Snowflake Snowpipe

Over the years, there has been an increase in the demand for real-time data analytics. Having real-time insights allows businesses to make timely and valuable decisions. This blog is intended to discuss major concepts of Snowflake Snowpipe, such as how it works, advantages, and so on. Let's get into the specifics.

Snowflake Snowpipe - Table of Content

What is a snowflake snowpipe?

Snowpipe is essentially a COPY command that sits on top of a cloud storage location. Snowpipe speeds up the process of loading data from files as soon as they arrive at the staging area. It enables users to load data in micro-batches and make it accessible to users in seconds. Snowpipe employs a combination of filename and file checksum to ensure that only new data is processed.

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How does a snowpipe work?

The most significant benefit of using Snowpipe is that it removes roadblocks and streamlines the process of developing near real-time analytics. The three major operations that must be performed in order to generate real-time data sets are as follows:

  1. The data must be extracted in real-time and delivered to a specific location.
  2. The real-time data should be distributed into a system with no room for data queuing or waiting to be processed.
  3. The system should populate a warehouse with data records.

Snowpipe focuses on automating the last two (2nd & 3rd) tasks and simplifies the setup process. If you want to produce near-real-time datasets, all you have to do is build a procedure which moves the data to cloud storage, and Snowpipe will handle the rest.

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Distinction between Snowpipe and Bulk data loading:


In this section, we'll look at the key differences between Snowpipe and a data load workflow that uses the COPY command.

1) Authentication: Data Loading in Bulk: Bulk data authentication is used to authenticate and start a user session, and it is dependent on the security option supported by the client.

Snowpipe requires key pair authentication with a JSON web Token in order to call REST endpoints (JWT).

2)Load History:

Bulk data load: The load history is stored in the target table's metadata for 64 days.

Snowpipe: The pipe's metadata stores the pipe's history for 14 days. The ACCOUNT USAGE view or SQL table function can be used to request the history from a REST endpoint.

3) Business transactions:Bulk data load: In this case, the entire data set is processed in a single transaction.Snowpipe: The loading process is entirely determined by the number and size of rows in each data file. Rows from partially loaded files can be combined or separated into single or multiple transactions.

4)Computing Power:

Bulk data load: Here, the user must specify the warehouse to which the COPY command should be executed.

Snowpipe: This makes use of the Snowflake resources.

5) Cost of bulk data loading: The bill will be generated based on how long each virtual warehouse is operational. Snowpipe: charges are assessed based on the compute resources used in the Snowpipe warehouse while loading data.

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Advantages of snowpipe:

  • Snowpipe eradicates the difficulties associated with legacy devices and offers organisations with numerous advantages.
  • Real-time insights: Snowpipe constantly offers fresh business data across all departments while avoiding workload issues.
  • Reduces costs: It is very cost-effective and charges customers per second based on computing time.
  • Usability: It is incredibly simple to use. All you have to do is connect it to the S3 bucket, and the data will instantly load.
  • Snowpipe is extremely adaptable and enables simple customizations to load data using a programmatic REST API, Python, and Python SDKs.
  • Zero Management: Snowpipe scales up or down instantly derived from data that lead to accumulation. There isn't anything that needs to be managed. 

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Conclusion:

Snowpipe seems to be a central element of Snowflake that provides businesses with new real-time perspectives to assist them in making data-driven decisions. I am glad you enjoyed this Snowpipe blog to be informative.Had any doubts please drop your query in the comments section to get them clarified.

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Gayathri
Gayathri
Research Analyst
As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.