Features
Snowflake SQL is the ANSI-compliant query language used to interact with Snowflake’s cloud-native data platform, enabling scalable analytics and data engineering without infrastructure management.
Snowflake SQL is the ANSI-compliant dialect for querying, transforming, and managing data stored in the Snowflake Data Cloud. It combines familiar SQL syntax with cloud-native features such as instant elasticity and secure data sharing.
Snowflake SQL is Snowflake’s implementation of ANSI SQL, extended with functions for semi-structured data, time travel, and access control. Users issue Snowflake SQL statements to load, query, and manage data without provisioning hardware.
Snowflake SQL offers automatic scaling, near-zero admin, and native support for JSON, Avro, Parquet, and XML. These features let teams focus on analytics rather than tuning clusters, making it ideal for modern ELT pipelines.
Snowflake separates storage and compute. Data reside in shared storage, while virtual warehouses provide on-demand compute. Each query runs on an isolated warehouse, eliminating resource contention and enabling per-team scaling.
SELECT, INSERT, UPDATE, DELETE, MERGE, CREATE, and ALTER follow ANSI syntax, easing migration from legacy warehouses.
Snowflake automatically reorganizes micro-partitions, removing manual index and vacuum tasks.
Built-in VARIANT datatype plus FLATTEN and LATERAL JOIN simplify querying nested JSON.
AS OF queries and UNDROP restore data without backups, guarding against accidental deletes.
Snowflake SQL powers interactive dashboards, batch ELT, machine-learning feature stores, and data sharing across business units or partners.
Use fully-qualified names: SELECT * FROM mydb.public.orders WHERE order_date > CURRENT_DATE-30;
Load JSON into a VARIANT column, then query with dot notation or colon syntax: SELECT v:customer:name AS cust_name FROM raw.orders;
Choose warehouse sizes by workload, leverage RESULT_SCAN for repeated queries, and avoid SELECT *. Use clustering keys only when query patterns require them.
Galaxy’s desktop SQL editor autocompletes Snowflake objects, suggests optimal warehouse usage, and its AI copilot rewrites or explains queries. Teams endorse trusted Snowflake SQL in Collections, preventing stale Slack snippets.
-- Calculate 7-day rolling revenue per product
WITH daily AS (
SELECT product_id,
order_date,
SUM(amount) AS revenue
FROM sales.public.orders
WHERE order_date >= CURRENT_DATE - 30
GROUP BY product_id, order_date)
SELECT product_id,
order_date,
SUM(revenue) OVER (
PARTITION BY product_id
ORDER BY order_date
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS rev_7d
FROM daily
ORDER BY product_id, order_date;
Avoid frequent warehouse resizing mid-query, overusing clustering keys, and relying on SELECT * in production views.
Snowflake SQL underpins analytics for thousands of modern companies. Understanding its cloud-native extensions lets data engineers design scalable ELT pipelines, control costs, and deliver insights quickly. Mastery empowers teams to optimize warehouses, secure data, and exploit semi-structured functions without vendor lock-in.
Yes, Snowflake SQL follows ANSI 2011 with additional commands for cloud features like EXTERNAL TABLE and STREAM.
Each warehouse supplies isolated compute; scaling it up adds CPU and memory, directly reducing query latency.
Absolutely. Galaxy’s connection manager stores Snowflake credentials securely, supports role switching, and surfaces database metadata for autocomplete.
Store JSON in VARIANT and use colon notation (data:key) or OBJECT_GET functions within standard SELECT statements.
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