KSnow: Load continuous data into Snowflake using Snowpipe

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In this blog, we will discuss loading streaming data into Snowflake table using Snowpipe. But before that, if you haven’t read the previous part of this blog i.e., Loading Bulk Data into Snowflake then I would suggest you go through it. As now we have been set so let’s get started and see what Snowpipe is all about.


Snowpipe is a mechanism provided by Snowflake to load high frequency or streaming data. Snowpipe provides us with the capability to load the data as soon as it becomes available in a defined stage. Therefore, achieving a near real-time or micro-batch load of data.

Use cases of steaming or near real-time data may be transactions arriving from core banking systems or event generated by IoT devices.

Snowpipe uses serverless architecture, so when you define a new Snowpipe it will not use our virtual warehouse resource. But, it will use its own resources, scaling up and down will be managed by Snowflake itself.

Snowpipe definition contains a COPY command through which Snowflake knows what data to load and which target table to load it to. We can say that Snowpipe is a wrapper around the COPY command.

Snowpipe loads data in response to new file notification events. These notification events are usually configured in cloud platforms. Or we can trigger a Snowpipe manually through custom programs calling the REST APIs.

Loading Data via Snowpipe

There are 4 high level steps in loading streaming data using Snowpipe:

1. Stage the Data: We would need to define a stage which could be a S3 bucket or Azure Blob where our streaming data will continuously arrive.

Note: As of date, Snowpipe doesn’t supports loading continuous data from Google Cloud Bucket.

2. Test the COPY command: Next step would be to create a target table and test our COPY command on sample data. We need to make sure that our COPY command runs successfully and correctly before we use it in our Snowpipe definition.

3. Create the Pipe: We will create a Pipe and we will provide the COPY command (that we have created for testing) in the Pipe definition. Here we are defining a Pipe and letting Snowflake know that when the Pipe executes it will load the data using the specified COPY command.

4. Configure Cloud Event: There are two ways to trigger the Pipe to load the data on a continuous basis.
i. Configure event through Cloud platform: This would trigger the Pipe whenever a new file arrives. When the event is triggered it will also trigger the associated Pipe which will then load the data into the target table. This is a preferable option since it requires less effort and is more robust compared to the alternate.

ii. Trigger the Snowpipe using REST APIs: We would write a custom program which we can execute whenever we want Snowpipe to perform the load.

The Process

a. We will have files arriving at an External Staging area like Amazon S3 or Azure Blob Storage.

b. We define a Snowpipe so that it will load files from the stage into the target Snowflake table.

c. Option 1: In order to trigger the Pipe, we make use of Cloud Notification Services (specific to the Cloud platform we are using)

Option 2: The trigger would be through a custom program invoking the REST APIs.

Fig: Trigger Snowpipe through Cloud Notification Service (Overview)

Fig: Trigger Snowpipe through Cloud Notification Service (Detailed)

Fig: Trigger Snowpipe through REST API


Let’s look into the code (Assuming you are working with AWS)


USE DATABASE Transaction_DB;

CREATE SCHEMA file_formats;

CREATE SCHEMA external_stages;

CREATE SCHEMA snowpipes;

-- AWS S3 Configuration (ACCOUNTADMIN has the privilege)
STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::111222333444:role/snowflake_role'
STORAGE_ALLOWED_LOCATIONS = ('s3://snowflake-private/knoldus/load_data/');

-- create a file format describing the continuous data files
CREATE OR REPLACE FILE FORMAT file_formats.snowpipe_csv_format

-- create an external stage using an S3 bucket
CREATE OR REPLACE STAGE external_stages.transaction_events STORAGE_INTEGRATION = s3_int
URL ='s3://snowflake-private/knoldus/load_data/' 
FILE_FORMAT = file_formats.snowpipe_csv_format;

-- list the files already present in the bucket
LIST @external_stages.transaction_events;

CREATE TRANSIENT TABLE public.transactionDetails (       
   Transaction_Date DATE,
   Customer_ID NUMBER,
   Transaction_ID NUMBER,
   Amount NUMBER

DESC TABLE public.transactionDetails;

Testing the COPY command on sample data.

-- test COPY command
COPY INTO public.transactionDetails FROM @external_stages.transaction_events FILE_FORMAT = file_formats.snowpipe_csv_format

-- check for data in the table
SELECT COUNT(*) FROM public.transactionDetails;

TRUNCATE TABLE public.transactionDetails;

Create/Define the Snowpipe

-- create the pipe
CREATE OR REPLACE PIPE snowpipes.transaction_pipe
COPY INTO public.transactionDetails FROM @external_stages.transaction_events
FILE_FORMAT = file_formats.snowpipe_csv_format

SELECT COUNT(*) FROM public.transactionDetails;


Here, the Pipe definition is created, this will not result in automatically loading of any data into the target table until an event has been defined in the cloud storage.

Thus, if you run SELECT COUNT(*) FROM public.transactionDetails; this will result in 0 (zero) rows.

Run the command SHOW PIPES; to the get ARN for the Notification Channel of the Pipe that we need to use and copy the ARN.

Set up S3 event notification:

Go to the Bucket > Click on Properties > Clink on Events > Add notification

Configure the event notification as per need and finally select SQS Queue as the Notification Destination. Finally, add the ARN that we have copied by running the SHOW PIPES; command. And click on Save button.

You can learn about SQS from my blog Getting Started with Amazon SQS.

Fig: Configuration of Notification Service

Finally, you can load a new file in your specified path in the S3 bucket and the data will get loaded into the Snowflake table. (This takes few seconds to load)

You can validate by checking the row count of the table.

--After loading new data to S3

SELECT COUNT(*) FROM public.transactionDetails;

TRUNCATE TABLE public.transactionDetails;

Hope you enjoyed the blog!! Stay connected for more future blogs. Thank you!! 🙂


Written by 

Sarfaraz Hussain is a Big Data fan working as a Senior Software Consultant (Big Data) with an experience of 2+ years. He is working in technologies like Spark, Scala, Java, Hive & Sqoop and has completed his Master of Engineering with specialization in Big Data & Analytics. He loves to teach and is a huge fitness freak and loves to hit the gym when he's not coding.