Using schemas in Snowflake can be beneficial in various scenarios, and one common scenario is organizing data and managing access control for different projects or departments within a single database. Let's explore this scenario in more detail:

**Scenario: Multi-Department Data Management**

Imagine a large organization with multiple departments, such as Sales, Marketing, Finance, and Human Resources. Each department collects and analyzes data relevant to their operations. Without proper organization, the database could become cluttered and difficult to manage. Schemas can provide a solution by logically grouping and segregating data and objects for each department.

**Benefits:**

1. **Data Organization:** Each department can have its own schema, containing tables, views, and other objects specific to its needs. This separation improves data organization and makes it easier to locate and manage objects.
2. **Access Control:** Schemas enable fine-grained access control. You can grant permissions at the schema level, allowing each department to have control over who can access their data. For example, only members of the Finance team should access financial data.
3. **Security:** Schemas help enforce data security by restricting access to sensitive information. Only authorized users and roles can access the data within a specific schema.
4. **Collaboration:** Departments can work independently within their schemas, facilitating collaboration within teams. Shared objects can also be placed in shared schemas accessible to multiple departments.
5. **Default Schema:** Users and roles can have default schemas set, which simplifies querying by eliminating the need to qualify object names with schema names.
6. **Data Consistency:** Schemas ensure consistent naming conventions for objects, reducing naming conflicts and providing a standardized structure.

**Example:**

In this scenario, each department might have its own schema:

- **`sales_data`**: Contains tables for sales transactions, customer information, and sales performance metrics.
- **`marketing_data`**: Contains tables for marketing campaigns, customer interactions, and campaign analytics.
- **`finance_data`**: Contains tables for financial records, budgets, and expense tracking.
- **`hr_data`**: Contains tables for employee information, payroll, and attendance.

Each schema would contain objects relevant to its respective department, and access to these schemas would be controlled based on roles and privileges. This approach enhances data management, access control, and collaboration while maintaining data segregation among different departments.

Schemas in Snowflake provide a versatile way to organize data and manage access, making them a powerful tool in a variety of scenarios like the one described above.

A schema search path in Snowflake is a sequence of schemas that the system searches through when resolving references to database objects (such as tables, views, functions) in SQL queries. The purpose of a schema search path is to simplify queries by allowing you to reference objects without specifying the schema name explicitly. Instead, Snowflake searches through the specified schemas in the order they appear in the search path until it finds the referenced object.

The schema search path is particularly useful in scenarios where you frequently query objects from the same set of schemas. It eliminates the need to specify the schema in every query, which can make your queries more concise and easier to read.

The schema search path is configured at the session level, meaning it affects the behavior of your current session only and does not impact other users or sessions.

To configure the schema search path in Snowflake, you use the **`SET`** command. Here's the syntax to set the schema search path:

```sql
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SET SEARCH_PATH = (schema_name1, schema_name2, ...);

```

- **`schema_name1, schema_name2, ...`**: A comma-separated list of schema names in the order you want Snowflake to search through.

For example, if you want Snowflake to search for objects first in the **`sales_data`** schema and then in the **`public`** schema, you would use:

```sql
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SET SEARCH_PATH = (sales_data, public);

```

With this configuration, if you reference an object without specifying a schema, Snowflake will first search for the object in the **`sales_data`** schema and then in the **`public`** schema.

To reset the schema search path to its default value (searching only the current user's schema), you can use:

```sql
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SET SEARCH_PATH = RESET;

```

Keep in mind that while the schema search path can simplify your queries, it's important to use it judiciously. Make sure that the schemas you include in the search path are relevant to your use case, and be aware that conflicts in object names between schemas can lead to unexpected results.

Yes, objects from different schemas can be joined together in a single query in Snowflake. Snowflake allows you to perform joins between tables, views, or other objects located in different schemas within the same database.

To join objects from different schemas, you need to provide the fully qualified names of the objects in your query. A fully qualified name includes both the schema name and the object name, separated by a dot. Here's how you can perform joins between objects in different schemas:

```sql
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SELECT
schema1.table1.column AS column1,
schema2.table2.column AS column2
FROM
schema1.table1
JOIN
schema2.table2
ON
schema1.table1.id = schema2.table2.id;

```

In this example, **`schema1.table1`** and **`schema2.table2`** are fully qualified object names representing tables located in different schemas. The query performs a join between the two tables based on a common column **`id`**.

When joining objects from different schemas, make sure you have the necessary privileges to access objects in all referenced schemas. Users need appropriate privileges on the objects they are joining and querying, regardless of the schema in which those objects are located.

