![]() So first, how do we get a TIMESTAMPZ value?Įither we populate a TIMESTAMPZ column with a string literal containing the time zone (e.g., ' 13:00:00 IST'), or we use the TIMEZONE function to create a TIMESTAMPZ value from a timestamp. If the time zone is not specified in a TIMESTAMPZ type, it's assumed to be UTC. The TIMESTAMPZ data type captures the time zone along with the timestamp. Thus Japan and England are in different time zones. of any given day can be different in different locations around the world we have time zones to capture this info.įor example, when it's sunrise in Japan, England is still in the dark, waiting for the earth's rotation to bring it to face the sun. We saw that the type TIMESTAMPZ above that can hold time zone info, but what is that, and why is it needed?īecause of the earth's rotation, X a.m./p.m. SYSDATE and GETDATE can also be used for the current timestamp, with the caveat that SYSDATE returns the timestamp for the current transaction. Note how the "now" column has both the date and time. ![]() We used TO_DATE to create a date from a string, and we used date arithmetic to get tomorrow's date from today. We've used Redshift's built-in functions to get today's date and time in the example above. Select current_date today, current_timestamp now, current_time time_now, to_date( '28-10-2010', 'DD-MM-YYYY') Using a function to create a datetime from some other input.Selecting a datetime column in the query.No, we can get datetime results in two ways: But how do we get the results of those types of queries? Does it always have to come from data stored in columns? Now we know the different data types you can get for time series. TIMESTAMPZ: Timestamp along with the time zone infoįor more information, check out the AWS documentation.TIMESTAMP: Includes both the date and time.TIMEZ: Time with time zone info (since time in New York is behind that in Tokyo, for instance, time zones reflect this difference).Like most other relational databases such as MySQL, SQL Server, Oracle, DB2, etc., Redshift can handle and manipulate time series data.įirstly, let's look at which data types for time series are supported by Redshift and what kind of data they can hold. Redshift is a database provided as a part of AWS (Amazon Web Services), based on the Postgres database, but with some differences. Time series data holds the date and/or time information and is different from string or numeric data since it has components like day, month, year, hours, etc., that we might want to access/compare/modify.įor example, we might want sales in the month of May or to find the difference in two dates as the number of months. This article will cover how to work with time series/datetime data in Redshift.
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