intermediate⏱75 minLesson 10 of 19

Domain 3.1 — Data Loading and Unloading

Master Snowflake's bulk data loading and unloading: file formats, stages (internal and external), the COPY INTO command, error handling options, and best practices for high-throughput ingestion.

Domain 3.1 — Data Loading and Unloading

Exam Weight

Domain 3.0 — Data Loading, Unloading, and Connectivity accounts for ~18% of the exam.

ℹ️Note

This lesson maps to Exam Objective 3.1: Perform data loading and unloading, including file formats, stages, the COPY INTO command, and error handling options.

The Data Loading Process

Snowflake's bulk loading process follows this flow:

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File Formats

A File Format defines how Snowflake parses files during loading and unloading. Supported types:

Format	Type	Notes
CSV	Structured	Most common; highly configurable delimiter, quotes, encoding
JSON	Semi-structured	Loaded into VARIANT columns
Avro	Semi-structured	Schema embedded in file
ORC	Semi-structured	Columnar, often from Hadoop
Parquet	Semi-structured	Columnar, highly compressed
XML	Semi-structured	Hierarchical document format

sql

-- CSV file format
CREATE FILE FORMAT csv_format
    TYPE = CSV
    FIELD_DELIMITER = ','
    RECORD_DELIMITER = '\n'
    SKIP_HEADER = 1
    FIELD_OPTIONALLY_ENCLOSED_BY = '"'
    NULL_IF = ('NULL', 'null', 'N/A', '')
    EMPTY_FIELD_AS_NULL = TRUE
    ENCODING = 'UTF-8'
    TRIM_SPACE = TRUE;

-- JSON file format
CREATE FILE FORMAT json_format
    TYPE = JSON
    STRIP_OUTER_ARRAY = TRUE    -- remove the outer [] wrapper
    STRIP_NULL_VALUES = FALSE
    IGNORE_UTF8_ERRORS = FALSE;

-- Parquet file format
CREATE FILE FORMAT parquet_format
    TYPE = PARQUET
    SNAPPY_COMPRESSION = TRUE;

-- Inline format (no need to create named format)
COPY INTO my_table
FROM @my_stage
FILE_FORMAT = (TYPE = CSV FIELD_DELIMITER = '|' SKIP_HEADER = 1);

Stages

A stage is the intermediary landing zone where files are placed before loading into Snowflake tables.

Internal Stages

Snowflake manages the underlying storage:

Stage Type	Syntax	Scope
User stage	`@~`	Per-user; private, not shareable
Table stage	`@%table_name`	Per-table; linked to a specific table
Named internal stage	`@stage_name`	Shared within account, most flexible

sql

-- Upload a file to a named internal stage using SnowSQL / CLI
PUT file:///local/path/orders.csv @my_stage AUTO_COMPRESS = TRUE;

-- Or via Snowflake CLI
snow stage copy ./orders.csv @my_stage/

-- List files in a stage
LIST @my_stage;
LIST @my_stage/orders/;

-- Remove files from a stage
REMOVE @my_stage/old_files/;

External Stages

Data remains in customer-owned cloud storage:

sql

-- External stage on Amazon S3
CREATE STAGE s3_orders_stage
    URL = 's3://my-bucket/orders/'
    STORAGE_INTEGRATION = my_s3_integration
    FILE_FORMAT = (FORMAT_NAME = csv_format)
    DIRECTORY = (ENABLE = TRUE);  -- enable directory table

-- External stage on Azure Blob Storage
CREATE STAGE azure_stage
    URL = 'azure://myaccount.blob.core.windows.net/mycontainer/data/'
    STORAGE_INTEGRATION = my_azure_integration
    FILE_FORMAT = (FORMAT_NAME = json_format);

-- External stage on Google Cloud Storage
CREATE STAGE gcs_stage
    URL = 'gcs://my-bucket/data/'
    STORAGE_INTEGRATION = my_gcs_integration;

Storage Integrations

A storage integration securely connects Snowflake to cloud storage using an IAM role / service principal — no credentials stored in Snowflake:

sql

CREATE STORAGE INTEGRATION my_s3_integration
    TYPE = EXTERNAL_STAGE
    STORAGE_PROVIDER = S3
    ENABLED = TRUE
    STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::123456789:role/snowflake-role'
    STORAGE_ALLOWED_LOCATIONS = ('s3://my-bucket/orders/', 's3://my-bucket/products/');

-- Get the IAM values to configure in AWS
DESC INTEGRATION my_s3_integration;