Fully qualifying object names in your query ensures that Snowflake knows which objects you are referencing and where to find them, enabling you to seamlessly join data from different schemas in a single query.

In Snowflake, both user-defined schemas and shared schemas are used to organize and manage database objects, but they serve different purposes and have distinct characteristics:

**User-Defined Schema:**

1. **Ownership:** User-defined schemas are owned by a specific user or role. The owner has full control over the schema and can grant privileges to other users and roles.
2. **Access Control:** The owner of a user-defined schema can control access to the schema and its objects by granting or revoking privileges to other users and roles.
3. **Data Segregation:** User-defined schemas are often used to segregate data and objects based on projects, teams, applications, or any other criteria. Each user or role can have their own set of schemas to manage their objects.
4. **Default Schema:** Users and roles can have a default schema set, which determines the schema where new objects are created by default.

**Shared Schema:**

1. **Ownership:** Shared schemas are typically owned by a system-defined role such as **`PUBLIC`** or a specific role designated for shared access. They are not owned by individual users or roles.
2. **Access Control:** Access to shared schemas is controlled by Snowflake administrators. They grant necessary privileges to roles or users that need to interact with the shared schema.
3. **Centralized Data:** Shared schemas are often used for centralizing data that needs to be accessed by multiple users or teams. They provide a common location for shared data assets.
4. **Data Collaboration:** Shared schemas facilitate collaboration by providing a standardized location for shared objects and data. Different teams or users can access and use the same data assets.
5. **Default Schema:** Shared schemas are not typically set as default schemas for users or roles since they are intended to be shared among multiple entities.

In summary, user-defined schemas are used to organize and manage data and objects for specific users or roles, allowing for data segregation and customized access control. Shared schemas, on the other hand, are used for centralized and shared data assets that need to be accessed by multiple users or teams. Snowflake administrators control access to shared schemas, while the owner of a user-defined schema has more control over its management.

Schema-level access control in Snowflake allows you to control user and role access to specific schemas within a database. This fine-grained access control ensures that only authorized users can perform actions (such as creating, altering, or dropping objects) within a particular schema. Schema-level access control is crucial for maintaining data security and enforcing data segregation based on different projects, teams, or applications.

Here's how schema-level access control works in Snowflake:

1. **Privileges:** Snowflake provides a set of privileges that can be granted at the schema level. These privileges control what users and roles can do within a specific schema. Common schema-level privileges include **`CREATE`**, **`ALTER`**, **`DROP`**, **`USAGE`**, and more.
2. **GRANT and REVOKE Statements:** To grant schema-level privileges, you use the **`GRANT`** statement. To remove privileges, you use the **`REVOKE`** statement. You can grant privileges to both users and roles.

```sql
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-- Grant USAGE privilege on a schema to a role
GRANT USAGE ON SCHEMA schema_name TO ROLE role_name;

-- Revoke privilege
REVOKE USAGE ON SCHEMA schema_name FROM ROLE role_name;

```

3. **Default Schema:** Users and roles can have a default schema set. When a user or role logs in, Snowflake will automatically use the default schema for that session. This helps simplify queries by allowing users to reference objects in their default schema without explicitly specifying the schema name.

```sql
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-- Set a user's default schema
ALTER USER user_name SET DEFAULT_SCHEMA = schema_name;

```

4. **Using Qualified Names:** When querying objects in a schema, you can use qualified names (schema name + object name) to specify the exact schema from which to retrieve the data or perform actions.

```sql
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SELECT * FROM schema_name.table_name;

```

Schema-level access control is an essential feature for managing data security and access permissions in Snowflake. It allows you to enforce data segregation and ensure that users and roles can only interact with specific schemas and objects for which they have been granted appropriate privileges.

To create a new schema in Snowflake, you can use the **`CREATE SCHEMA`** statement. This command creates a logical container for organizing database objects within the Snowflake database. Here's the SQL syntax for creating a new schema:

```sql
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CREATE SCHEMA schema_name;

```

- **`schema_name`**: The name you want to give to the new schema.

Here's an example of creating a new schema named **`sales_data`**:

```sql
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CREATE SCHEMA sales_data;

```

Once the schema is created, you can use it to organize and manage database objects such as tables, views, functions, and more. For instance, you can create tables within the newly created schema:

```sql
sqlCopy code
CREATE TABLE sales_data.sales (
sale_id INT,
sale_date DATE,
amount DECIMAL
);

```

In this example, a schema named **`sales_data`** is created, and a table named **`sales`** is created within that schema. This helps keep related objects together and improves data organization and management within the Snowflake database.

