Building a No-Code ETL Solution: Incremental Data Loading from AWS S3 to Snowflake using Snowpipe
Dipti Pasupalak
Data Architecture | Data Engineering | Data warehouse | Data Lake | Lake house | Generative AI | Machine Learning | Analytics | Database Pro | Product | Consulting | AWS | Azure | Databricks | Snowflake | Blogging
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:
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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');
CREATE PIPE t1_pipe AUTO_INGEST=true AS COPY INTO T1_DEMO FROM @test_stage FILE_FORMAT = (TYPE='csv');
Conclusion:
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3 个月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
Sr Software Engineer at Eagle Investment Systems (BNY Mellon ) Banking and Finance domain.
1 年Awesome. Bhai teach me snowflake.