Directory Tables

Enable file listing on external stages as a queryable table:

sql

ALTER STAGE s3_orders_stage SET DIRECTORY = (ENABLE = TRUE);

-- Refresh the directory table metadata
ALTER STAGE s3_orders_stage REFRESH;

-- Query the directory table
SELECT * FROM DIRECTORY(@s3_orders_stage);

COPY INTO — Loading Data

COPY INTO <table> is the primary bulk loading command:

sql

-- Basic load
COPY INTO orders
FROM @my_stage/orders/
FILE_FORMAT = (FORMAT_NAME = csv_format);

-- Load specific files
COPY INTO orders
FROM @my_stage/orders/
FILES = ('orders_2025_01.csv', 'orders_2025_02.csv')
FILE_FORMAT = (FORMAT_NAME = csv_format);

-- Load using pattern matching
COPY INTO orders
FROM @my_stage/orders/
PATTERN = '.*orders_2025_.*\\.csv'
FILE_FORMAT = (FORMAT_NAME = csv_format);

-- Transform during load (SELECT from stage)
COPY INTO orders (id, amount, load_ts)
FROM (
    SELECT $1::NUMBER, $2::DECIMAL(10,2), CURRENT_TIMESTAMP
    FROM @my_stage/orders/
)
FILE_FORMAT = (FORMAT_NAME = csv_format);

Load Status Tracking

COPY INTO automatically tracks which files have been loaded to prevent duplicates:

sql

-- View load history for a table (last 64 days)
SELECT *
FROM TABLE(INFORMATION_SCHEMA.COPY_HISTORY(
    TABLE_NAME => 'ORDERS',
    START_TIME => DATEADD('day', -7, CURRENT_TIMESTAMP)
));

-- Account-wide history
SELECT * FROM SNOWFLAKE.ACCOUNT_USAGE.COPY_HISTORY
WHERE TABLE_NAME = 'ORDERS'
AND LAST_LOAD_TIME > DATEADD('day', -7, CURRENT_TIMESTAMP);

ℹ️Note

Snowflake tracks the 64-day load history per stage. Files loaded within the last 64 days are tracked and will not be re-loaded by default. To force a reload, use FORCE = TRUE.

Error Handling Options

The ON_ERROR parameter controls how COPY INTO handles errors:

Option	Behavior	Use Case
`CONTINUE`	Skip bad rows, load good rows	Tolerant loading
`SKIP_FILE`	Skip the entire file with errors	File-level quality gate
`SKIP_FILE_<n>`	Skip file if more than n errors occur	Threshold-based
`SKIP_FILE_<n>%`	Skip file if error rate exceeds n%	Percentage threshold
`ABORT_STATEMENT`	Abort entire COPY on first error (default)	Strict validation

sql

-- Continue loading, skip bad rows
COPY INTO orders
FROM @my_stage
FILE_FORMAT = (FORMAT_NAME = csv_format)
ON_ERROR = 'CONTINUE';

-- Skip file if more than 5 errors
COPY INTO orders
FROM @my_stage
FILE_FORMAT = (FORMAT_NAME = csv_format)
ON_ERROR = 'SKIP_FILE_5';

-- Skip file if more than 10% errors
COPY INTO orders
FROM @my_stage
FILE_FORMAT = (FORMAT_NAME = csv_format)
ON_ERROR = 'SKIP_FILE_10%';

-- Check what was rejected
SELECT *
FROM TABLE(VALIDATE(orders, JOB_ID => '<copy_job_id>'));

Validating Before Loading

sql

-- Validate without actually loading (dry-run)
COPY INTO orders
FROM @my_stage
FILE_FORMAT = (FORMAT_NAME = csv_format)
VALIDATION_MODE = 'RETURN_ERRORS';  -- returns error rows only

-- Or validate all rows
COPY INTO orders
FROM @my_stage
VALIDATION_MODE = 'RETURN_ALL_ERRORS';

Semi-Structured Data Loading

Loading JSON

sql

-- Load JSON into a VARIANT column
CREATE TABLE raw_events (v VARIANT);

COPY INTO raw_events
FROM @json_stage
FILE_FORMAT = (TYPE = JSON STRIP_OUTER_ARRAY = TRUE);

-- Query nested JSON
SELECT
    v:event_type::STRING AS event_type,
    v:user.id::NUMBER AS user_id,
    v:user.name::STRING AS user_name,
    v:properties:page_url::STRING AS page_url
FROM raw_events;