Yes, you can have multiple schemas within a single Snowflake database. Schemas are used to logically group and organize database objects, such as tables, views, and functions. Multiple schemas help you manage and categorize your objects more efficiently.

When executing queries, you can specify the schema to use by qualifying the object names with the schema name. This ensures that Snowflake knows which schema to look in when searching for the specified objects. To specify a schema in queries, you use the following syntax:

```sql
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SELECT column1, column2, ...
FROM schema_name.table_name;

```

- **`schema_name`**: The name of the schema where the object resides.
- **`table_name`**: The name of the table or other object you want to query.

Here's an example of how to specify a schema in a query:

```sql
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SELECT customer_name, order_date, total_amount
FROM sales_data.sales_orders;

```

In this example, the **`sales_orders`** table is accessed within the **`sales_data`** schema.

If you do not specify a schema explicitly in your query, Snowflake will assume that you are referring to an object within the default schema for your session. You can set the default schema for your session using the **`USE SCHEMA`** command:

```sql
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USE SCHEMA schema_name;

```

This command sets the default schema for your session, so you don't need to specify the schema name in every query. However, even with a default schema, you can still fully qualify object names with schema names if needed.

Having multiple schemas within a single database provides flexibility for organizing your data and database objects based on your needs, projects, or teams.

In Snowflake, a schema is a logical container that helps organize and manage database objects such as tables, views, functions, and procedures. It provides a way to group related objects together, which improves data organization, access control, and management within a database.

Key points about schemas in Snowflake:

1. **Logical Container:** A schema is a named logical container that holds a collection of database objects. It acts as a namespace for these objects, allowing you to organize and categorize them.
2. **Access Control:** Schemas enable you to control access to groups of objects. You can grant permissions at the schema level, making it easier to manage security and access for multiple objects at once.
3. **Object Organization:** Schemas help keep the database organized by grouping related objects together. This is particularly useful as the database grows and contains numerous objects.
4. **Reduced Naming Conflicts:** Schemas prevent naming conflicts by allowing objects with the same name to exist in different schemas without conflict.
5. **Schema Privileges:** You can grant privileges on schemas, controlling what users and roles can do within the schema (e.g., creating, altering, dropping objects).
6. **Migration and Cloning:** Schemas simplify migration and cloning of objects between different environments, such as development, testing, and production.
7. **Logical Separation:** Schemas allow logical separation of data for different applications, projects, or departments within the same database.

Here's an example of creating a schema and using it to organize objects:

```sql
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-- Create a schema
CREATE SCHEMA my_schema;

-- Create tables within the schema
CREATE TABLE my_schema.sales (
sale_id INT,
sale_date DATE,
amount DECIMAL
);

CREATE TABLE my_schema.customers (
customer_id INT,
customer_name STRING,
email STRING
);

```

In this example, the **`my_schema`** schema contains two tables: **`sales`** and **`customers`**. This schema helps keep related tables together and makes it easier to manage access and permissions for these tables.

Schemas are a fundamental organizational tool in Snowflake that enhance data management, access control, and overall database organization.

To drop a view in Snowflake, you can use the **`DROP VIEW`** statement. This command removes the view from the database. Here's the SQL syntax to drop a view:

```sql
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DROP VIEW [IF EXISTS] view_name;

```

- **`IF EXISTS`**: This is an optional keyword. If used, it prevents an error from being raised if the view doesn't exist. If omitted and the view doesn't exist, an error will be raised.
- **`view_name`**: The name of the view you want to drop.

Here's an example of dropping a view named **`MyView`**:

```sql
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DROP VIEW MyView;

```

If you want to drop a view only if it exists, you can use the **`IF EXISTS`** keyword:

```sql
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DROP VIEW IF EXISTS MyView;

```

Please be cautious when using the **`DROP VIEW`** statement, as it permanently removes the view from the database, and there is no way to recover the view once it has been dropped. Always make sure you have a backup or a plan to recreate the view if needed before using the **`DROP VIEW`** command.

If the underlying table structure of a view is changed, the view itself may be affected, and you might need to take certain actions to ensure the view remains functional and accurate. The impact of changing the table structure depends on the type of changes made and the nature of the view.

