Snowpipe and Handling Schema Evolution without Re-ingesting the Data from an External Table

Are you tired of dealing with the hassle of re-ingesting data from an external table every time your schema changes? Do you wish there was a more efficient way to handle schema evolution in Snowflake? Look no further! In this article, we’ll explore the magic of Snowpipe and how it can help you handle schema changes without re-ingesting data from an external table.

What is Snowpipe?

Snowpipe is a cloud-based data ingestion service offered by Snowflake that allows you to load data from external sources, such as S3 buckets, Azure Blobs, or Google Cloud Storage, into your Snowflake account. It’s a game-changer for data teams, as it enables fast, secure, and scalable data ingestion.

Key Benefits of Snowpipe

• Faster Data Ingestion: Snowpipe can ingest data at incredible speeds, making it ideal for real-time data analytics and reporting.
• Scalability: Snowpipe can handle large volumes of data and scale according to your needs.
• Security: Snowpipe provides encryption and authentication mechanisms to ensure your data is secure during transmission.
• Flexibility: Snowpipe supports various data formats, including CSV, Avro, and JSON, making it easy to work with different data sources.

Handling Schema Evolution with Snowpipe

One of the biggest challenges in data engineering is handling schema changes. When your schema evolves, you need to re-ingest the data from the external table to reflect the changes. But what if you could avoid re-ingesting data altogether? That’s where Snowpipe comes in.

Snowpipe allows you to define a schema for your data ingestion pipeline, which can be updated as your schema evolves. This means you can make changes to your schema without having to re-ingest the data from the external table.

Step-by-Step Guide to Handling Schema Evolution with Snowpipe

Follow these steps to handle schema evolution with Snowpipe:

1. Define Your Initial Schema: Create a Snowpipe pipeline and define the initial schema for your data ingestion. This schema should align with the structure of your external table.
2. Ingest Initial Data: Ingest the initial data from the external table into Snowflake using Snowpipe.
3. Update Your Schema: When your schema evolves, update the Snowpipe pipeline schema to reflect the changes.
4. Resume Data Ingestion: Resumption of data ingestion will occur automatically, and Snowpipe will apply the updated schema to new data ingested from the external table.
5. Backfill Data (Optional): If you need to apply the updated schema to historical data, you can backfill the data by re-ingesting the data from the external table using the updated schema.

CREATE PIPELINE my_pipe
  AS
  COPY INTO my_table
  FROM '@my_stage/my_file.csv'
  FILE_FORMAT = (TYPE = 'CSV');

Example: Handling Schema Evolution with Snowpipe

Let’s consider an example where we have an external table with a simple schema:

Column Name	Data Type
ID	INTEGER
NAME	VARCHAR(50)
EMAIL	VARCHAR(100)

Our initial Snowpipe pipeline schema matches this table structure:

CREATE PIPELINE my_pipe
  AS
  COPY INTO my_table
  (
    ID INTEGER,
    NAME VARCHAR(50),
    EMAIL VARCHAR(100)
  )
  FROM '@my_stage/my_file.csv'
  FILE_FORMAT = (TYPE = 'CSV');

Later, we decide to add a new column, PHONE_NUMBER, to our table:

Column Name	Data Type
ID	INTEGER
NAME	VARCHAR(50)
EMAIL	VARCHAR(100)
PHONE_NUMBER	VARCHAR(20)

We update our Snowpipe pipeline schema to reflect the changes:

ALTER PIPELINE my_pipe
  AS
  COPY INTO my_table
  (
    ID INTEGER,
    NAME VARCHAR(50),
    EMAIL VARCHAR(100),
    PHONE_NUMBER VARCHAR(20)
  )
  FROM '@my_stage/my_file.csv'
  FILE_FORMAT = (TYPE = 'CSV');

Now, when we resume data ingestion, Snowpipe will apply the updated schema to new data ingested from the external table, and we don’t need to re-ingest the entire dataset.

Benefits of Handling Schema Evolution with Snowpipe

By handling schema evolution with Snowpipe, you can:

• Avoid Re-ingesting Data: No need to re-ingest the entire dataset when your schema changes.
• Maintain Data Continuity: Ensure data continuity and avoid data loss during schema changes.
• Improve Data Freshness: With Snowpipe, you can ingest data in near real-time, ensuring your data is always fresh and up-to-date.
• Reduce Maintenance Efforts: Simplify your data maintenance efforts by eliminating the need for re-ingesting data during schema changes.

Conclusion

In this article, we’ve explored the benefits of using Snowpipe for handling schema evolution without re-ingesting data from an external table. By following the steps outlined above, you can ensure seamless schema changes and maintain data continuity. Remember, with Snowpipe, you can focus on what matters most – extracting insights from your data – while letting Snowflake handle the heavy lifting of data ingestion and schema evolution.

Try Snowpipe today and experience the power of effortless data ingestion and schema evolution!

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About Snowpipe and Handling Schema Evolution Without Re-Ingesting the Data from an External Table