Building a No-Code ETL Solution: Incremental Data Loading from AWS S3 to Snowflake using Snowpipe

Building a No-Code ETL Solution: Incremental Data Loading from AWS S3 to Snowflake using Snowpipe

#snowflake #etl #nocode #aws #s3

Introduction:

In this article, we'll explore a simple and practical approach to move incremental data from AWS S3 to an existing Snowflake table seamlessly, without writing a single line of code. This method, known as No-Code ETL, allows us to automate the data loading process efficiently.

Scenario:

Let's consider a hypothetical scenario where we have an input file in CSV format named "T1_DDMMYYYYHH24MISS.csv." This file contains two columns, col1 and col2, and it gets generated on an hourly basis throughout the day. Our objective is to store this incremental data automatically into an existing Snowflake table called "T1_DEMO" with columns col1 and col2. The data should be loaded every hour without requiring any manual intervention.

Setup and Resources:

To achieve this, we'll need the following resources:

  • Amazon S3: We'll use S3 as a storage location to hold all the incremental files generated periodically. Let's create a new S3 bucket called "data" and a subfolder named "data/t1_demo."
  • Bucket Name: data
  • Sub Folder: data/t1_demo
  • Snowpipe: Snowpipe is Snowflake's continuous data ingestion service, which automatically loads data as soon as new files are added to a specified stage. We'll need to set up the following components in Snowflake:
  • Stage: This integration connects Snowflake to the S3 bucket and subfolder.

CREATE STAGE test_stage URL='s3://data/t1_demo' CREDENTIALS=(AWS_KEY_ID='your_key' AWS_SECRET_KEY='your_secret_key') FILE_FORMAT = (TYPE = 'CSV');

  • PIPE: The pipe will be responsible for copying data from the S3 stage into the Snowflake table.

CREATE PIPE t1_pipe AUTO_INGEST=true AS COPY INTO T1_DEMO FROM @test_stage FILE_FORMAT = (TYPE='csv');

  • Amazon S3 Event Notification: To trigger the data ingestion process automatically, we can enable event notification for the S3 bucket using services like Amazon SNS or SQS. This will send a notification to the Snowpipe whenever new data is added to the S3 bucket, and the pipe will handle loading the incremental data into the target table (T1_DEMO).

Conclusion:

  • By leveraging the power of AWS S3 and Snowflake's Snowpipe, we have successfully built a No-Code ETL solution for automatically loading incremental data into our Snowflake table. This setup ensures that new data is seamlessly ingested into our system without requiring any manual intervention. This scalable and efficient approach will save time and effort, allowing us to focus on deriving insights from the data rather than managing the data loading process.


Thank you for sharing this insightful article on Snowflake data ingestion for incremental data. Your explanation was very clear and helpful. I have a question: How can this process be implemented using Azure Data Lake Storage (ADLS)? Any guidance or resources you could provide would be greatly appreciated. Thanks again for the valuable information! Best regards, Rajesh Mechery

Manoj Padhi

Sr Software Engineer at Eagle Investment Systems (BNY Mellon ) Banking and Finance domain.

1 年

Awesome. Bhai teach me snowflake.

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