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snowflake-development Comprehensive Snowflake development assistant covering SQL best practices, data pipeline design (Dynamic Tables, Streams, Tasks, Snowpipe), Cortex AI functions, Cortex Agents, Snowpark Python, dbt integration, performance tuning, and security hardening.
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下载 Zip 下载中... name snowflake-development description Comprehensive Snowflake development assistant covering SQL best practices, data pipeline design (Dynamic Tables, Streams, Tasks, Snowpipe), Cortex AI functions, Cortex Agents, Snowpark Python, dbt integration, performance tuning, and security hardening. type skill created 2026-02-27T00:00:00.000Z domain data category data-engineering risk safe source community tags ["skill","data","data-engineering","snowflake"]
Snowflake Development
You are a Snowflake development expert. Apply these rules when writing SQL, building data pipelines, using Cortex AI, or working with Snowpark Python on Snowflake.
When to Use
When the user asks for help with Snowflake SQL, data pipelines, Cortex AI, or Snowpark Python.
When you need Snowflake-specific guidance for dbt, performance tuning, or security hardening.
SQL Best Practices
Naming and Style
Use snake_case for all identifiers. Avoid double-quoted identifiers — they create case-sensitive names requiring constant quoting.
Use CTEs (WITH clauses) over nested subqueries.
Use CREATE OR REPLACE for idempotent DDL.
Use explicit column lists — never SELECT * in production (Snowflake's columnar storage scans only referenced columns).
Stored Procedures — Colon Prefix Rule
In SQL stored procedures (BEGIN...END blocks), variables and parameters must use the colon : prefix inside SQL statements. Without it, Snowflake raises "invalid identifier" errors.
BAD:
CREATE PROCEDURE my_proc(p_id INT ) RETURNS STRING LANGUAGE SQL AS
BEGIN
LET result STRING;
SELECT name INTO result FROM users WHERE id = p_id;
RETURN result ;
END ;
GOOD:
CREATE PROCEDURE my_proc(p_id INT ) RETURNS STRING LANGUAGE SQL AS
BEGIN
LET result STRING;
name : users id :p_id;
;
;
SELECT
INTO
result
FROM
WHERE
=
RETURN
result
END
Semi-Structured Data
VARIANT, OBJECT, ARRAY for JSON/Avro/Parquet/ORC.
Access nested fields: src:customer.name::STRING. Always cast: src:price::NUMBER(10,2).
VARIANT null vs SQL NULL: JSON null is stored as "null". Use STRIP_NULL_VALUE = TRUE on load.
Flatten arrays: SELECT f.value:name::STRING FROM my_table, LATERAL FLATTEN(input => src:items) f;
MERGE for Upserts MERGE INTO target t USING source s ON t.id = s.id
WHEN MATCHED THEN UPDATE SET t.name = s.name, t.updated_at = CURRENT_TIMESTAMP ()
WHEN NOT MATCHED THEN INSERT (id, name, updated_at) VALUES (s.id, s.name, CURRENT_TIMESTAMP ());
Data Pipelines
Choosing Your Approach Approach When to Use Dynamic Tables Declarative transformations. Default choice. Define the query, Snowflake handles refresh. Streams + Tasks Imperative CDC. Use for procedural logic, stored procedure calls. Snowpipe Continuous file loading from S3/GCS/Azure.
Dynamic Tables CREATE OR REPLACE DYNAMIC TABLE cleaned_events
TARGET_LAG = '5 minutes'
WAREHOUSE = transform_wh
AS
SELECT event_id, event_type, user_id, event_timestamp
FROM raw_events
WHERE event_type IS NOT NULL ;
Set TARGET_LAG progressively: tighter at top, looser at bottom.
Incremental DTs cannot depend on Full refresh DTs.
SELECT * breaks on schema changes — use explicit column lists.
Change tracking must stay enabled on base tables.
Views cannot sit between two Dynamic Tables.
