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                     Top 7 Snowflake Schema 
Tips for Better Data 
Management
Snowflake Training in Ameerpet is a powerful cloud-based data warehousing platform 
that allows users to manage large volumes of data with ease, scalability, and flexibility. 
A critical concept for effective data management in Snowflake is understanding schema 
attributes. Schemas in Snowflake are used to organize and manage database objects 
like tables, views, and stages, ensuring that data is easily accessible, secure, and 
manageable. In this article, we will explore the key schema attributes in Snowflake, 
their importance, and how to use them effectively. Snowflake Course
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What is a Schema in Snowflake?
A schema in Snowflake is a logical container that holds While a database holds multiple schemas, each schema can 
database objects such as tables, views, stages, file be uniquely identified by its name, which helps to organize 
formats, and stored procedures. It serves as a namespace data at a granular level. Snowflake Training in Ameerpet 
within a database, allowing data to be logically grouped supports the creation of schemas within databases, which can 
and organized. A schema provides structure and then be used to organize related datasets.
simplifies access control, data management, and query 
optimization.
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Key Schema Attributes in Snowflake
Schema attributes define the properties of a schema, describing how it behaves, is structured, and is managed within Snowflake. The 
main schema attributes that you will frequently encounter are:
1 Schema Name 4 Data Retention Time
2 Database Name 5 Schema Comments
3 Schema Owner 6 Schema Data Sharing Attributes
7 Schema Size
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Schema Naming and Database Relationship
1. Schema Name 2. Database Name
The schema name is the unique identifier for the schema within the A schema is part of a database, which serves as the overarching 
database. It helps users and administrators easily locate and container. A database can contain multiple schemas, and each 
reference the schema. Snowflake Course supports schema names schema will belong to a specific database. Understanding this 
with up to 255 characters. When naming schemas, it’s essential to relationship helps users manage and structure data at a database 
follow consistent naming conventions that reflect the purpose or level.
Example:
data stored within the schema.
Example:
CREATE DATABASE ecommerce_db;
CREATE SCHEMA sales_data;
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Schema Ownership and Data Retention
3. Schema Owner 4. Data Retention Time
The schema owner is the user or role responsible for Snowflake allows you to define the data retention period 
managing the schema, including its creation, for tables within a schema. This attribute specifies how 
modification, and access control. In Snowflake, roles can long historical data will be kept before being purged 
be assigned to manage schemas, and the ownership automatically. By default, Snowflake retains data for 1 
provides the owner with the ability to alter, drop, or day (72 hours), but you can adjust this retention time 
manage permissions on the schema. Proper role based on your business or regulatory needs.
Example:
management and security policies are vital to ensure that 
only authorized users can perform specific actions on the 
Example: CREATE OR REPLACE SCHEMA sales_data 
schema.
RETENTION_TIME = 30 DAYS;
GRANT OWNERSHIP ON SCHEMA 
ecommerce_db.sales_data TO ROLE admin_role;
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Schema Comments and Data Sharing
6. Schema Data Sharing Attributes
Snowflake supports data sharing, which allows 
5. Schema Comments
you to share specific data (like tables or views) 
Schema comments are used for documentation across different Snowflake accounts. With 
purposes. They provide context for the schema, schema-level data sharing, you can securely 
helping team members and other users grant access to a schema and its objects without 
understand its purpose and the types of data it requiring data replication. This feature is 
stores. Adding comments at the schema level is particularly beneficial when collaborating with 
particularly useful for collaboration and future- other teams or third-party vendors.
proofing your Snowflake environment.
CREATE SHARE sales_data_share;
COMMENT ON SCHEMA 
ecommerce_db.sales_data IS 'Schema for 
storing sales transaction data'; GRANT USAGE ON SCHEMA 
ecommerce_db.sales_data TO SHARE 
sales_data_share;
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Schema Size Monitoring
7. Schema Size
Schema size represents the total storage occupied by 
the schema’s objects. As your data grows, it’s 
important to monitor schema size to manage storage 
costs and optimize performance. Snowflake provides 
metadata views that allow users to track the storage 
usage of schemas, ensuring that large datasets are 
handled efficiently.
Example Query
SELECT SCHEMA_NAME, SUM(BYTES) AS 
schema_sizeFROM 
INFORMATION_SCHEMA.SCHEMASWHERE 
DATABASE_NAME = 'ecommerce_db'GROUP BY 
SCHEMA_NAME;
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Best Practices for Managing Schema Attributes
1. Consistent Naming Conventions
A consistent naming convention is essential for managing schemas, especially in larger environments. 
The schema name should be descriptive, reflecting the type of data it contains. For example, a schema 
holding sales transaction data might be called sales_data, while one for marketing analytics could be 
marketing_analytics. Consistent naming helps users locate and understand schemas quickly.
2. Utilize Role-Based Access Control (RBAC)
Snowflake provides robust role-based access control, which allows you to assign different levels of 
permissions to schemas. Using RBAC ensures that only authorized users have access to sensitive data 
and can modify schema objects. It’s essential to assign appropriate roles such as USAGE, SELECT, INSERT, 
ALTER, or DROP based on the user's responsibilities.
Example:
GRANT USAGE, SELECT ON SCHEMA ecommerce_db.sales_data TO ROLE analyst_role;
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Data Retention Policies
Implement Policies
Implement data retention policies that align with your organization's compliance and 
business  requirements.
Configure Automatic Retention
Snowflake allows you to configure automatic data retention, ensuring that 
older data is archived or deleted after a certain period.
Ensure Compliance
This is particularly important for complying with data 
privacy regulations like GDPR.
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Conclusion
Understanding schema attributes in Snowflake is fundamental to managing and optimizing your data environment. 
These attributes define how schemas are organized, managed, and accessed, playing a critical role in ensuring data is 
secure, efficient, and accessible. By following best practices such as using consistent naming conventions, 
implementing proper access control, and monitoring storage usage, you can optimize your Snowflake environment for 
performance, security, and scalability. Snowflake’s schema attributes provide the flexibility and control needed to 
effectively manage complex datasets and collaborate across teams, making it a powerful platform for modern data 
warehousing.
Visualpath is the Leading and Best Institute for learning in Hyderabad. We provide Snowflake Online Training. You will 
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