-- Flatten arrays
SELECT
    v:order_id::NUMBER AS order_id,
    item.value:product_id::NUMBER AS product_id,
    item.value:quantity::NUMBER AS qty
FROM raw_events,
LATERAL FLATTEN(INPUT => v:items) item;

Data Unloading with COPY INTO Location

COPY INTO @stage exports data from Snowflake to files:

sql

-- Unload to internal stage
COPY INTO @my_stage/exports/orders_export
FROM orders
FILE_FORMAT = (TYPE = CSV HEADER = TRUE)
SINGLE = FALSE         -- multiple files (parallel export)
MAX_FILE_SIZE = 500000000;  -- 500 MB per file

-- Unload to external S3 stage
COPY INTO @s3_export_stage/orders/
FROM (SELECT * FROM orders WHERE order_date = CURRENT_DATE)
FILE_FORMAT = (TYPE = PARQUET)
HEADER = TRUE;

-- Unload as gzip-compressed CSV
COPY INTO @my_stage/compressed_export
FROM orders
FILE_FORMAT = (TYPE = CSV COMPRESSION = GZIP HEADER = TRUE);

Unloading Best Practices

Setting	Recommendation
`SINGLE = FALSE`	Use multiple files for large exports (parallel)
`MAX_FILE_SIZE`	Keep files at 100–500 MB for downstream compatibility
Compression	Use GZIP or SNAPPY to reduce transfer costs
File naming	Use `INCLUDE_QUERY_ID = TRUE` for unique filenames

Practice Questions

Q1. A COPY INTO command is executed and some rows have invalid data types. Which ON_ERROR option loads all valid rows while skipping only the bad rows?

A) ABORT_STATEMENT
B) SKIP_FILE
C) CONTINUE ✅
D) RETURN_ERRORS

Q2. Snowflake tracks COPY INTO load history for how many days per stage to prevent duplicate loading?

A) 7 days
B) 30 days
C) 64 days ✅
D) 90 days

Q3. Which COPY INTO parameter allows you to validate file contents without actually loading any data?

A) ON_ERROR = CONTINUE
B) VALIDATION_MODE = 'RETURN_ERRORS' ✅
C) FORCE = TRUE
D) PURGE = TRUE

Q4. An external stage uses a STORAGE_INTEGRATION instead of credentials. What is the primary security benefit?

A) Data is encrypted automatically
B) No cloud credentials are stored in Snowflake ✅
C) The stage can only be used by SYSADMIN
D) Files are automatically deleted after loading

Q5. Which special stage syntax refers to a table's auto-provisioned stage?

A) @~
B) @stage_name
C) @%table_name ✅
D) @$table_name

Q6. When unloading with SINGLE = FALSE (default), what advantage does this provide?

A) Files are encrypted automatically
B) Export runs in parallel across warehouse nodes ✅
C) Files are compressed automatically
D) Only one file format can be used

✅Success

Key Takeaways for Exam Day:

Load flow: Files → Stage → COPY INTO → Table
Internal stages: @~ (user), @%table (table), @name (named)
ON_ERROR = CONTINUE = skip bad rows | ABORT_STATEMENT = fail on first error (default)
COPY history tracked for 64 days per stage (prevents duplicate loads)
VALIDATION_MODE = dry-run to check errors without loading
Storage Integrations = IAM role auth; no credentials in Snowflake

Progress53%

Domain 2.3 — Monitoring and Cost Management

Domain 3.2 — Automated Data Ingestion

Course Content

1. Domain 1.1 — Snowflake Architecture 2. Domain 1.2 — Snowflake Interfaces and Tools 3. Domain 1.3 — Snowflake Object Hierarchy and Types 4. Domain 1.4 — Configuring Virtual Warehouses 5. Domain 1.5 — Snowflake Storage Concepts 6. Domain 1.6 — AI/ML and Application Development Features 7. Domain 2.1 — Snowflake Security Model and Principles 8. Domain 2.2 — Data Governance Features 9. Domain 2.3 — Monitoring and Cost Management 10. Domain 3.1 — Data Loading and Unloading 11. Domain 3.2 — Automated Data Ingestion 12. Domain 3.3 — Snowflake Connectors and Integrations 13. Domain 4.1 — Evaluating Query Performance 14. Domain 4.2 — Optimizing Query Performance 15. Domain 4.3 — Snowflake Caching 16. Domain 4.4 — Data Transformation in Snowflake 17. Domain 5.1 — Data Collaboration, Replication, and Business Continuity 18. Domain 5.2 — Secure Data Sharing and Data Clean Rooms 19. Domain 5.3 — Snowflake Marketplace and Native Apps