"Handling Common Data Loading Errors in Snowflake: Best Practices with case study considering Orders Table"

When loading data into Snowflake, especially for a table like an orders table with 10 columns, you might encounter a range of errors due to data quality issues, file format mismatches, or system-related problems. Below are common errors you could face and the best practices for mitigating them for the next run:

1. File Format Mismatch (e.g., CSV vs JSON)

Error: The file format doesn't match the one specified in the COPY INTO command. For example, if you try to load a JSON file as a CSV.

Example:

• The orders table expects a CSV, but the file is in JSON format.
• Command might fail with an error like: File format mismatch: expected CSV but received JSON.

Mitigation:

• Check File Format: Before loading, ensure the file format is correct (e.g., CSV, JSON, etc.).
• Explicit Format Specification: When using COPY INTO, specify the correct file format options. For instance, for CSV files, ensure you're using FILE_FORMAT = (TYPE = 'CSV').

Solution for CSV file:

sql

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COPY INTO orders FROM @my_stage FILE_FORMAT = (TYPE = 'CSV' FIELD_OPTIONALLY_ENCLOSED_BY = '"');

• For JSON:

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COPY INTO orders FROM @my_stage FILE_FORMAT = (TYPE = 'JSON');

2. Data Type Mismatch

Error: Data types in the file do not match the column types in the target table (e.g., trying to load a string into a column defined as a numeric type).

Example:

• The price column in the orders table is defined as NUMBER, but the data file contains a string like "29.99" instead of 29.99.

Mitigation:

• Pre-Check Data Types: Ensure that the data file’s column types match the destination table’s column types.
• Modify Data File: Clean or transform the data before loading, ensuring that numbers are not quoted as strings.
• Use CAST: If needed, use the CAST or TO_NUMBER function during the load to convert data types where mismatches might occur.

Example Solution:

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COPY INTO orders (order_id, customer_id, price, order_date, etc.)

FROM @my_stage

FILE_FORMAT = (TYPE = 'CSV')

ON_ERROR = 'SKIP_FILE';? -- Skip problematic file rows

3. Missing or Extra Columns

Error: The number of columns in the file doesn't match the number of columns in the target table. For instance, a file with 9 columns when the table has 10 columns.

Example:

• The orders table has 10 columns: order_id, customer_id, price, order_date, etc.
• The file only has 9 columns, causing the load to fail with an error like: Column count mismatch: expected 10 columns but received 9.

Mitigation:

• Ensure Consistent File Structure: Ensure the data file has the correct number of columns.
• Use Column List in COPY INTO: If the file has missing or extra columns, use the COLUMN_LIST option in the COPY INTO command to map the file columns to the correct table columns.

Solution:

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COPY INTO orders (order_id, customer_id, price, order_date, etc.)

FROM @my_stage

FILE_FORMAT = (TYPE = 'CSV');

• Alternatively, handle missing data by using ON_ERROR = 'CONTINUE' to load only valid rows.

4. Invalid Data or NULL Values

Error: Some rows contain invalid data or nulls where non-null values are expected. For example, a NULL in a NOT NULL column.

Example:

• The order_id column is defined as NOT NULL, but the file contains a row where the order_id is NULL.

Mitigation:

• Pre-Process Data: Cleanse the data before loading to ensure that required columns have valid values.
• Use ON_ERROR Clause: Use ON_ERROR = 'CONTINUE' to skip rows with invalid data, or use ON_ERROR = 'SKIP_FILE' to skip the entire file in case of critical errors.
• Add Default Values: For nullable columns, ensure that missing or NULL values are replaced with a default or placeholder value before loading.

Example Solution:

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COPY INTO orders FROM @my_stage FILE_FORMAT = (TYPE = 'CSV')

ON_ERROR = 'CONTINUE';

• Use Default or Expression:

sql

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COPY INTO orders

(order_id, customer_id, price, order_date, etc.)

FROM @my_stage

FILE_FORMAT = (TYPE = 'CSV')

ON_ERROR = 'SKIP_FILE';

5. Incorrect Date Format

Error: If the date format in the file doesn't match the expected format in Snowflake, the load operation will fail.

Example:

• The order_date column in the orders table is defined as DATE, but the file has dates in DD/MM/YYYY format, while Snowflake expects YYYY-MM-DD.

Mitigation:

• Set the Correct Date Format: Specify the date format in the file format options. For example, if the date is in DD/MM/YYYY, configure the format.
• Use DATE_MASK: If the date format is unconventional, Snowflake allows the use of the DATE_MASK option.

Solution:

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COPY INTO orders FROM @my_stage FILE_FORMAT = (TYPE = 'CSV' FIELD_OPTIONALLY_ENCLOSED_BY = '"',

DATE_FORMAT = 'DD/MM/YYYY');

6. File Encoding Issues

Error: The file encoding (e.g., UTF-8, ISO-8859-1) does not match Snowflake’s expected encoding, leading to errors like Invalid UTF-8 encoding.

Mitigation:

• Check File Encoding: Ensure that the file is encoded in UTF-8 or another compatible encoding for Snowflake.
• Set File Encoding in COPY INTO: If necessary, specify the encoding type when defining the file format.

Solution:

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COPY INTO orders FROM @my_stage

FILE_FORMAT = (TYPE = 'CSV' FIELD_OPTIONALLY_ENCLOSED_BY = '"', ENCODING = 'UTF-8');

7. File Size Issues

Error: If the file is too large or too small, it might fail to upload or lead to timeout errors during the load.

Mitigation:

• Split Large Files: Split large files into smaller chunks to reduce load time and prevent timeouts.
• Monitor Staging Area: Ensure that the stage has enough space to accommodate large files.

Conclusion

To mitigate these errors, thorough data validation and preparation steps are essential before loading files into Snowflake. For each type of error, implement the appropriate data cleansing, format specification, and error-handling mechanisms such as ON_ERROR, DATE_FORMAT, and ENCODING parameters to ensure smooth data ingestion.

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