Snowflake Convert Timezone Command 101: Syntax, Usage and Examples Simplified

Time is one of the most important aspects for any kind of data. Whether you are working with data in real-time or not, it is important to know when a particular record was generated, modified, etc. To analyze your data efficiently, sometimes you need to convert Timestamps from one time zone to another and Snowflake Convert Timezone Command lets you do that.

Upon a complete walkthrough of this article, you will gain a holistic understanding of Snowflake along with the salient features that it offers. You will also learn about how to use the Snowflake Convert Timezone command along with some example queries.

Significance of Timezone Conversion

It especially has high importance during data analysis since data could be obtained from various regions. So, timezone conversion ensures that timestamps are interpreted in the correct way so that uniformity is obtained throughout datasets. Therefore, by converting all these timestamps into a common timezone, you are pretty much eliminating discrepancies and making your analysis stronger and accurate. This process enables the comparison of data in a more reliable manner. Uniformity helps ascertain that your insights would be found on the same data, irrespective of when or where it was gathered. Proper timezone conversion is important to derive actionable insights from global datasets.

Prerequisites

• An active Snowflake account.

What is Snowflake?

Snowflake is one of the most popular Cloud Data Warehouses that offers a plethora of features without compromising simplicity. It scales automatically, both up and down, to offer the best Performance-to-Cost ratio. The distinguishing feature of Snowflake is that it separates Compute from Storage. This is significant as almost every other database, including Amazon Redshift, combines the two, implying that you must consider the size for your highest workload and then incur the costs associated with it. 

Snowflake requires no hardware or software to be Chosen, Installed, Configured, or Managed, making it ideal for organizations that do not want to dedicate resources to the Setup, Maintenance, and Support of In-house Servers. It allows you to store all of your data in a centralized location and size your compute independently. For example, if you require real-time data loads for complex transformations but only have a few complex queries in your reporting, you can script a massive Snowflake Warehouse for the data load and then scale it back down after it’s finished – all in real-time. This will save you a significant amount of money without jeopardizing your solution goals.

Key Features of Snowflake

Some of the key features of Snowflake are as follows:

• Scalability: The Compute and Storage resources are separated in Snowflakes’ Multi-Cluster Shared Data Architecture. This strategy gives users the ability to scale up resources when large amounts of data are required to be loaded quickly and scale back down when the process is complete without disrupting any kind of operation.
• No Administration Required: It enables businesses to set up and manage a solution without requiring extensive involvement from Database Administrators or IT teams. It does not necessitate the installation of software or the commissioning of hardware.
• Security: Snowflake houses a wide range of security features, from how users access Snowflake to how the data is stored. To restrict access to your account, you can manage Network Policies by whitelisting IP addresses. Snowflake supports a variety of authentication methods, including two-factor authentication and SSO via Federated Authentication.
• Support for Semi-Structured Data: Snowflake’s architecture enables the storage of Structured and Semi-Structured data in the same location by utilizing the VARIANT schema on the Read data type. VARIANT can store both Structured and Semi-structured data. Once the data is loaded, Snowflake automatically parses it, Extracts the attributes out of it, and stores it in a Columnar Format.

How to Use the Snowflake Convert Timezone Command?

Snowflake Convert Timezone Command is used to convert a timestamp from one time zone to another. Let’s understand the Snowflake Convert Timezone command with its syntax in detail along with some examples.

Syntax for Snowflake Convert Timezone Command:

CONVERT_TIMEZONE( <source_tz> , <target_tz> , <source_timestamp_ntz> )

CONVERT_TIMEZONE( <target_tz> , <source_timestamp> )

The Snowflake Convert Timezone command consists of the following arguments:

• <source_tz> represents a string that specifies the time zone of the input timestamp.
• <target_tz> represents a string that specifies the desired timezone to which the input timestamp should be converted.
• For the 3 argument version <source_timestamp_ntz> is a string that specifies the timestamp to convert.
• For the 2 argument version <source_timestamp> is a string that specifies the timestamp to convert.

Usage Notes for using Snowflake Convert Timezone Command

There are some key pointers that you need to take into account while working with the Snowflake Convert Timezone Commands. They are as follows:

• The “wallclock” time in the result is the same as the input “wallclock” time in the input time zone but in the Destination time zone.
• If you are using the 3 argument version, the return value of the Snowflake Convert Timezone command is always of type TIMESTAMP_NTZ.

• The time zone is assumed to be included in the <source_timestamp> argument. If the value is of the type TIMESTAMP_TZ, the time zone is derived from it. Otherwise, the time zone of the current session is used.
• If you are using the 3 argument version, the return value of the Snowflake Convert Timezone command is always of type TIMESTAMP_TZ.
• Snowflake supports iana.org time zones such as America/Los Angeles, Europe/London, UTC, ETC/GMT, and so on while certain simple time zones, such as PDT, are currently not supported.
• The names of time zones are Case Sensitive and must be enclosed in single quotes.

Example Queries

Let’s observe the following use cases and the outputs to understand the working of the Snowflake Convert Timezone command:

1) If you want to convert a “wallclock” time in Los Angeles to the matching “wallclock” time in  New York. The query would look something like this:

select convert_timezone('America/Los_Angeles', 'America/New_York', '2022-01-05 13:00:00'::timestamp_ntz) as ans;

Output:

+-------------------------+
| ans                   |
|-------------------------|
| 2022-01-05 16:00:00.000 |
+-------------------------+

2) If you want to Convert a “wallclock” time in Warsaw to the matching “wallclock” time in UTC. The query would look something like this:

select convert_timezone('Europe/Warsaw', 'UTC', '2022-01-05 00:00:00'::timestamp_ntz) as ans;

Output:

+-------------------------+
| ans                   |
|-------------------------|
| 2022-01-05 23:00:00.000 |
+-------------------------+

Conclusion

In this article, you have learned how to effectively use the Snowflake Convert Timezone command to perform the time zone conversions. As your business begins to grow, data is generated at an exponential rate across all of your company’s SaaS applications, Databases, and other sources. To meet this growing storage and computing needs of data,  you would require to invest a portion of your engineering bandwidth to Integrate data from all sources, Clean & Transform it, and finally load it to a Cloud Data Warehouse such as Snowflake for further Business Analytics. All of these challenges can be efficiently handled by a Cloud-Based ETL tool such as HevoData.

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations such as Snowflake, with a few clicks.

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Tell us about your experience of working with the Snowflake Convert Timezone command! Share your thoughts with us in the comments section below.

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