Streams and Tasks CREATE OR REPLACE STREAM raw_stream ON TABLE raw_events;
CREATE OR REPLACE TASK process_events
WAREHOUSE = transform_wh
SCHEDULE = 'USING CRON 0 */1 * * * America/Los_Angeles'
WHEN SYSTEM $STREAM_HAS_DATA('raw_stream' )
AS INSERT INTO cleaned_events SELECT ... FROM raw_stream;
ALTER TASK process_events RESUME;
Cortex AI
Function Reference Function Purpose AI_COMPLETELLM completion (text, images, documents) AI_CLASSIFYClassify into categories (up to 500 labels) AI_FILTERBoolean filter on text/images AI_EXTRACTStructured extraction from text/images/documents AI_SENTIMENTSentiment score (-1 to 1) AI_PARSE_DOCUMENTOCR or layout extraction AI_REDACTPII removal
Deprecated (do NOT use): COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, PARSE_DOCUMENT, SUMMARIZE, TRANSLATE, SENTIMENT, EMBED_TEXT_768.
TO_FILE — Common Error Source Stage path and filename are SEPARATE arguments:
TO_FILE('@db.schema.mystage' , 'invoice.pdf' )
Use AI_CLASSIFY for Classification (Not AI_COMPLETE) SELECT AI_CLASSIFY(ticket_text,
['billing' , 'technical' , 'account' ]):labels[0 ]::VARCHAR AS category
FROM tickets;
Cortex Agents CREATE OR REPLACE AGENT my_db.my_schema.sales_agent
FROM SPECIFICATION $spec$
{
"models": {"orchestration": "auto"},
"instructions": {
"orchestration": "You are SalesBot...",
"response": "Be concise."
},
"tools": [{"tool_spec": {"type": "cortex_analyst_text_to_sql", "name": "Sales", "description": "Queries sales..."}}],
"tool_resources": {"Sales": {"semantic_model_file": "@stage/model.yaml"}}
}
$spec$;
Use $spec$ delimiter (not $$).
models must be an object, not an array.
tool_resources is a separate top-level object, not nested inside tools.
Do NOT include empty/null values in edit specs — clears existing values.
Tool descriptions are the #1 quality factor.
Never modify production agents directly — clone first.
Snowpark Python from snowflake.snowpark import Session
import os
session = Session.builder.configs({
"account" : os.environ["SNOWFLAKE_ACCOUNT" ],
"user" : os.environ["SNOWFLAKE_USER" ],
"password" : os.environ["SNOWFLAKE_PASSWORD" ],
"role" : "my_role" , "warehouse" : "my_wh" ,
"database" : "my_db" , "schema" : "my_schema"
}).create()
Never hardcode credentials.
DataFrames are lazy — executed on collect()/show().
Do NOT use collect() on large DataFrames — process server-side.
Use vectorized UDFs (10-100x faster) for batch/ML workloads instead of scalar UDFs.
dbt on Snowflake Dynamic table materialization (streaming/near-real-time marts):
{{ config(materialized= 'dynamic_table' , snowflake_warehouse= 'transforming' , target_lag= '1 hour' ) }}
Incremental materialization (large fact tables):
{{ config(materialized= 'incremental' , unique_key= 'event_id' ) }}
Snowflake-specific configs (combine with any materialization):
{{ config(transient= true , copy_grants= true , query_tag= 'team_daily' ) }}
Do NOT use {{ this }} without {% if is_incremental() %} guard.
Use dynamic_table materialization for streaming/near-real-time marts.
Performance
Cluster keys : Only multi-TB tables, on WHERE/JOIN/GROUP BY columns.
Search Optimization : ALTER TABLE t ADD SEARCH OPTIMIZATION ON EQUALITY(col);
Warehouse sizing : Start X-Small, scale up. AUTO_SUSPEND = 60, AUTO_RESUME = TRUE.
Separate warehouses per workload.
Estimate AI costs first: SELECT SUM(AI_COUNT_TOKENS('claude-4-sonnet', text)) FROM table;
Security
Follow least-privilege RBAC. Use database roles for object-level grants.
Audit ACCOUNTADMIN regularly: SHOW GRANTS OF ROLE ACCOUNTADMIN;
Use network policies for IP allowlisting.
Use masking policies for PII columns and row access policies for multi-tenant isolation.
Common Error Patterns Error Cause Fix "Object does not exist" Wrong context or missing grants Fully qualify names, check grants "Invalid identifier" in proc Missing colon prefix Use :variable_name "Numeric value not recognized" VARIANT not cast src:field::NUMBER(10,2)Task not running Forgot to resume ALTER TASK ... RESUMEDT refresh failing Schema change or tracking disabled Use explicit columns, check change tracking
Connections
Domain: [[Daten & Analyse]]
Kategorie: [[Data Engineering]]
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