Understanding Amazon Redshift Timestamps: 5 Comprehensive Types

Enterprises need to analyze their data to make data-driven decisions. A huge volume of data flowing in real-time needs to be handled and Data Analysts need to perform queries as quickly as possible. To make it happen companies ensure their data to be present in query optimizable form. To achieve this, they use Data Warehouses as they are known for quickly processing any query. Data Warehouses gather data from multiple data sources into a uniform schema to run complex queries faster than traditional Databases. Different enterprises use data to generate reports and dashboards to gain valuable insights from their customers, run Data Analysis, and handle many other activities.

Companies use multiple platforms, apps, and services to accomplish their daily tasks. The data stored in multiple platforms have a different schema. One of the common issues developers face is handling DateTime data from multiple sources because this data is available in distinct formats. DateTime is an essential piece of data as it serves as a primary point to keep track of multiple activities. Data Warehouses such as Amazon Redshift, Google BigQuery, etc., manage DateTime data in different ways. Amazon Redshift Timestamps data type holds value in several formats based on the class of data. 

Applications process DateTime data in different formats based on their usage. Like some websites only needs days, some apps track time to microseconds precision. Amazon Redshift Timestamps can handle all these data formats effortlessly. In this article, you will learn about different Amazon Redshift Timestamps data types and their usage. Also, you will understand why it’s an essential part of Amazon Redshift and how it comes in handy in handling all the requirements.

Table of Contents

• Introduction to Amazon Redshift
  • Key Features of Amazon Redshift
• Understanding Amazon Redshift Timestamp Literals
• Amazon Redshift Datetime Data Types
  • DATE
  • TIME
  • TIMETZ
  • TIMESTAMP
  • TIMESTAMPTZ
• Need For Amazon Redshift Timestamps
• Conclusion

Introduction to Amazon Redshift

Amazon Redshift is a petabyte-scale Data Warehouse developed by AWS (Amazon Web Services). It allows enterprises to store and analyze their data. It also lets you start from hundreds of gigabytes of data and scale up to petabytes of storage. Amazon Redshift can deliver fast query processing and high-class performance due to its Massively Parallel Processing (MPP) and columnar data structure architectures. Parallel processing in Amazon Redshift is possible because it stores data in multiple parallel clusters and has several nodes working simultaneously.

Key Features of Amazon Redshift

Amazon Redshift enables companies to analyze and store their data in a safer place at less cost than on-premise data storage solutions. It offers many more features that attract enterprises to prefer Redshift over Data Warehouse services. A few key features are listed below:

• Automated Backups: Amazon Redshift offers automatic backup services to Amazon S3. One can back up their data to multiple S3 located in different regions for disaster recovery.
• Concurrency: Amazon Redshift allows users to run thousands of concurrent queries without affecting the performance. The computation capacity is increased automatically as the workload increases.
• End-to-End Encryption: To deliver high-class security, one can easily set up SSL for data transfer and hardware-accelerated AES 256 Encryption for hardware at rest. By default, the encryption keys are managed by Amazon Redshift.
• Massively Parallel Processing: Amazon Redshift delivers fast query performance by running multiple queries in different clusters and distributes workload on other processors. The MPP makes it easier to complete tasks within minutes.
• Redshift ML: It makes it easier for Data Analysts to create, train and deploy Amazon SageMaker models. Developers can use SQL commands to use create models and train them with data in Amazon Redshift.

To learn more about Amazon Redshift, click here.

Understanding Amazon Redshift Timestamps Literals

Before jumping to different Amazon Redshift Timestamps data types, you need to understand a few common literals used in Amazon Redshift Timestamps. Literals are the rules which define the properties of terms Amazon Redshift Timestamps In this section, you will read about the defined rules of literals commonly used. A few of them are listed below:

• Dates
• Times
• Timestamp

1) Dates

The dates in Amazon Redshift Timestamps follow a default style mode of “MDY” (Month-Day-Year) DateStyle mode. Dates simply contain information about months, dates, and years. It can be all digits or can have dates and years as digits and months in strings. Amazon Redshift Timestamps has the month value precedes the date value as string or digits. The following table with a few examples of different formats of dates in Amazon Redshift Timestamps supports, shown below:

Input Date	Full Date
2020-01-08	January 8, 2020
January 8, 2020	January 8, 2020
1/8/2020	January 8, 2020
01/02/00	January 2, 2021
2021-Jan-31	January 31, 2021
Jan-31-2021	January 31, 2021
31-Jan-2021	January 31, 2021
20200215	February 15, 2020
080215	February 15, 2020
2020.366	December 31, 2020 (here the three-digit after year are the number of days of the year from 001 as 1 Jan to 365-365 as 31 Dec)

2) Times

The Amazon Redshift Timestamps follows times values in hours, minutes, and seconds. The following examples of Times for Amazon Redshift are shown below:

Input times	Description (of time part)
04:05:06.789	4:05 AM and 6.789 seconds
04:05:06	4:05 AM and 6 seconds
04:05	4:05 AM exactly
040506	4:05 AM and 6 seconds
04:05 AM	4:05 AM exactly; AM is optional
04:05 PM	4:05 PM exactly; the hour value must be less than 12
16:05	4:05 PM exactly

3) Timestamps

The Timestamp is the collection of both date and time. Amazon Redshift Timestamps follows input as date value preceding the time values. Here, the date values follow the format as “YMD” (Year-Month-Date). The following examples of Amazon Redshift Timestamps are listed below:

Input timestamps (concatenated dates and times)	Description (of time part)
20200215 04:05:06.789	4:05 AM and 6.789 seconds
20200215 04:05:06	4:05 AM and 6 seconds
20200215 04:05	4:05 AM exactly
20200215 040506	4:05 AM and 6 seconds
20200215 04:05 AM	4:05 AM exactly; AM is optional
20200215 04:05 PM	4:05 PM exactly; the hour value must be less than 12
20200215 16:05	4:05 PM exactly
20200215	Midnight (by default)

