The ever-increasing demand for Efficient Data Handling & Processing has reached a new peak. Owing to the limitations of On-premise Data Storage and Analytics tools, businesses are increasingly turning to Cloud Solutions like Snowflake. Snowflake is a Cloud Data Warehousing and Analytics Platform that enables independent instant scaling of Storage and Computational resources. Snowflake, which supports Standard SQL, enables you to gain access to your data and perform high-speed analysis.
Snowflake Temporary Tables, in addition to Permanent Tables, which is the default table type, are especially useful for storing data that does not need to be kept for long periods of time (i.e. Transitory Data).
In this article, we will look at how to create a Snowflake Temporary Table, the syntax, usage, and limitations, as well as some examples!
Table of Contents
What is Snowflake?
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Snowflake is a Fully-managed Data Warehousing service that allows clients to connect, load, analyze, and securely share their data while supporting near-infinite concurrent operations scalability. Data Lakes, Data Engineering, Data Application Development, Data Science, and Safe Data Consumption are some of the most common use cases of Snowflake.
Snowflake’s Architecture is unique as it naturally separates Computation and Storage. This method provides Users and Data Workloads virtual access to a single copy of the data without sacrificing speed. Snowflake enables you to execute your data solution across multiple locations and Clouds for a consistent user experience.
The Virtual Warehouse concept from Snowflake applies to Amazon EC2 Clusters that provide immense Query Processing power. Each virtual Warehouse includes an MPP EC2 Compute Cluster with many nodes for a quick data analysis.
The Snowflake Data Marketplace, which connects you with thousands of other Snowflake users, also gives you access to shared datasets and data services. For more information, go to the official website.
Key Features of Snowflake
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The following are some of the benefits of using Snowflake as a Software as a Service (SaaS) solution:
- By switching from nightly Batch Loads to Real-time Data Streams in your Analytics Pipeline, Snowflake allows you to improve the quality and speed of your analytics. Enabling secure, concurrent, and monitoring access to your Data Warehouse across your organization can help you improve the quality of analytics at your company.
- Snowflake employs the Caching Paradigm to deliver cache results quickly. To avoid regenerating the report when nothing has changed, Snowflake uses Persistent (during the session) Query results.
- Snowflake helps you break down Silos and ensure that everyone in your organization has access to relevant data, resulting in better data-driven decision-making.
- Snowflake can help you better understand customer behaviour and product usage. You can also use the entire scope of data to ensure customer satisfaction, dramatically improve product offerings, and drive Data Science Innovation.
- Snowflake allows you to build your own Data Exchange, which allows you to securely share live, regulated data. It also encourages you to strengthen data connections across your business units, as well as with your partners and customers.
A fully managed No-code Data Pipeline platform like Hevo Data helps you integrate data from 100+ Data Sources (including 40+ Free Data Sources) to a destination of your choice such as Snowflake in real-time in an effortless manner. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources provides users with the flexibility to bring in data of different kinds, in a smooth fashion without having to code a single line.
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What is a Snowflake Temporary Table?
Snowflake’s Temporary Table is only visible during the current session. Snowflake Temporary tables exist only during the session in which they were created and are only valid for the duration of that session. When a session ends, the system deletes the data stored in a Temporary Table, which is not recoverable.
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Syntax for Snowflake Temporary Tables
Simply include the TEMPORARY keyword (or TEMP Abbreviation) in your CREATE TABLE DDL command to create a Temporary Table.
create temporary table temptablename (col1 type1, col2 type2, ... coln typen,);
For Example, you may use the following SQL Query to create a Temporary Table by the name of Demo in Snowflake:
create TEMPORARY table DEMO (col1 int, col2 int);
+----------------------------------+
| status |
|----------------------------------|
| Table DEMO successfully created. |
+----------------------------------+
How to Make a Snowflake Temporary Table with Identical Permanent Table Name?
Temporary Tables, like other table types, are associated with a Specific Database and Schema. They are not, however, bound by the same Unique Name requirements because they are session-based. This means that within the same Schema, you can have both Temporary and Non-temporary Tables with the same name.
As an example,
--Permanent Table
select * from DEMO;
+------+--+
| A | B |
|------+--|
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+------+--+
-- Temporary Table
create TEMPORARY table DEMO (A int, B int);
+----------------------------------+
| status |
|----------------------------------|
| Table DEMO successfully created. |
+----------------------------------+
However, keep in mind that the Temporary Table takes precedence over any other table, that is, with the same name in the same schema during the session. When you create a Temporary Table with the same name as a Permanent Table, the system hides the Permanent Table.
What are the Restrictions of Snowflake Temporary Tables?
The following are some of the Snowflake Temporary Table restrictions:
- Other users or sessions cannot see Temporary Tables.
- Some standard features, such as Cloning, are not supported by Temporary Tables.
- Data cannot be recovered. At the end of the session, the system will delete the Temporary Table data. The data cannot be recovered by Snowflake as well.
- There is no Fail-safe Period for Snowflake Temporary Tables.
What is a Snowflake Transient Table?
Snowflake Transitory Tables are accessible to all users who have the appropriate credentials until they are explicitly abandoned. The Transient Tables are used to store temporary data that must be retained after the current session has ended.
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Because they lack a fail-safe period, transient tables are a useful alternative for managing the cost of very large tables used to hold transitory data. However, once the Time Travel Retention Period has expired, neither you nor Snowflake will be able to retrieve the data.
For example, if a system failure occurs and a Transitory Table is dropped or lost, you or Snowflake will no longer be able to access the data after one day. As a result, it’s best to use Snowflake Transient Table only for data that doesn’t need to be protected against failure or can be recreated outside of Snowflake.
If you want to know more about the Snowflake Transitory Tables, you can visit them here.
Conclusion
You should now be familiar with Snowflake Temporary Table. These articles are intended to help you learn more about Snowflake. You’ll have a better chance of getting the most out of your Snowflake Data if you remember this information!
While Snowflake Services are useful, maintaining the proper environment on a consistent basis is a difficult task. Furthermore, extracting data from various sources and integrating it into your Data Warehouse such as Snowflake can also be a difficult task. This is where Hevo Data comes to your aid to make things easier! Hevo Data is a No-code Data Pipeline and has awesome 100+ pre-built Integrations that you can choose from.
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Hevo can help you integrate your data from numerous sources and load them into destinations like Snowflake to analyze real-time data with BI tools of your choice. It will make your life easier and Data Migration hassle-free. It is user-friendly, reliable, and secure.
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Share your experience of learning about Snowflake Temporary Tables in the comments section below. We would love to hear more from you!
Davor is a data analyst at heart with a passion for data, software architecture, and writing technical content. He has experience writing more than 100 articles on data integration and infrastructure.