Here's what can happen to a view when the underlying table structure changes:

1. **Column Addition or Removal:** If columns are added to or removed from the underlying table, the view may still work as long as the view's query doesn't explicitly reference the added or removed columns. The view's query will continue to present data based on the columns it references.
2. **Column Renaming or Data Type Change:** If columns are renamed or their data types are changed in the underlying table, and the view's query references those columns, the view may become invalid. You might need to modify the view's query to reflect the changes in the underlying table.
3. **Primary Key or Unique Key Changes:** If the primary key or unique key of the underlying table changes, this could potentially affect queries involving joins or other data relationships in the view's query.
4. **Data Truncation:** If data types of columns in the underlying table are changed to narrower data types, data truncation could occur for values that no longer fit within the new data types.
5. **Table Renaming or Deletion:** If the underlying table is renamed or deleted, the view will become invalid. You might need to recreate the view using the new table name or restore the table.

It's important to note that Snowflake does not automatically update views when the underlying table structure changes. You will need to manually review and modify the view's query if necessary to accommodate any changes in the underlying table.

In general, if you plan to make changes to the structure of an underlying table that is used by one or more views, it's a good practice to review and update the views accordingly to ensure their continued accuracy and functionality. Additionally, it's wise to test the impact of changes in a non-production environment before applying them to a live system.

Yes, a Snowflake view can reference objects from multiple schemas. Snowflake supports cross-schema references, which means you can create a view that pulls data from tables, views, or other objects located in different schemas within the same database.

To reference objects from multiple schemas in a Snowflake view, you need to provide the fully qualified object names. A fully qualified name includes both the schema name and the object name, separated by a dot. Here's how you can create a view that references objects from different schemas:

```sql
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CREATE VIEW MyCrossSchemaView AS
SELECT
schema1.table1.column AS column1,
schema2.table2.column AS column2
FROM
schema1.table1
JOIN
schema2.table2
ON
schema1.table1.id = schema2.table2.id;

```

In this example, **`schema1.table1`** and **`schema2.table2`** are fully qualified object names that specify tables located in different schemas. The view **`MyCrossSchemaView`** pulls data from both tables and performs a join across schemas.

Make sure you have the necessary privileges to access objects in the referenced schemas. Users need appropriate privileges on the objects they are referencing, regardless of the schema in which those objects are located.

By leveraging cross-schema references, you can build views that provide a unified and consolidated view of data spread across different schemas within the same Snowflake database.

In Snowflake, you can perform certain DDL (Data Definition Language) operations on views, but there are limitations based on the nature of views and their purpose. DDL operations involve defining or modifying the structure of database objects. Here's an overview of what DDL operations are possible and why:

**Possible DDL Operations on Snowflake Views:**

1. **CREATE VIEW:** You can use DDL to create a view by defining its query and structure. This is a standard DDL operation.
2. **ALTER VIEW:** You can use DDL to alter a view by changing its query definition or renaming it.
3. **DROP VIEW:** You can use DDL to drop (delete) a view from the database.

**Not Possible DDL Operations on Snowflake Views:**

1. **ALTER COLUMN:** You cannot use DDL to directly alter a column's data type or attributes in a view. This is because views do not store data themselves; they provide a dynamic presentation of data from underlying tables. Altering a column would involve modifying the structure of the underlying tables, not the view.
2. **ADD COLUMN / DROP COLUMN:** Similarly, you cannot directly add or drop columns in a view using DDL. Again, this would involve modifying the structure of the underlying tables.
3. **RENAME COLUMN:** You cannot use DDL to rename columns within a view.

Snowflake's design philosophy revolves around maintaining the separation between the logical view of data (provided by views) and the physical storage of data (in underlying tables). Views are designed to encapsulate data transformations and abstractions, not to alter the underlying structure.

If you need to modify the structure of the underlying tables, you should perform those operations on the tables themselves. Any changes made to the underlying tables will be automatically reflected in the view's presentation of data.

In summary, while you can perform some DDL operations on Snowflake views (such as creating, altering, and dropping), the scope is limited to managing the view object itself rather than directly modifying the structure of the data presented by the view.

"View chaining" in Snowflake refers to the practice of creating one or more views that are built upon or reference other views. This creates a chain or hierarchy of views where the output of one view is used as the input for another, allowing for progressively more complex data transformations and abstractions.

