Function: Syntax and Examples

Redshift Datepart
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The date and time for an event are the most commonly recorded data that is stored by many applications in various organisations. The date data types are complex and generally consist of Century, Year, Month, Day, Hour, Minute, and Second. To query a specific part of the date and time data in an SQL environment, you can use the Redshift Datepart function. To understand the functionality properly let’s take a look at the Redshift Datepart Syntax.

1) Redshift Datepart Syntax

You can execute the Redshift Datepart in the form given below.

DATE_PART ( datepart, {date|timestamp} )

The above syntax has 2 parameters:

  • Datepart: This is the interval/range we require from a certain date. The Redshift Datepart function returns the interval value as an integer.
  • Date/Timestamp: This could either simply be a date/time or an expression to pinpoint that date from which the datepart is retrieved. 

The table below provides the standard Redshift Datepart parameter intervals: 

Date Part or Time Part IntervalsAbbreviationsUsage and Integer Values
millennium, millenniamil, milsFull 1000 year periods starting from “0001-01-01 00:00:00 AD”
century, centuriesc, cent, centsFull 100 year periods
decade, decadesdec, decsFull 10 year periods
epochepochNumber of seconds from 00:00:00 UTC on 1 January 1970
year, yearsy, yr, yrs4 digit year
quarter, quartersqtr, qtrsQuarters of a year, from 1 to 4
month, monthsmon, monsMonths of an as 1 to 12
week, weekswThe week number in the year of the specific date. (1-53)
day of weekdayofweek, dow, dw, weekday Day of the week from 0 to 6, starting with Sunday.
day of yeardayofyear, doy, dy, yearday Day of the year(1-366)
day, daysdDay of the Week from 1 to 7  where Sunday is 1
hour, hoursh, hr, hrsHour of the day, from 0 to 23
minute, minutesm, min, minsMinute of the hour, from 0 to 59
second, secondss, sec, secsSecond of the minute, from 0 to 59
millisecond, millisecondsms, msec, msecs, msecond, mseconds, millisec, millisecs, millisecondSecond of a minute with 3 decimal place accuracy multiplied by 1000 (0 to 59999)
microsecond, microsecondsmicrosec, microsecs, microsecond, usecond, useconds, us, usec, usecsSecond of a minute with 6 decimal place accuracy multiplied by 1000000 (0 to 59999999)

The above table can be used as a reference when executing the Redshift Datepart command.

2) Redshift Datepart Examples

Let’s understand this function from the following examples:

A) Extract the century from a literal date value


(1 row)

Note: the default name “pgdate_part” will be shown when the column is unnamed.

B) Extract a quarter from the date


(1 row)

C) Extract the day of the week and the day of the year

SELECT date_part('dow',TIMESTAMP '2021-03-19 10:20:30') dow,date_part('doy',TIMESTAMP '2021-03-19 10:20:30') doy;


dow | doy
 7  | 78
(1 row)

D) Extract hour, minute, and seconds from the timestamp

SELECT date_part( hour,TIMESTAMP '2021-06-20 09:11:44') h,
date_part( minute ,TIMESTAMP '2021-06-20 09:11:44') m,
date_part( second ,TIMESTAMP '2021-06-20 09:11:44') s;


h   | m | s
 09 | 11 | 44
(1 row)

D) Using Where Clause

SELECT date_part(dow, arrivaltime) as dow,
arrivaltime from event
where date_part(dow, arrivaltime)=5
order by 2,1;


dow | arrivaltime
   5 | 2021-01-01 13:00:00
   5 | 2021-01-01 13:00:00   
   5 | 2021-01-01 13:00:00
   5 | 2021-01-01 13:00:00


In this article, you have learned to employ the Redshift Datepart command to query the desired results based on the specific part of date and time. The Redshift Datepart function provides smart parameters such as “quarter”, “day of week”,” epoch”, etc. which provides direct answers that otherwise require time-consuming manual calculation. With the easy syntax and the list of parameters, you are now equipped to comfortably handle Date and Time data using the Redshift Datepart command. 

When a company starts growing, data is generated at an astonishing rate across all the SaaS applications of your firm. To address the growing storage and computation requirements of this data, you would be required to invest a section of your Engineering Bandwidth to integrate the data from all your sources and store it in a Cloud Data Warehouse such as Amazon Redshift for further business analytics. All of these challenges can be comfortably handled by a Cloud-Based ETL Tool like Hevo Data.

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Tell us about your experience of working with the Redshift Datepart command! Share your thoughts with us in the comments section below.

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