Data Loading in Snowflake

📥 Data Loading in Snowflake – A Beginner’s Guide

Snowflake makes it easy to load data from a variety of sources, whether it's local files, cloud storage, or streaming pipelines. Here's a breakdown of how data loading works in Snowflake, from concepts to practical steps.

🧩 Key Concepts

Term Description
Stage Temporary or permanent storage location for files before loading (local, internal, or cloud-based)
File Format Definition of how data is structured (CSV, JSON, Parquet, etc.)
Copy Command SQL command used to load data from a stage into a Snowflake table
Warehouse Required to perform data loading (acts as compute engine)

✅ Steps to Load Data into Snowflake
📁 1. Prepare Your Data
Format: CSV, JSON, Avro, Parquet, ORC, or XML

Compressed formats like .gz or .bz2 are supported

🚚 2. Choose a Staging Option
Type Description
Internal Stage Storage inside Snowflake (e.g., @%table, user, or named stage)
External Stage Cloud storage outside Snowflake (AWS S3, Azure Blob, GCS)

Internal Stage Examples:
@my_stage — Named internal stage

@%my_table — Table stage (automatically tied to the table)

🛠️ 3. Create Required Objects
a. Create a Target Table
sql
Copy
Edit
CREATE TABLE employees (
  id INT,
  name STRING,
  department STRING
);
b. Create a File Format (Optional but recommended)
sql
Copy
Edit
CREATE FILE FORMAT my_csv_format
  TYPE = 'CSV'
  FIELD_OPTIONALLY_ENCLOSED_BY = '"'
  SKIP_HEADER = 1;
c. Create a Named Stage (optional)
sql
Copy
Edit
CREATE STAGE my_stage
  FILE_FORMAT = my_csv_format;
⬆️ 4. Upload Data to Stage
a. Internal Stage (using snowflake CLI or Web UI)
bash
Copy
Edit
snowsql -a <account> -u <user> -f load.sql

PUT file:///path/to/data.csv @my_stage;
b. External Stage Example (AWS S3):
sql
Copy
Edit
CREATE STAGE my_s3_stage
  URL = 's3://mybucket/data/'
  CREDENTIALS = (AWS_KEY_ID = '...' AWS_SECRET_KEY = '...');
📦 5. Load Data Using COPY INTO
sql
Copy
Edit
COPY INTO employees
FROM @my_stage/data.csv
FILE_FORMAT = (FORMAT_NAME = 'my_csv_format');
🔁 Monitoring and Error Handling
Use VALIDATION_MODE to test data before loading:

sql
Copy
Edit
COPY INTO employees
FROM @my_stage/data.csv
FILE_FORMAT = (FORMAT_NAME = 'my_csv_format')
VALIDATION_MODE = 'RETURN_ALL_ERRORS';
Query LOAD_HISTORY() to track data load:

sql
Copy
Edit
SELECT * FROM INFORMATION_SCHEMA.LOAD_HISTORY WHERE TABLE_NAME = 'EMPLOYEES';
🔄 Incremental & Continuous Data Loads
Use streams and tasks for incremental/automated loading

Integrate with tools like:

Snowpipe (real-time or auto-loading from cloud storage)

ETL tools: Fivetran, Talend, Matillion

Custom pipelines with Python or Spark

📊 Common Use Cases
Use Case Tools Involved
One-time batch load PUT + COPY INTO
Real-time streaming Snowpipe or Kafka connectors
Data ingestion from S3/Azure/GCS External Stage + COPY INTO
Scheduled ELT Snowflake Tasks + Streams

🚀 Summary

Step What You Do
1. Prepare Structure and clean your data
2. Stage Upload to internal or external stage
3. Copy Use COPY INTO to load data into tables


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