View chaining is a powerful technique that offers several benefits:

1. **Modularity and Reusability:** By breaking down complex logic into smaller, more manageable views, you can create modular components that can be reused across different queries and analyses.
2. **Data Abstraction:** Each layer of chained views can abstract away certain complexities, presenting users with a simplified and meaningful representation of the data.
3. **Simplified Queries:** Views can simplify the querying process by encapsulating complex joins, aggregations, and calculations. Chaining views can make queries more concise and easier to read.
4. **Security and Access Control:** Views can be used to enforce data access controls and expose only the required attributes to different users or roles. Chaining views allows you to control data exposure at different levels of abstraction.
5. **Performance Optimization:** Views can help optimize performance by precomputing and storing intermediate results. Chaining views can further enhance performance by building on these optimized results.
6. **Data Consistency:** Chaining views ensures that multiple users or applications access the same derived data, promoting consistency across analyses.

Here's a simplified example of view chaining:

Suppose you have three views: **`RawSalesData`**, **`MonthlySales`**, and **`QuarterlySummary`**.

1. **`RawSalesData`**: Contains raw sales data from a table.
2. **`MonthlySales`**: Aggregates data from **`RawSalesData`** to calculate monthly sales totals.
3. **`QuarterlySummary`**: Aggregates data from **`MonthlySales`** to provide a summary of sales for each quarter.

By chaining these views, you can build complex summaries starting from raw data while maintaining a clear and organized structure. A query on **`QuarterlySummary`** would implicitly include the calculations and transformations defined in the previous views.

Here's an example of chaining views:

```sql
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CREATE VIEW MonthlySales AS
SELECT
YEAR(SaleDate) AS SaleYear,
MONTH(SaleDate) AS SaleMonth,
SUM(Revenue) AS TotalRevenue
FROM RawSalesData
GROUP BY SaleYear, SaleMonth;

CREATE VIEW QuarterlySummary AS
SELECT
SaleYear,
(SaleMonth - 1) / 3 + 1 AS Quarter,
SUM(TotalRevenue) AS TotalQuarterlyRevenue
FROM MonthlySales
GROUP BY SaleYear, Quarter;

```

In this example, **`MonthlySales`** chains on **`RawSalesData`**, and **`QuarterlySummary`** chains on **`MonthlySales`**. The result is a chain of views where each view builds upon the output of the previous one.

View chaining is a flexible approach to data transformation and organization, allowing you to create a layered architecture that enhances data management, analysis, and reporting.

To grant access to a view to a different user in Snowflake, you can use the **`GRANT`** statement. This statement allows you to specify the privileges you want to grant to a user on the view. Here's the SQL syntax to grant access to a view:

```sql
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GRANT privilege ON VIEW view_name TO USER user_name;

```

- **`privilege`**: The privilege you want to grant. For granting access to a view, you would typically use **`SELECT`**.
- **`view_name`**: The name of the view you want to grant access to.
- **`user_name`**: The name of the user to whom you're granting access.

Here's an example of granting **`SELECT`** access on a view named **`SalesSummary`** to a user named **`analyst_user`**:

```sql
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GRANT SELECT ON VIEW SalesSummary TO USER analyst_user;

```

In this example, the **`SELECT`** privilege on the **`SalesSummary`** view is granted to the user **`analyst_user`**. This allows the user to query the view.

You can also grant other privileges like **`INSERT`**, **`UPDATE`**, **`DELETE`**, or a combination of privileges depending on your access requirements.

Remember to replace **`SalesSummary`** and **`analyst_user`** with the actual names of your view and user. Granting access to views provides controlled access to data without directly exposing the underlying tables, enhancing data security and access control.

In Snowflake, both views and materialized views are database objects that allow you to organize and present data in a structured manner, but they have different purposes and behavior:

**Views:**

1. **Definition:** A view is a virtual table defined by a SQL query. It doesn't store data itself; it provides a dynamic way to access data from one or more underlying tables.
2. **Data:** Views don't store data. They retrieve data in real-time based on the query definition when queried.
3. **Usage:** Views are suitable for simplifying queries, abstracting complex data structures, enforcing data security, and providing a logical layer over the underlying tables.
4. **Maintenance:** Views are easy to maintain as they don't store data. Changes in the underlying tables are immediately reflected in the view's data.
5. **Performance:** Views can improve query performance by encapsulating complex joins, calculations, and filtering.

**Materialized Views:**

1. **Definition:** A materialized view is a precomputed, physical copy of a result set from a SQL query. It stores data, unlike regular views.
2. **Data:** Materialized views store data that is periodically refreshed based on the query's definition. The data is computed and saved at the time of refresh.
3. **Usage:** Materialized views are suitable for improving query performance by precomputing and storing aggregates, summaries, or other complex calculations.
4. **Maintenance:** Materialized views need periodic refreshes to stay up to date. Depending on the refresh frequency, there can be a lag between changes in the underlying data and updates in the materialized view.
5. **Performance:** Materialized views can significantly improve query performance for certain types of analytical queries by reducing the need for complex calculations during runtime.

