Snowflake Snowpipe: A Comprehensive Guide
The demand for real-time data analytics has been growing over years. Having real-time insights helps business in taking prompt and valuable decisions. This blog has been designed to discuss major concepts of Snowflake Snowpipe, how it works, advantages, etc. Let’s get into the details part.
What is Snowflake Snowpipe?
In simple words, Snowflake Snowpipe is a COPY command residing on top of a cloud storage location. Snowpipe streamlines the process to load data from files as soon as they reach the staging area. It allows you to load data in micro-batches and makes data available to users within a fraction of minutes. To ensure only new data gets processed, Snowpipe uses a combination of filename and file checksum.
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How does Snowflake Snowpipe work?
The biggest advantages of using Snowpipe is that it eliminates the roadblocks and streamlines the process to build near real-time analytics. Following are the three major operations that need to be executed to generate real-time data sets:
- The data needs to be extracted to a targeted location in real-time
- The real-time data should be distributed into a system in which there is no scope for data que or waiting to process.
- The system should insert data records into a warehouse.
Snowpipe automates the last two (2nd & 3rd) tasks and makes the process easy to set up. If you wish to deliver near-real-time datasets, the only thing you are required to do is build a process that brings the data to cloud storage and the rest is performed by Snowpipe.
Difference between Snowpipe and Bulk data loading
Here we are going to discuss major differences between Snowpipe and a data load workflow using the COPY command.
1) Authentication:
Bulk data load: To authenticate and initiate a user session bulk data authentication depends on the security option supported by the client.
Snowpipe: to call the REST endpoints, Snowpipe demands key pair authentication with JSON web Token (JWT).
2) Load History:
Bulk data load: The load history is stored in the metadata of the target table for a period of 64 days.
Snowpipe: The history stored for 14 days in the metadata of the pipe. You can request the history from a REST endpoint using the ACCOUNT_USAGE view or SQL table function.
3) Transactions:
Bulk data load: Here the entire data is executed in a single transaction.
Snowpipe: The loading process completely depends on the number and size of the rows in each data file. Rows related to partially loaded files can be combined or divided into single or multiple transactions.
4) Compute Resources:
Bulk data load: Here a user has to specify the warehouse to execute the COPY command.
Snowpipe: This makes use of the resources provided by Snowflake.
5) Cost:
Bulk data load: The bill will be generated based on the amount of time each virtual warehouse is active.
Snowpipe: billed based on the compute resources utilized in the Snowpipe warehouse while loading data.
Advantages of Snowpipe
Snowpipe eliminates the challenges associated with legacy systems and provides the following benefits to the organizations.
1) Real-time insights: Snowpipe continuously provides fresh business data across all the departments without creating any workload issues.
2) Minimizes cost: It is very economical and charges customers per second based on the computation time.
3) Ease-of-use: It is very simple to use. All you need to do is connect it with the S3 bucket and data will load automatically.
4) Flexibility: Snowpipe is highly flexible and allows easy customizations to load data using programmatic REST API, using Python, and Python SDKs.
5) Zero Management: Snowpipe automatically scales up and scales down based on the data loading requirements. There is no need to manage anything.
Closing Thoughts
Snowpipe is one of the core components of Snowflake and presents business with fresh real-time insights to make data-driven decisions. Hope you have found useful insights through this Snowpipe blog. You can also check out our frequently asked Snowflake interview questions and Snowflake tutorial here.
Author Bio
Yamuna Karumuri is a content writer at CourseDrill. Her passion lies in writing articles on the IT platforms including Machine learning, Workday, Sailpoint, Data Science, Artificial Intelligence, Selenium, MSBI, and so on. You can connect with her via LinkedIn.
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