Amazon Redshift Timestamps Types

An Amazon Redshift Timestamps comes with 5 Data Types to handle all kinds of data imports in Data Warehouse. In this section, you will understand each Amazon Redshift Timestamps datatype in detail. The DateTime data types are listed below:

• DATE
• TIME
• TIMETZ
• TIMESTAMP
• TIMESTAMPTZ

1) DATE

The DATE data type allows one to store simple calendar dates containing year, month, and date as its values. It is the simplest Amazon Redshift Timestamps data type. DATE takes 4 bytes of storage and has a range from 4713 BC to 294276 AD. Moreover, if you try to insert a timestamp value (date with time) in a DATE column, only date values will get loaded, and the remaining time value will get ignored. Given below are some commands to illustrate the DATE Data Type:

create table datetable (start_date date, end_date date);
insert into datetable values ('2020-06-01','2020-12-31');

2) TIME

The TIME is an Amazon Redshift Timestamps data type that stores time value without a time zone. One can use this Amazon Redshift Timestamps data type to store time values with up to six digits of precision values for fractional seconds. But for convenience, TIME values associates time data with UTC (Coordinated Universal Time) in Amazon Redshift. It avoids any error in querying TIME data with a time zone data query. Given below are some commands to illustrate the TIME Data Type:

create table datetable (init_time time);
insert into datetable values ('04:05:06');

3) TIMETZ

The TIMETZ is similar to the TIME data type of Amazon Redshift Timestamps. The only difference is, it stores TIME data with the time zone. It can also store time values with up to six digits of precision values for fractional seconds plus the timezone provided. It occupies 8 bytes of storage and ranges from 00:00:00+1459 to 00:00:00+1459 with a resolution of 1 microsecond. Given below are some commands to illustrate the TIMETZ Data Type:

create table telecasttime(broadcastTime timetz); 
insert into telecasttime values('09:59:59 EST');

4) TIMESTAMP

Amazon Redshift Timestamps comes with another data type that can store both date and time to avoid adding extra calculations in queries. The TIMESTAMP data type supports timestamp without time zone that means it contains date and time values without a time zone. It follows similar properties of TIME data type. TIMESTAMP occupies 8 bytes of storage and ranges from 4713 BC to 294276 AD. 

If you try to insert only date or date with incomplete time value (like missing seconds or microseconds) into the TIMESTAMP column, it will fill 00 as default values in all missing places in time values. It takes all the values as string, and if the input contains a time zone value, the TIMESTAMP ignores the input value. Given below are some commands to illustrate the TIMESTAMP Data Type:

create table tstamp(timeofday timestamp); 
insert into tstamp values('Sept 1,2021 09:03:37'); 
insert into tstamp values('Sept 1,2021 09:04:59 EST');

timeofday 
--------------------- 
2021-09-01 09:03:37

5) TIMESTAMPTZ

The TIMESTAMPTZ is another Amazon Redshift Timestamps DateTime data type similar to TIMESTAMP. Additionally, it supports time zone in its values, and all the properties of TIMESTAMP apply to TIMESTAMPTZ. The default time zone value it follows is UTC. Also, the time zone values in the input string are converted to UTC, and then it stores those values. Given below are some commands to illustrate the TIMESTAMPTZ Data Type:

create table tstamptz(timeofday timestamptz); 
insert into tstamptz values('Sept 1,2021 09:10:41'); 
insert into tstamptz values('Sept 1,2021 EST');

timeofday 
--------------------- 
2021-09-01 09:10:41 UTC
2021-09-01 00:00:00 UTC

To get a list of all supported time zones names, run the following command given below:

select pg_timezone_names();

Need For Amazon Redshift Timestamps 

Data can be in any format, whether it’s a string or numerical. They all require some data type to handle it. The simplest form of any Data Type one can consider is string or digits. So why one needs any other data type like Amazon Redshift Timestamps DateTime. If one needs to store the DateTime values, they can be stored as strings. 

DateTime values contain lots of information such as year, month, seconds, minutes, etc., and storing them as a string will block the flexibility to access them. BI tools connected to Amazon Redshift need DateTime data in different formats, like taking reference from the same DateTime data in different intervals (day or hour). It increased the flexibility for users to manipulate and understand data more effectively.

Each DateTime data need to be defined in its valid and efficient format. If there is only one data type to handle DateTime data, it becomes time-consuming and inefficient to manipulate the date and time for every query. That is why Amazon Redshift Timestamps offers different data types to handle date and time in every format, like a date with timezone and only date with no timezone are different data types. 

Conclusion

In this article, you learned about various Amazon Redshift Timestamps Data types and their uses. You also read about the need for Amazon Redshift Timestamps data types for better query performance and less manipulation. It allows developers to effortlessly load data to Amazon Redshift and Data Analysts to perform faster queries. Amazon Redshift Timestamps avoid extra manipulation of DateTime data from the string and makes it easier to get any part of date and time with its label (Hour, Minute, Month, etc.).

Visit our Website to Explore Hevo

Companies spend significant time loading data from multiple data sources to a Data Warehouse because every source needs a different script to perform ETL (Extract Transform Load) process. Hevo Data is a No-code Data pipeline solution that can help you transfer data from 100+ sources to Amazon Redshift or other Data Warehouse of your choice. Its fault-tolerant and user-friendly architecture fully automates the process of loading and transforming data to destination without writing a single line of code. 

Want to take Hevo for a spin? Sign Up here for a 14-day free trial and experience the feature-rich Hevo suite first hand.

Share your experience of learning about Amazon Redshift Timestamps in the comments section below!