In summary, the key differences lie in how data is stored and refreshed. Views provide a dynamic view of data in real-time, whereas materialized views offer improved performance by storing precomputed data that requires periodic refreshes. The choice between using a view or a materialized view depends on your specific use case and performance optimization requirements.

Yes, you can update data through a Snowflake view under certain conditions. Snowflake supports updatable views, which allow you to perform data modifications (INSERT, UPDATE, DELETE) on the view itself, and these changes are then reflected in the underlying tables. However, there are some limitations and requirements to consider:

1. **Simple Updatability:** Views are updatable if they meet certain criteria. The view must be based on a single table (not a join or subquery) and must not include certain constructs like DISTINCT, GROUP BY, HAVING, etc.
2. **Primary Key or Unique Key:** The underlying table must have a primary key or a unique key defined on it. This is necessary for Snowflake to determine which rows to update or delete when changes are made through the view.
3. **Column Constraints:** The view's columns must be directly mapped to the underlying table's columns. Computed columns, expressions, or transformations can't be updated directly through the view.
4. **Limited to One Table:** As of my knowledge cutoff in September 2021, updatable views in Snowflake are limited to updating data in a single underlying table. You can't use views to update data in multiple tables using a single view.

Here's an example of creating an updatable view and performing an update through the view:

```sql
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-- Create an updatable view
CREATE OR REPLACE VIEW UpdatableEmployee AS
SELECT EmployeeID, FirstName, LastName, Salary
FROM Employees
WHERE Department = 'Sales';

-- Update data through the view
UPDATE UpdatableEmployee
SET Salary = Salary * 1.1
WHERE EmployeeID = 123;

```

In this example, the **`UpdatableEmployee`** view is based on the **`Employees`** table and includes only employees from the 'Sales' department. The **`UPDATE`** statement modifies the salary of an employee through the view. This change will be reflected in the underlying **`Employees`** table as well.

Keep in mind that while updatable views can be convenient, they have limitations and may not be suitable for all scenarios. It's important to carefully consider your use case and ensure that your view meets the requirements for updatable views in Snowflake. Always refer to the latest Snowflake documentation for the most accurate and up-to-date information regarding updatable views.

To create a view in Snowflake, you use the **`CREATE VIEW`** statement and define the view's query. Here's the syntax for creating a view:

```sql
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CREATE VIEW view_name AS
SELECT column1, column2, ...
FROM source_table
WHERE condition;

```

- **`view_name`**: The name you want to give to the view.
- **`column1, column2, ...`**: The columns you want the view to expose.
- **`source_table`**: The table or tables from which you're selecting data.
- **`condition`**: An optional condition to filter the data in the view.

Here's an example of creating a simple view named **`EmployeeInfo`** that selects specific columns from an **`Employees`** table:

```sql
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CREATE VIEW EmployeeInfo AS
SELECT EmployeeID, FirstName, LastName, Department
FROM Employees;

```

In this example, the **`EmployeeInfo`** view includes columns **`EmployeeID`**, **`FirstName`**, **`LastName`**, and **`Department`** from the **`Employees`** table. You can query this view as if it were a regular table:

```sql
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SELECT * FROM EmployeeInfo;

```

You can also create more complex views that involve joins, aggregations, calculations, and other transformations. Views in Snowflake provide a convenient way to encapsulate these complex operations and simplify querying.

Remember that views don't store data themselves; they're based on the query's definition. If the underlying tables change, the view's data changes accordingly. Views can be a powerful tool for data organization, security, and simplification of complex queries.

Developers interested in crafting Snowflake Native Apps can access resources, quickstart guides, code samples, and documentation on the Snowflake website at www.snowflake.com/native-apps/. Furthermore, a Snowflake Native Apps Community provides a platform for developers to connect with fellow builders and share insights.

The Snowflake Native App Framework incorporates versioning, allowing app providers to introduce incremental feature updates or address bugs. Release directives permit targeted releases to specific customers, and new versions are automatically distributed to customers without causing additional downtime or disruptions.

Snowflake Native Apps bring data and apps into closer proximity, enabling customers to explore, acquire, and install apps within their own Snowflake accounts. This eliminates the need to transfer or duplicate data, streamlines security and procurement processes, and ensures that customers only interact with the app's interface without accessing proprietary datasets or logic from providers.